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emberfrostlovesloki · 2 years ago

Text

Demons [Hotch x Reader]

Photo credits: Left and Right (Google) Center (@hotchs-big-hands [my beloved])

Prompt: The team is forced into very close quarters during a case on an offshore oil rig in Alaska. It’s bitterly cold and there’s nowhere to go, and three men have been beaten and stabbed to death. The team must solve the mystery before it’s too late. A mix-up in rooms also has Aaron and the reader closer than ever. It allows him to learn something new about her.

Category: Angst/comfort

Word Count: 15.6K

Content Warnings: Sleep paralysis, canon typical violence, death (of a victim and unsub), beating (with a blunt object), choking (briefly described), mention of death by stabbing, the threat of death by knife/gun, mention of drowning (unsub), mention of abuse (in the past [Hotch]), slight mention of blood, language, hospitals, slight body image issues (Hotch). Please let me know if I missed any.

A/N: Ahhhh, hi loves. Did anyone ask for something this long? No. Did I expect to write this much? No. But the scenes kept coming, and I kept writing them down. I just love the writing process. It’s so cool. But enough of that. This story’s mostly based on a northern gothic vibe and the age-old, ‘only one bed’ trope. I am very happy with how this turned out and I hope you all like it too. You could read this as a stand-alone or as a prequel to my story, Unwanted Attention (linked). A huge thank you to my top hype woman @sadgirlzluvdilfs (ILY) If you like this story as well, likes, comments, and reblogs are appreciated! I hope you all have a great Friday night! - Love Levi.

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y/n = your name

_y/e/c_ = your eye color

Hotch got a call from Strauss in his office. He had hoped that Monday would be a quiet work day for himself and the team. He was currently drowning in paperwork, and as he glanced down at the bullpen. He could see his team trying to do their best to also catch up on the more clerical side of their jobs as FBI agents. Strauss had told him to meet her in her office immediately. He replied, “Yes, Ma’am. I’m on my way.” He hung up the phone, grabbed his shoulder bag, and moved toward the elevator. It was a short ride up to the twentieth floor of their building, and Aaron wondered what he should prepare for when he arrived at Struas’s office.

He walked down the long hallway and knocked twice on his boss’s door before opening it. Aaron was not expecting to see The Federal Energy Regulatory Commission, Frank Ridgewell, seated across from Strauss. Both the Commissioner and Strauss stood when he entered her office. Erin gestured to Ridgewell and said, “Agent Hotchner, I’m sure you know Commissioner Ridgewell.” Aaron nodded. He had never met the man in person before, but he was aware of who he was, and how important he was to the United States. Aaron extended a hand and Mr. Ridgewell took it, giving it a firm shake. Once the quick introduction was done, Strauss said, “Let’s all have a seat.” From Erin’s tone, whatever was happening here was important. Strauss indicated to the other man in the room, and Frank turned toward Aaron asking, “Are you aware of the new offshore drilling operation in Alaska?” Hotch furrowed his brow and replied, “Only tangentially. I understand that the rig was built quickly and there was a land dispute. I’m also aware that there were environmental protests over beginning new drilling so close to a naturally preserved site.” Ridgewell nodded and said, “You’re correct. As of three months ago, the oil rig has been fully operational. The rig employs sixty-seven people in total. Fourty-four of those individuals are employed part-time or have shift work on the operation. The other twenty-three are full-time employees that work one month on-site and three weeks off.” Aaron had his legal pad out and was taking a few notes as the Commissioner spoke. He was waiting for the important information with a bit of impatience. This had to be important if it wasn’t coming from JJ. If it was coming from the top, he needed to be meticulous in his work and the various details being thrown at him.

After another minute of the smaller details about the rig and its crew. Ridgewell’s tone changed. The man said, “Well all that preliminary information is building up to say that within the first three months of operation, three men have been killed. Only one of the twenty-five men working full time on shift could be responsible for the deaths. The three victims were found beaten and stabbed to death at various locations on the job site. The first victim was found by one of the security guards and the next two were found by workmen.” Aaron nodded, finally understanding the gravity of the situation, and asked, “And you believe that the BAU can assist you in finding the unsub on this oil rig?” Frank looked confused as Hotch said, “Unsub,” and Aaron clarified, “The Behavioral Analysis Team calls potential murderers Unknown Subjects, or unsubs for short.” At hearing this, Ridgewell nodded and replied, “Yes, yes I do, but there’s more to it than just the killings.” Aaron raised an eyebrow and Strauss chimed in for the first time during the meeting, saying, “Take a look at these Agent Hotchner.”

Strauss pushed a file labeled ‘Classified,” in front of the Unit Chief. Hotch opened the folders and inside found a dozen or so copies of transcripts and pictures of messages that had been unencrypted. The jist of all of them was that the three deaths had been an intentional attack on the U.S. oil and gas industry. After Aaron had carefully examined these pictures, he looked over to Strauss and then Ridgewell. He asked, “So you believe there is credibility to these claims?” Ridgewell gave a half-shrug before answering, “We can’t be sure yet, but if this information got and with the new site already having a negative reputation, there would be dire consequences. The current administration is desperate to keep prices on gas and oil low and even a momentary shutdown of operations would affect the bottom line. And heaven forbid those messages leaked to the public. Half of the States would be in a panic, and there'll be a run on fuel.”

Hotch nodded. This case was more complicated than he had first expected. Strauss looked at her Senior Agent and stated, “We need you and the team in Alaska as soon as possible. This is a matter of national security. Agent Hotchner. You and the BAU will need to be extremely careful.” Aaron replied, “Yes, Ma’am.” He then turned to Ridgewell and said, “I’ll need to brief my team. We’ll be headed to the site by the afternoon.” Frank looked relieved and replied, “Thank you, Agent Hotchner. I’ll email you the file with the current crew of the rig and their supervisor. I should warn you that it’s going to be close quarters up there.” Aaron nodded. He didn’t ask for elaboration about the space, he was going to be there by the end of the day anyway, and he didn’t have time to think about it right now. Hotch stood and shook hands with Ridgewell and Strauss before straightening his jacket and moving outside to the hallway again.

Back in the bullpen, he moved to his office, He would need to check his email and do a bit of research before calling the team to the briefing room. He moved toward his office and Rossi passed him. Dave looked over his neutral expression. Though Aaron rarely showed his emotions openly, Rossi knew him well enough to know that something was amiss. David flashed him his, ‘New case?” Look, to which he nodded affirmatively. Aaron could see Rossi’s shoulders fall slightly. Hotch understood that his friend had also wanted a break. The caseload had been extra heavy over the last month and a half, and the team was aching for a break.

As the two men passed each other on the stairs, the sound of laughter caught Aaron’s attention. He knew the laugh well. Better than he should. Aaron turned and saw y/n sitting at her desk. She had a file slightly covering her face and her _y/e/c_ eyes were bright and wide. Emily and Derek were standing beside her having made some joke that he hadn’t heard. Looking at her like this, as a casual observer made a small flame in his chest flicker slightly, like a lighter being turned on by an unsteady hand. Aaron had begun to recognize that the small attraction that he had for y/n had grown. Now every time he looked at her, he felt the need to stifle a sigh.

For now, he was safe. y/n hadn’t shown any particular interest in him, that he could tell. Or maybe he was just pretending to not notice when _y/n_ looked at him for longer than necessary, or how she checked in on him often, just to make sure he was really doing alright. Hotch turned away as another peal of laughter emerged from the group downstairs. In his office, he turned on his lamps and opened his email inbox for the new information Strauss had CC’d him. It was a good 110 pages of personnel files and maps of the site. More important for the team was when the supply boat schedule which went to the rig in the morning and early evening. It took Hotch a full hour to skim all of the new information. He sent Garcia an SOS to get as much dirt off the Northern Oil and Gas Supply LLC as she could. Particularly the new oil site called Farpoint 52, -153. The name was unassuming, and the first thing Penelope told him was that the numbers were latitude and longitude points in Alaska, but not those of the actual site.

When Aaron was ready, he had seven file folders with all the most important information accumulated including pictures of the victims that the local PD in Anchorage had just sent over. The attacks were brutal. The injuries on the three bodies seemed to be caused by blunt force trauma, and as Ridgewell had said, there were multiple stab wounds on the victims as well. Hotch took a long breath as he got up and moved outside his office. He knocked on JJ and Rossi’s doors and gave them their files. JJ said, “I’ll get Garcia to come and set up the screen in the briefing room.” Aaron thanked her, and she and Rossi moved out of his way.

Hotch placed his hands on the railing of the stairs and called out for his agents saying, “BAU team, I need you in the conference room.” As all four members of the team looked up to him, the mood of the room changed, dimming from how lively it had just been. Hotch turned toward the meeting room before he could see their faces fall once more. Sometimes he thought that he couldn’t keep doing this to them. To himself. The isolation he felt when he was home alone left him a breathless aching mess. It was rare when he allowed those feelings to overwhelm him, but sometimes he couldn’t help it. When this happened, he often found himself in a steaming hot shower. So hot that it hurt. When he couldn’t control his emotions, he felt like a kid again after his father had gone on either a verbal or physical diatribe about his perceived shortcomings. The reminders of the abuse he endured for years would flare up and make him feel a different kind of shame and hurt than letting his team down. By the end of the shower, he had normally excised these emotions and feelings of weakness and would fall into a fitful sleep afterward.

y/n watched Aaron turn quickly. She let out a long sigh at the announcement about a new case. Everyone on the team was exhausted, and it seemed that Hotch was the most exhausted of them all. She had watched him closely over the last month. Something about his demeanor had shifted. y/n wondered if it was the two-year anniversary of Hailey leaving him that had initiated the change, or if it was something else personal or professional. She wanted to ask him what was wrong. How she could soothe him from the stress she could sense coming off of him. But she assumed that might be stepping over some personal work line, and she was still relatively new to the team. She didn’t want to risk any consequences for being overly personal. For now, all she could do was watch and wait for a time that seemed appropriate. She was fully aware that that time may never come and would have to be okay with just being near someone as commanding and steady as Aaron.

In the briefing room, everyone but Hotch and JJ sat. Aaron moved to the head of the table and said, “This morning I got a call from Director Strauss. When I got to her office, the Federal Energy Regulatory Commissioner was waiting for me. He has a case for us in Alaska that is a top priority. And…” Hotch paused as seven pairs of eyes waited for more details. Realizing that it might be more efficient to have his agents just read the brief in their files, he said, “Actually, why don’t you just turn to page one in your files and read over the case notes so far? I’ll give you a few moments.”

The team opened the files in front of them and read the 1,000-ish word briefing on the first page. They were all aware that this case was different than the rest and that the brief hadn’t been written by JJ. Spencer and y/n could tell it was Hotch’s writing almost immediately. The tense use and wording were very direct and blunt, just as Aaron was. Not that JJ added fluff, just that she took a few more words to make a point than their Unit Chief. Once those seated at the table had read over the first page and taken a look at the victim's pictures, Aaron moved forward and said, “Well as you can see, this latest case doesn’t have a lot of victims, but the timeline is progressing quickly and given that the crew is so small, these deaths have caused issues in the operation of the rig. On top of this, it seems that foreign agents are claiming they are playing a part in these deaths. If this is true or not; we can’t be sure until we find the Unsub.” Rossi tossed in the comment, “If it is foreign agents, they are most likely to come from Russia or the Middle East where much of the oil in the U.S. comes from. We should look at the crew and see if there are any potential ties to those countries.” Hotch inclined his head at the suggestion. It was a good one. With the primary details being said and a long flight ahead of them, Aaron concluded the information session by saying, “I recommend bringing the warmest clothes you have in your go bags, and something waterproof if you have it; I’m sure you can guess that it will be cold and wet where we’re headed.”

Aaron looked at his team as they started standing, and he added one last thing that stilled the team and drew their attention to him again. He simply said, “I understand that this team has gone through a lot in the last few cases. I know you’re tired. After this case, I’m going to do my best to find some time for us to be off and recuperate for a bit. Please know that your efforts and work don’t go unnoticed by me. That’s all.” With his mini pep-talk finished, the team moved into action again. Aaron had meant what he said of course, but had also said it for himself too.

As everyone filed out of the room, y/n approached Aaron and just barely touched his forearm to get his attention. Hotch looked down at her and asked, “What is it, y/n?” Sometimes when y/n looked at him specifically, intentionally, he thought he saw a flicker of something more in her eyes than just attention and respect. He thought he saw it now, but he cleared his head. Now wasn’t the time for those thoughts. y/n didn’t seem to notice how deeply he was looking at her when she said, “When you spoke to Strauss this morning, did she say anything more about the case? Is there anything else we need to know?” She hoped she wasn’t asking for information he couldn’t give. Hotch continued looking down at her for a moment before replying, “She just said that we needed to be safe. There are a lot of unknowns here. More than usual for a case.”

y/n acknowledged his statement and said softly, “Got it. See you in the parking lot.” With that, she quickly left to gather her go-bag and race to get some coffee if she could before the jet left for Anchorage. When y/n had gone, Aaron took another moment to minorly compose himself. Then he moved to catch up with Garcia. He was going to ask her to join the team on this case due to the technical aspects that might be involved. He had a sinking suspicion that getting her on wifi all the way out where they would be might be harder than on the mainland. If foreign assets were involved or there was tampering with the equipment of the rig’s systems, Penelope was the most capable of any of them to lend a hand.

Thirty minutes later, the team piled into the jet with their go bags and files. Garcia was a balm to what seemed to be an already dreary case. As everyone sat, the ideas started flowing about motive and the type of unsub that they were dealing with. Spencer and Morgan were thinking about the physical elements of the unsub. Things like the impact of the wounds, the type of weapon being used to inflict them, and the force that would be needed to cause them. Their top ideas for weapons were a baseball bat or some other blunt object that had lots of fulcrum power. Meanwhile, JJ and Emily were looking through the personnel of the twenty-five full-time employees. Garcia was on every possible legal and illegal site that tracked energy and messages that could possibly correlate with countries like Russia, Iran, or Iraq. Rossi and Aaron were looking deeper into the oil company as a whole.

To them, it seemed a little sketchy and the fact that Mr. Ridgewell had asked for the team personally belied that there might be some shady business not only from outside but inside the company as well. Rossi was talking about a possible correlation with BP and their recent legal troubles. As all this was happening, y/n sat and listened to the cacophony of sounds bouncing around the plane. She had her notebook out and was taking her usual annotations on the case and jotting down when someone on the team said something she thought was important. She was feeling a bit overwhelmed with all the chatter happening around her, so she took a moment to grab a coffee from the back of the jet. The team had moved out so quickly that she didn’t get a chance to grab a cup in the office break room. She moved past Hotch and noticed he wasn’t holding a cup either. y/n stopped briefly in front of her boss, and he looked up at her. She made a hand motion to indicate ‘drink?’ Aaron gave her a small, grateful look and nodded his head yes.

At the back of the jet across from the small sink and mirror, was the coffee machine. She put in the water and a capsule for the Keurig. y/n placed a cup under the dispenser and pushed run, on the machine. y/n somehow hated the Keurig coffee more than the office coffee. It managed to always taste burned and flavorless no matter which flavor of pod she picked. But the caffeine was a necessity at the start of a case for her. It was half ritual half desire, and she didn’t fight it. When her cup was finished, she started the process again for Aaron, who no matter what coffee he was drinking, seemed unfazed by the quality of the brew. As Hotch’s cup started filling, y/n doctored her own cup with milk and white sugar.

Once both drinks were ready, she placed lids on the cups and moved back to the cabin of the plane. As she passed Hotch, she seamlessly handed his cup to him, as she settled back to her own spot further down the row. Garcia watched as this happened. It was like moving a baton between two runners in a relay. ‘They hardly looked at each other while it happened!’ The technical analyst thought. Penelope hadn’t been on a case since y/n had joined, and this behavior was new and exciting for someone like Hotch.

Garcia had taken special care with y/n. The newest BAU agent was young, and she knew more about y/n’s background than most of the team. Because of this, Garcia had done her best to uplift and support y/n. But it seems that y/n was supporting the team in small ways too. Penelope knew that _y/n_ was attentive and sharp in her mind and actions if she needed to be. But this was generally hidden beneath her gentle warm exterior. But seeing y/n meet even the smallest needs without even being asked to was such her thing; at least, that was what Penelope thought. Now that she was here seeing this, Garcia was going to have to pay more attention to y/n and Hotch. Because she wanted to know if this was just a them thing, or was y/n acting like this with the whole team?

y/n sat and took a sip of the coffee that was a little too hot. The liquid burned her tongue and she made a small face of pain. Thankfully no one was looking at her at the time. y/n set the cup in the cupholder next to her seat and looked at the picture of the rig itself again. This setting was so bizarre for a crime. Even the photos of the outside of the rig made her feel unnerved. y/n tried to think of any prior cases like this. There had to be some. y/n was fiercely thinking about old cases. Old old cases even. This case was going to require thinking outside the box. Finally, with eyes closed and brows pinched, some ideas started coming to her. With some inspiration, she began writing quickly on a new page of her notepad As this was happening, Aaron looked around the cabin. Everyone was still intensely focused, and he walked around each group to see what they had come up with so far.

Spencer and Morgan had surmised that the unsub was probably smaller than they might assume. Given that they used blunt objects to kill the victim. If the unsub had a lot of strength, they should be able to kill their victims without the need for an object. Between JJ and Emily, they had pinpointed a few possible workers who might fit certain profile types and those specifically seemed to be linked to odd organizations or firms that could be linked to terrorist organizations. As Aaron moved to ask Garcia how it was going, she shooed him away with a tut-tut indicating that she was too busy for a check-in at the moment.

The last person he needed to see was y/n. As he approached her, she seemed deep in thought, and he sat across from her and sat for a minute or two in silence as he let her wrap up whatever she was writing. When y/n’s pen stilled, she looked up at him and he asked, “You seem to be having some ideas overhear. Mind sharing them with me?” y/n nodded, looking down to her notes. She resisted the urge to say, ‘I don’t feel good about this case. I can’t pinpoint it, but something feels off here.’ Instead, she said, “I was thinking about the setting; the rig. Looking at the ariel photographs, the maps of the interior, and the security footage from the main hallways made me think about something. It’s so isolated. If you work there then it’s a tight space, and you work a dangerous job, and you see the same twenty or so people day after day for three to four weeks.”

Hotch nodded along, getting a feeling for where she was going. When they made eye contact again, he said, “And?” He was encouraging her to finish her thought. y/n gave a soft sigh as if she was doubting herself. Whether there was doubt or not, y/n continued, saying, “Well those working conditions can’t be good for one’s psyche. I was considering some old cases. I know that Cabin Fever isn’t a diagnosable psychological condition, but there’s a history of those symptoms manifesting in groups of isolated people. I’m thinking as far back as the Donner party in 1846. There was the Highcliff’s in 1980, and more recently the Smith and Wess party in 1992. I know these are ancient cases for the team but it seems to fit to me. I know this case could be way more complex given the terrorism element, but just looking at the brief you wrote, I think this might be a case of insanity due to the location. I could be wrong. I could be totally off here, but it’s what I’ve got so far.”

Aaron thought about what y/n had said and replied, “I’m not saying that that train of thought is not out there, but given the novelty of this case, I think we need someone who is thinking with a separate mind frame. Once we’re on site, keep what you have here in the back of your mind. If you see anything that relates to this theory let me know immediately.” y/n nodded at him in agreement as he stood and made his way back to Rossi. Sometimes when Hotch or anyone asked her her thoughts early on, she feared that she sounded unhinged, or worse, stupid. y/n was still finding solid footing with the team, but Hotch never dismissed her ideas unless they were fully implausible, and she appreciated that about him.

The flight moved quickly after this. Although there were five hours left, the team regrouped and shared what they knew before touchdown in Anchorage. When the jet landed, the sun was already setting in the West although it was only 5:30 p.m. It only took a few minutes before they arrived in the SUVs at the dock with the resupply boat that would take them to the rig thirty minutes offshore. The team turned in all three sets of keys to the cars to the police officer waiting for them at the dock. Aaron promised to call the local LEO when the team was ready for their return to the small airstrip.

The team pulled their go bags from the back of the cars. Derek was kind enough to carry Garcia’s pink and sparkly duffle on top of his small carry-on suitcase. The team had bundled up in their jackets and they were buffeted by the harsh northern winds beating them from all sides. As they boarded the gangway, Hotch momentarily steadied y/n who he was walking next to. Though she seemed okay, it seemed to him that she could use a steadying hand for a moment as she battled the wind. When she felt Aaron approach her and then place a steadying hand just barely against her back she looked at him. He wore that expression that just said, ‘I’m here.’ y/n gave him a nod, indicating that she appreciated the gesture. Aaron kept his hand where it was until they got on deck.

Once they were on a more sturdy surface, Hotch removed his hand but moved to take the handle of y/n’s small wheeled case in his open left hand. Their hands brushed briefly as they exchanged the weight of her luggage. Neither Aaron nor y/n said anything at the exchange, but she gave him a soft smile as he moved toward the stairway that led to the passenger area of the ship. This had become a little pattern of theirs over the past few months. There was a kind of shared understanding of care between them. Aaron told himself that this was him taking care of his newest agent, and _y/n_ told herself that this was her trying to prove that she noticed the small needs of the team; both of them were lying to themselves.

Once the team was downstairs, y/n took charge of her case again, as Aaron and JJ moved to the control room to introduce themselves to the captain and get some relevant information. While the team waited to start moving, they all settled into the uncomfortable benches either in the center of the boat or those near the sides of the room that had a few windows looking out onto the choppy Alaskan Sea. After a few minutes, the boat motors started roaring to life and the resupply vessel moved toward the open water. Garcia moved to sit next to y/n who had slumped down on a bench next to one of the windows. The waves were a dark green and blue, and the clouds had turned a charcoal grey as the sun started to dip below the horizon. Penelope looked over to y/n and asked, “How are you holding up, friend?”

y/n looked over to Garcia and said, “I don’t like this Penelope. This feels off to me. This case.” Garcia nodded along and said, “Trust your gut y/n. You know yourself better than anyone else. If you ever need to talk, I’m here for you.” y/n nodded and both she and the tech whizz turned to look as Hotch and JJ returned from the bridge.

Aaron stepped into the center of the room. The boat listed up and down slightly, but he seemed perfectly stable even as the boat took on a large wave. In his smooth voice, Aaron said, “According to the skipper we should be at the rig in around twenty-five minutes. Apparently, the seas are pretty rough tonight. Once we get there, the boat will take a few minutes to dock. A worker on the rig is going to get our luggage for us, so leave it here by the door when we disembark. Once we’re on the rig the first thing we'll do is meet the foreman. As you saw in the file, his man is Mr. Obermann. Once I’ve introduced us all, we’ll get a tour of the rig. Find rooms and then debrief if that sounds alright?” Everyone agreed and said so in some way or another. y/n always found it interesting that he said things like, ‘If that’s alright with you.’ To the team. It’s not exactly like they had a choice on what happened at the start of a case. y/n hypothesized that he did this to give them an allusion of control. Also, if someone did really have a suggestion that the team do something differently -- like asking to go to a crime scene or the hospital or something like that -- then he would consider it. However, Aaron was usually good at predicting the needs of the team and the case. He was their leader after all.

The resupply boat arrived at the rig and the size of the massive object that was being buffeted by the cold waves was more massive than any of them had imagined. The rig wasn’t the only thing being pushed by the harsh wind. As the team got outside and made it to the short ladder they would need to climb to get to the main platform. Derek helped y/n and Garcia get to the ladder while Aaron helped JJ, and Rossi provided Emily a steadying hand. The whole team pulled their jackets tighter around themselves as they made it to the main door. A worker pulled the heavy metal door open for them. The door led directly to the crew’s rec room. Mr. Obermann was waiting for them and stood as the team entered the room.

Aaron moved to the front of the pack and introduced himself and the team quickly. Mr. Obermann looked stressed and worn out which was understandable given the circumstances. The man said, “Well I appreciate you all coming so far. If this doesn’t get fixed it will be hell for the company, but more importantly to me twenty-five good hard-working men. Because you’ve all come I’ve sent all the temp workers home until you find our guy. What did you call him again?” Aaron replied, “The unknown subject, or unsub for short.” Obermann nodded and said, “Yeah. That. The men that are still here are freaked. They all think they’re going to be the next victim. It’s not good for the job as they need to pay full attention to what they are doing. Risk of injury on offshore rigs are thirty-three percent more likely than those on land.” Obermann stopped to take a breath before continuing, “Now I’ll give you a tour of the place. I need you all to put on hard hats.

The protective headwear was passed out, and the team put the hats on. JJ, Penelope, and y/n struggled not to laugh at the look of all the men on the team wearing the hats. Particularly Rossi, Morgan, and Aaron. Hotch looked like a midwestern politician trying to get votes from the rustbelt to y/n, and she actually had to cough to hide her laugh. She was fully aware that she must also look like a fool, but she just couldn’t help but chuckle a bit. Once they were equipped, the team and Mr. Obermann moved through an internal door that led to a long hallway. The foreman moved through each of the rooms on that floor, including his small office, the mess hall, the laundry room, and some of the sleeping quarters. They moved outside, and the team looked at the helipad, and the derrick that brought the oil up to the surface. The team moved back inside and moved down the steps to the first level of the rig.

The lower floor was very dark and close to the water level. The sound of the waves could be heard through the thick steel and concrete which spoke to the power of the water surrounding them. Obermann guided through the more mechanical side of the rigs. The communal showers for the crew were also located on the second level. As they walked through the mechanical room, Obermann said, “This room is generally off limits, but as you know, the first victim was found here. I ask that if you need to be in here, let me know and I’ll send someone to open it for you.” The tour took a long time as the space was cramped and a lot of the rooms required them to be careful. Obermann led them back to the rec room where their luggage was waiting for them. Oberman said, “I’ll give you all a few minutes to pick rooms for yourself. The rooms that are free are downstairs. With all of you here, you’ll have to double up. The keys to the rooms are on the table and are labeled with the number that matches the door. Now I have some paperwork to attend to, but I’ll be in my office for any questions you have.”

As Obermann moved to his office, the team looked at each other. Having to share rooms was never something they enjoyed. Though the team was close, it was an entirely different thing to have to share a room. The team's cases often brought a lot of stress and little sleep, and having the privacy of their own space let them decompress in their own form or fashion was needed. On the rare occasions that the team did share rooms, it was fine, but everyone was far more comfortable alone. But, the work needed to be done, and they needed to start quickly, so no one made a fuss. With that out of the way, the team paired up. Derek, Spencer, and Rossi shared one of the rooms that had three beds, and JJ, Emily, and Garcia took the other room with three beds. Emily offered to share her bed with y/n but y/n said that she was alright being with Hotch in the room with two beds. If it meant having her own bed she would be fine.

Aaron overheard y/n and Prentiss’s conversation, and he felt a tug in his chest. He wasn’t sure if the feeling was because y/n seemed so okay with sharing a room with him, or the fact that he was even thinking about it. Hotch had noticed his feelings change toward y/n in the last few months. He wasn’t sure what was pulling him to her, but in some tiny way, things seemed to have shifted in the air for them. And Aaron knew that it wasn’t just him that felt the change. y/n had started to adapt around him. Doing things for him she didn’t need to but that he wanted. He had started reciprocating the gestures and it just kind of clicked in place. Hotch hadn’t given this much thought yet. There hadn’t been time, and he wanted to wait before he did anything more. The fact that he was thinking this now felt like he was breaking some kind of supervisory rule. Even if y/n seemed completely fine with sharing a room with him, he wanted to check in personally. As the rest of the groups moved down the stairs with their suitcases, Hotch stepped toward her.

When Aaron was next to her, he looked down into her eyes and said, “y/n, you don’t have to share a room with me. I can make another arraignment or sleep on one of the couches in here if you prefer.” y/n appreciated the gesture, and she looked over what appeared to be the most old, decrepit, and uncomfortable couches she had seen in her life. Not only would Hotch’s sleep be compromised, but he honestly might be unsafe here given that the rec room was open 24/7. With the killings happening, she would never risk him like that. Even if she was uncomfortable with the idea of sleeping in the same room as her boss, she still wouldn’t make him sleep in a space like this. Although y/n didn’t find the idea of sleeping in the same room as Hotch uncomfortable, she did find it a bit awkward. Over the past few months, she had had some less-than-professional thoughts about her Unit Chief. None of them had strayed into the lewd, lurid, or vulgar, but even so, being that close to Aaron made her insides flutter slightly.

She told the butterflies just trying to take flight for the first time to slow down. For now, she was just thinking about this situation by internally telling herself, ‘It’s just Hotch.’ y/n didn’t mean this in a demeaning way. Many of her close relationships or intimate moments with men were marred by pain or betrayal. So for her to simply and honestly say, ‘It’s just Hotch,’ meant a great deal. “Alright, but if at any time you feel like you need space during the night, just tell me and I’ll give it to you.” y/n smiled and nodded, saying, “I will, Hotch. Now, maybe we should put our stuff away so we can get to work?” Aaron nodded in agreement and he stood more straight. The pair grabbed the last room key and their cases. The duo moved down the stairs and to the end of the hallway where their room was.

Hotch pulled the door’s key from his pants pocket and fitted it into the lock. There was the pleasant sound of the bolt clicking back. Aaron took the metal handle in his large hand. The door swung outward, and he froze momentarily as he looked into the room. y/n noticed his shift in demeanor and softly asked, “What is it?” She pressed closer to him, and she realized why he had reacted as he had. The room they were supposed to share only had one bed and from the size of it, there was no space for another mattress. Aaron muttered something she couldn’t hear under his breath before he more loudly articulated, “There must be a mistake. I’ll talk to Obermann. Or we can talk to someone on the team. Emily will still let you sleep with her. I’m sure of it.”

While he said this, two thoughts were happening simultaneously in y/n’s head. The first was that her work phone had vibrated in her pocket about ten times since she and Hotch had been trying to negotiate about the room. y/n took a moment to look through her messages. It seemed the other team members were ready to start building the profile for the unsub and were waiting for her and Hotch. This meant she had little time to think about the second thought running through her head like a fire. Imagining sleeping in the same bed as Aaron, even momentarily pulled a light flush to her face. She pushed the latter thought back for later and said, “Hotch, we can figure it out later. I think the team is waiting for us in the rec room. Em said there’s coffee. Let’s just put our cases inside and you and I can figure this out later.”

Aaron turned to y/n with a furrowed brow. For a second he could see the flush on her skin but moved to look away not ready to acknowledge that fact yet. Though he wanted to rectify this situation immediately, y/n was right. He gave a small sigh and said, “You’re right. We can figure it out later." With that Aaron and y/n moved into the small space. Hotch pushed his suitcase under the small space of the bed while y/n placed her smaller case in the only open storage area the room had. When this was done, they both left the room; Aaron switched off the light and locked the door behind him. As they made their way back up to the first floor, Aaron sighed. This wasn’t particularly Obermann’s fault, but it was a unique situation for sure. One that he would resolve to make sure y/n was comfortable. For some reason when he saw her in pain or discomfort, it really ate at him. This had only happened twice, but those were two times he did not want to repeat. And he certainly wasn’t going to be the cause of her discomfort.

The pair moved back to the team and settled into the open spots at one of the tables in the rec room. The darkness outside the windows of the rec room seemed to try and penetrate through and around the lights on the rig, and the howl of the wind crashed with the waves as the team worked late into the night. They bounced ideas off each other and looked at the first three spots where the victims had been found. By 2:40 a.m. the team had a basic profile down. They assumed the unsub was around forty to fifty-five years old, which eliminated a little less than half of the twenty-five workers. They also assumed the man was important and potentially used violence as a substitute for sex and a form of release. y/n kept the idea of cabin fever in the back of her head and suggested acts of hysteria or depression for the profile. She clarified, “We wouldn’t see that behavior here, but while this unsub is not on the rig I think bouts of anger and depression might be a pattern. He might have even lost jobs because of this before.” Rossi agreed and said, “We can ask Mr. Obermann about people with those traits tomorrow morning. We also know the attacks happen at night when most of the crew are asleep and only the night shift workers are awake.”

Derek tacked on, “And they happen where there aren’t cameras or the lighting is too dark to see clearly. It’s often near the machinery to drown out any screaming.” Now that the preliminary profile was created, it would give the team a better chance to look over all the workers tomorrow who they were meeting in the morning. They had only met two men officially that night and it was the two security guards. One was a younger man in his thirties named Joe, and the other was in his fifties named Pete. The team had met the two while they changed shifts. Both men had introduced themselves and told the members of the BAU to call them at any time if they needed help. Derek and Aaron both clocked that neither man carried a gun, but did have retractable nightsticks in their belts.

By this point, it was nearly three, and many members of the team decided to call it a night. They needed to wake up at five a.m. to meet the oil workers before their shift started at 6:00 a.m. It was only Rossi, Garcia, Aaron, and y/n left awake. y/n could feel the weight of her exhaustion pulling at her. Her mind was foggy and looking at the files actually hurt her eyes. The lights on the rig at night were a bit dimmed and she longed to get to sleep. She pushed away from the table and Garcia looked up and asked, “Are you going to bed, darling?” y/n nodded. At hearing this, Aaron looked over to her and she approached him.

Mr. Obermann had retired hours ago and y/n was sure Emily was out like a light by now. She could see Hotch eyeing the couches again and she just barely touched his shoulder. He looked over to her and she nodded her head toward his phone, which pinged once. Aaron picked up the device and swiped up on _y/n_’s text message. He quickly read it over. The message read: “Hotch. I guarantee that sleeping in the same room, even the same bed as you doesn’t make me uncomfortable. It may be unorthodox by FBI standards, but I’m tired and I don’t to wake JJ or Emily. Please don’t sleep on those couches or stay up all night to try and make tonight better for me. You need rest too. If sleeping with me makes you uncomfortable, I’ll take the couch, just wake me up and let me know.” Hotch turned back to y/n and could see that she was being honest, about all of it. That she wasn’t uncomfortable, and that she would take the couch if he wanted to be alone. Again he had that feeling that he was being cared for by y/n. And even though he felt uncertain for some unknown reason, he couldn’t deny he’d rather be on a bed than the couches. Finally, he gave her a small nod letting her know that he would be down at the room later. Silently, y/n mouthed, “Night Aaron.” With that, she slipped into the corridor and out of sight. Garcia had observed whatever that odd interaction was between her two friends and she was sure something was happening. What that was, she couldn’t say yet, but with her snooping and pleading skills, she hoped to find out soon enough.

After another hour, Aaron was the only one still up. He was stalling and he knew it. With a sigh, Hotch put his loose papers in his file. He picked up the manila folder and moved downstairs. The grimy, dim hall lights flickered and the shadows seemed to move as Hotch walked down the small corridor. Hotch stopped outside the showers and considered taking one. Again he was stalling. He didn’t need a shower, he needed sleep. He passed the showers and tried to unlock the door as quietly as he could. It was dark in the room and he felt around the dark space for the edge of the bed. y/n’s slow breathing filled the room along with the sound of the wave slapping the sides of the rig.

Aaron knelt down and tried to quietly remove his suitcase from under the bed. He stopped once it was out and listened. From her breathing, it seemed that y/n was still asleep. He unzipped the case and at this point, his eyes had adjusted to the darkness. He found his grey sweatpants and a sleeping shirt. He couldn’t tell what color it was in the dark but it didn’t really matter. He wasn’t trying to impress anyone. Once he had the articles of clothing, he pushed his suitcase back under the bed. Once he was standing again, he considered moving back to the showers to change.

However, opening the door and letting in the light from the hall might wake y/n. He looked over at his agent who was turned away from him facing the wall. She was obviously asleep, and he decided to just quickly change in the room. He placed his pajamas on the empty side of the twin bed. He faced the other direction and quietly undid the buckle of his belt. He slipped it out of his belt loops and when it was free, he silently placed the leather on the bed. With a fast intentional movement, he undid the button and zipper of his pants. He slipped off the fabric and when his legs were free, he grabbed his sweats and slipped them on. Somehow Aaron felt that it would be alright if y/n saw him in his undershirt or even bare-chested, but something about her seeing his legs or worse his groin -- even if it was covered with briefs was too embarrassing to think about.

A tiny voice in his head said mockingly, “And you just thought ‘you weren’t trying to impress someone?’” Hotch grit his teeth, removed his shirt and undershirt, and put on the soft cotton of his sleeping shirt. Aaron folded the clothes that he had put on the bed and set them on top of y/n’s case. He would hang them up tomorrow. He slowly sat down on the edge of the mattress and it dipped slightly with his weight. Very slowly he moved his legs onto the bed and it was just long enough to fit his tall frame. He lay on his back. For his sake and y/n’s he decided to sleep on top of the covers, while y/n stayed bundled beneath them. This would at least give them a layer of separation between them. Aaron wasn’t sure if it was his stirring or even his body heat, but y/n seemed to momentarily wake, and in a sleep-heavy voice asked, “Hotch.” It was hard to tell if she was still asleep or not, but he softly replied, “It’s me.” This answer seemed to soothe her and y/n quickly fell back asleep. The exhaustion Aaron felt nearly made him fall asleep before he turned on his side to face the opposite direction from _y/n_. For once, he was grateful that he was so tired that his mind couldn’t wander to places he shouldn't let it.

An hour or so later Aaron woke when y/n made a small sound in her sleep. It was like a little hum or maybe the softest “yes” he had heard in his life. As he came to a more conscious state he realized that he was warm. Warmer than he had been when he fell asleep. In his sleep, he had managed to get under the covers and he was nestled next to y/n. His right arm was around her waist and his face was pressed into the soft smooth skin of her neck. Hotch stilled his body. Apart from the fact that being pressed close to y/n felt good, he realized that he needed to move slowly or he might wake her while he disentangled his body from hers. Hotch pulled his face back first, and in his tired mind, he thought about how he missed y/n’s crisp scent. Next, he removed his arm from her waist. y/n made another noise at this retraction but still didn’t wake. Aaron thanked the gods for apparently making y/n a deep sleeper. Finally, Aaron rolled onto his back and then to his original position facing the other wall. He was too drained to try and get out and above the covers again, and anyway, the warmth from both the blankets and y/n who was only an inch or so away from him felt good, and he fell back into unconsciousness after a few minutes.

In the morning, neither Aaron nor y/n had the time to reflect that they had ended up in each other's arms again during the night because they were jolted awake by the sound of someone screaming. y/n said, “It’s Garcia.” Both Aaron and y/n quickly put on their shoes and grabbed their guns in case there was any danger. Aaron moved out the door first and y/n followed closely after. The sound had come from the nearby showers. Mr. Obermann had set up for the showers to be open from six to seven a.m. each morning for the BAU women to shower safely and with the guarantee that a man wouldn’t interrupt them. This was something JJ had set up on the flight over to Alaska. JJ had ensured that the same was promised for the men on the team, but their hours were in the evening.

As Aaron and y/n arrived outside the showers, Morgan was gently guiding Penelope from outside. The technical analyst was sobbing and Derek sort of passed her over to y/n who put her gun away. Morgan firmly said, “Get her away from here, y/n. We have a new victim.” y/n nodded and she tried to comfort Garcia as they moved away from the new scene. Hotch slipped past them and at his point the whole team assembled. Rossi was acting as a guard against the workers who wondered what was going on, and if someone had been killed. As y/n passed JJ, she asked the media liaison to come with her and Garcia to provide another more comforting presence for Penelope. JJ nodded and they moved back to the women’s room.

It was a hectic three hours at the start of the morning as a coroner and the local authorities were called. The oil workers became increasingly restless with all of the authorities and the BAU around. To the men, so far these newcomers hadn’t done anything to protect them yet. Once Penelope had calmed, y/n sat on Emily’s bed and thought to the morning. To Hotch in his sneakers and grey sweatpants and dark blue shirt with his gun drawn. To Rossi in a dressing gown and undershirt, and Morgan in a tank and sweats. In fact, they had all been in sleeping clothes except for Spencer.

y/n expected that the young genius had stayed up all night. The sight of all of them with guns in such casual clothing would have been funny if it was in a dream or something. But this wasn’t a dream. They were isolated in the middle of nowhere. So far away from civilization that it took an hour for the coroner to arrive. y/n thought back to her isolation theory. She looked forward to speaking with Obermann when she got the chance to see what he had to say. She could also get JJ to look over the personnel files with her for clues as well. After Emily dropped off a soda for Garcia, y/n asked Garcia if she could describe what had happened in the morning and any clues she might have seen or observed. y/n had her pen and pad ready when her friend began to speak. Finally, the police left, the coroner took the body, and the team changed into their professional clothes and assembled in the rec space. Obermann and all the workers minus the fourth victim were assembled.

Obermann spoke first and said, “Alright, new rule. Teams of three only. No one moves alone, even to piss. No teams of two, teams of three. I’ve called corporate and am waiting for a response. If they tell us to leave today, we will. But until then we can still do our jobs. And if you can’t tell me. Before we get to today’s work, I’ll have Agent Hotchner speak to you. Listen to him and his team without any grumbling or complaints unless you want to be written up.”With that, Oberman stepped aside for Aaron. Hotch tried to make this quick. He could tell the men in front of him were angsty. He cleared his throat and said, “As Mr. Obermann said, I’m Agent Aaron Hotchner. I work for the FBI in the Behavioral Analysis Unit. I and my team are here to find the person who is making this an even more dangerous place to work. I am sincerely sorry for your loss this morning. I and my team standing beside me will do everything we can to try and not allow something like that to happen again while we are here. If any of you see something off or suspicious, don’t hesitate to tell me, our Media Liaison, or anyone on the team. I promise we won’t get in your way. For now, that’s all.”

Aaron stepped back and motioned for the team to move further back into the room as Obermann started giving the instructions for the day's labor. Aaron had cut out a lot of the formalities, his title, and the science behind the team's work. It wasn’t needed now. He had been speaking to hardened working men, not cops, and sounding fancy or professional wouldn’t make their opinions of him or the team any higher. As Obermann gave orders, Aaron similarly divvied up tasks for the team. Garcia, who had much recovered from her shocking morning would continue looking at the terror element and online leads. He and Morgan would look at the crime scenes. Rossi volunteered to watch the men at work and see if he saw anything that fit the profile. JJ, y/n, and Emily volunteered to look at the files of the employees again, as well as rewatch any relevant footage, and Spencer would work on a geographic profile if that was even possible in a space this small. Hotch, like Obermann, told his team that he wanted them in pairs. The events of the morning were a clear reminder that there was significant danger for everyone on the rig.

The team worked tirelessly through the day. They all even missed breakfast and lunch. They reconvened at mid-day and shared what they had. Rossi had suspicions about two men, Baker and Price. Em, JJ, and y/n had thoughts about three men: Slainfield, Parkins, and Jotenson. y/n also had a bad feeling about Pete. However, Pete was standing by them so she didn’t say anything to the whole team. But once the man was gone, she approached Aaron. He was leaning over his and Rossi’s notes on the table, but he acknowledged her presence by turning his head to her for a moment. y/n said, “I think that there’s something off about Pete. He seems to match the profile well and…” She paused momentarily and Hotch looked at her closely, saying, “And?” y/n swallowed and said, “Maybe this is silly but he gives me a bad feeling.” Hotch nodded and said, “It’s not silly. We’ll keep an eye on him.”

The team worked through the afternoon and into the evening. Every now and then they would update the group as they discovered new things. Morgan and Hotch had looked at the crime scene and the pictures of the victim. It was clear that this murder was faster and more reckless. It had happened in a more public place unlike the last three and there was less bruising which meant the death had been rushed. Hotch and Rossi made two hypotheses: one was that the killer was trying to show dominance to the team. To demonstrate that he could kill even with them watching. The second was that he was getting sloppy. He might be trying to show strength, but it was evident in the victim’s body that he was slipping. In the evening the team met for dinner with the rest of the workers.

The BAU members all sat together at a table on the far side of the room. Clear cliques could be seen in the oil men as the group sat and chattered softly. None of the men looked at the team and they clearly didn’t want to be overheard. It was clear that the team's presence and the fact that a killer was among them was altering their behavior. As y/n looked over the group and then to her friends it all suddenly felt like high school. And in a moment that felt like a bad teen romance, y/n thought of the morning, before Garcia had shifted the course of events for the day with her discovery. y/n had woken early. She wanted a shower even if she didn’t really need it. She had not expected to wake up warm and cozily tucked in Aaron’s arms with his face pressed into her hair. The comfort she found in his embrace knocked the senses out of her for a moment before she realized he was her boss and any feeling that might or might not been growing in her would be rejected. Not that she’d ever have the nerve to say or do anything. She liked her job too much to do something stupid. She liked Aaron too. As a colleague and friend, she wouldn’t want to make things awkward between them.

y/n came back to herself and wondered how she could navigate out of the small bed and his warm, strong arms to get to the showers. Just then Penelope had screamed and saved her from having to think about it. y/n snapped back to reality as Emily said something to her. y/n looked over at Prentiss and said, “Sorry, come again?” As she picked up her pizza for another bite.

To call the food good would be hyperbole, but the team was so famished the cafeteria-grade food tasted amazing. The workmen moved to finish their tasks for the night before turning in. The team continued working for an hour or so before many members also turned in for the night. Perhaps it was the cramped space or the fact that the daylight faded quickly leaving the rig in darkness much of the time, or just the sounds of the waves that made them all a little more sleepy than usual.

Emily, Garcia, y/n, and JJ were one of the groups to turn in early. _y/n_ could tell that Garcia was going to start asking her questions about what the night with Aaron had been like. To avoid having that personal conversation right now, y/n faked a yawn to indicate that she was really sleepy, which she was. Her strategy worked and Garcia, who was actively going to ask y/n about her night yesterday stopped herself realizing that her friend was tired. Each of the women moved to their rooms and got ready for bed. When the lights were off and y/n was under the blanket and her breathing was the only sound in the room, she thought she heard a creaking from the corner of the small space. y/n sat up, but there was obviously no one there. She lay back down and pulled the covers over her head like a little girl. The place unnerved her. It was like an isolated haunted British mansion with a vengeful ghost roaming the corridors. Except this ghost was real and would do more than scare you to death. y/n let out a sigh at her silly thoughts. She cleared her head and actually focused on getting some rest.

Aaron was not the last one up this time. That honor went to Derek who was chatting with Garcia about something technical that he wasn’t sure he fully understood. Hotch stood and excused himself. Aaron was smarter the second night, and he had set out a clean pair of pajamas and his toiletries for his shower night. Aaron grabbed the items and moved back to the shower room. Hotch stripped and moved into one of the communal showers. He pulled the frosted plastic curtain back for privacy. He turned on the water and flinched as the ice-cold water hit his skin. It took a moment before the warm water replaced the frigid.

When the hot water did come, he let out a little sigh. He didn’t know where it had come from. He assumed it was from being so tired. From the real and present danger his team was in, and also that there had been a dead body in this very space many hours earlier. As he reflected, he thought, ‘These cases certainly make strange bedfellows of places.’ And it was true. Where hadn’t he seen a crime? Churches, dressing rooms, parks, campgrounds, strip clubs, showers, houses, houses, houses… Aaron tried to not focus on the morbidity of his job. He was actually thinking about the ‘bedfellows’ part of his thought. Because this case was making him have a strange bedfellow in y/n.

In what world would something like this happen? In what twisted world was he so comfortable with it happening? He thought back to when he woke up holding y/n. Now Aaron actually stopped himself from groaning. ‘You’re tired,’ Aaron reassured himself. He more quickly worked through his routine of thoroughly cleaning his skin and washing his hair. After drying off with a towel and changing. He moved back into the room and settled into the bed. As he lay down, he looked at the metal ceiling painted an unimaginative hospital beige. He silently said, ‘You won’t hold y/n tonight.’ He repeated it a few times. It was a technique he used with Jack when he had bad dreams. Aaron told his son that if you say something while you’re awake, like, “I won’t have a nightmare tonight,” that it will happen in your sleep too. Hotch softly chuckled at the fact that he was using a comforting technique for his son on himself. As his thoughts shifted to Jack, he slipped into sleep.

It was the middle of the night, Aaron woke when he felt like all the air had been sucked from the room and a heavy weight seemed to press down on him. He shifted up and looked at y/n. He was surprised when he saw her eyes wide open apparently looking at the foot of the bed. He could tell something was off. Her body was stiff like a board. Aaron tried to get her to relax by gently shaking her shoulder and calling her name, but this had no response. Hotch swallowed and placed his fingers over her pulse. It was a bit elevated, but he could see her breathing normally. Her condition scared him, and he called her name again. After a moment y/n’s eyes shifted from the edge of the bed and up to the ceiling. Aaron knew there was nothing there, but he looked up at the flat surface anyway.

He tried shaking her again. He was wondering if she was having a stroke, but the odd symptoms didn’t look like those of a stroke, and y/n was far too young and healthy to have a stroke. He would have seen it in her medical history and not let her on the team. For another agonizing minute, y/n lay still. y/n seemed to snap out of whatever this episode was. She quite literally collapsed into the mattress, and she took large unsteady breaths like she was panicking or had been unable to breathe over the last few minutes. Aaron’s voice was filled with concern and worry, as he brushed his hand over her arm and said, y/n. What was that?”

In a strained voice, y/n said, “Lights. Give me a minute.” Hotch nodded, and he felt relieved hearing her voice, even if it did sound distressed. He leaned over to his side of the wall and flipped the light switch on. The low-level fluorescent glow of the overhead made Hotch blink a few times. When his eyes had adjusted, he watched y/n. Her eyes were closed and she was clearly doing some breathing techniques to calm herself and her nervous system down. Aaron’s hand briefly ghosted over her upright palm. For a moment he wanted the take it in his own hand, but he stopped himself. He grabbed at the sheets of the bed and made a fist with the fabric instead. After a few minutes, _y/n_ sat up. One of her legs was bent to her chest, and she placed her forehead in her right hand. Aaron cleared his throat and as if she just now remembered he was there, she turned her head to look at him with her forehead still in her hand. She looked so scared. Her eyes shone with it. After a final beat of silence, y/n said, “Do you know what sleep paralysis is?” Her voice was slightly hoarse, lower than its normal register. Hotch thought about what he knew about the condition. He’d heard of it before, but never experienced it himself. Softly, he replied, “I have. Though I don’t know a lot about it.” y/n nodded and then said, “Well now you’ve seen it.” Seeing y/n like this pulled at his insides, and he couldn’t take it anymore. He moved his hand to the small of her back to provide some comfort.

y/n seemed to settle with his touch, and she took her head out of her hand. Aaron wanted, needed some answers. So as kindly as he could, he asked, “What is that like exactly? You were so stiff for about three minutes.” y/n’s brow pinched for a moment and she replied, “It’s like locked-in syndrome a bit. You’re aware, awake but there's no moving or being able to snap out of it. You’re trapped until the episode is over. People see, hear, or feel things. One or all of those things can happen.” Hotch’s mind went back to while the episode was happening. She had clearly been looking at something at the foot of the bed and then at the ceiling. He asked, “Do you see things?”

y/n nodded and said, “Yeah.” Aaron could see the discomfort as she thought about it. Aaron wasn’t going to press, but he did wonder what she had seen. His unspoken question was answered by y/n, who said, “For me, I… I see a man. He’s large and cloaked in a kind of shadow. Like his body is there but not there. He smiles at me but other than his mouth there’s no face.” y/n swallowed thickly and said, “Normally he’s at the foot of my bed, but sometimes he’s near my face. Tonight he crawled up the wall and looked down at me from the ceiling.” While she spoke about the figure, her voice hitched and Aaron noticed the small sob she was trying to hide. Her description of sleep paralysis sounded horrible. His bouts of insomnia felt like nothing compared to what she described. It was an actual living nightmare. Hotch took a breath and started running a gentle circle on her back with his thumb. He wanted to know more. Like how often does this happen? Or if there’s something that causes these events. But right now he was more concerned about making sure y/n was comfortable and felt safe.

Aaron removed his hand from her back, and this made her look at him more intently. He first acknowledged how frightening that sounded, and he said, “I’m sorry you’ve gone through this. It sounds, scary. Is there anything you do that helps you calm down? Is there anything I can do to help? I could grab you a coffee, or give you space if you need.” y/n gave Hotch one of those small half smiles that she flashed him when he was doing something for her that he didn’t need to exactly. She replied, with a more stable voice, “I, um actually think that coffee might make it worse. Trying to stay up and outlast the feelings doesn’t normally help with anything. But maybe some water would be nice.” Hotch nodded and turned toward the small nightstand on his side of the bed. He grabbed the water bottle that he had taken from dinner. He had only taken a sip or two, and he offered it over to y/n saying, “Is this okay? I just had a sip, but I can get you a new one if you prefer.”

y/n chuckled lightly as she unscrewed the cap and took a drink. She really wasn’t worried about germs from Aaron. After a few sips, she put the cap back on and Aaron placed it back on the table. Aaron then asked, “Is there anything else?” y/n continued looking at him and said, “Normally I just grab a weighted blanket and and try and get back to sleep and pray it doesn’t happen again.” The idea that something like this would happen more than once in a night was abhorrent to Hotch. He looked around the room for anything that might act like a weighted blanket even though there wasn’t anything of the kind around. Aaron’s brain came up with an idea and his mouth voiced the thought before he could stop himself. He said, “Maybe I can hold you? It’s not a weighted blanket, but maybe it could help?” There was a silence after the offer was out there. Both Aaron and y/n were a bit surprised. Aaron bit the inside of his mouth at what he had said. He feared that he had crossed a line, and y/n looked at him like she was surprised that he had offered. However, much to Hotch’s relief, she said, “I’d like that, actually.” Aaron nodded and softly replied, “Okay. Do you want me to turn off the lights?” y/n nodded and laid back on the mattress.

Aaron switched off the light and lay flush with the mattress as well. He wasn’t exactly sure how to start what he had offered without it being awkward or uncomfortable. So he started by just taking y/n’s hand in the darkness. He gave it a gentle squeeze, and she let out a breath at his touch. His hand trailed up her arm to her bicep where he ran his pointer and middle fingers up and down the area gently. He wanted to ensure that she was okay with this. After a minute of this, y/n softly said, “Aaron, please.” Maybe it was the way he said his first name or the fact that he wanted to provide the comfort that gave him the courage to move his body close to hers. He placed a hand on her hip and asked, “Do you want to face my chest or face the wall?” Her comfort was most important to him. _y/n_ thought for a moment and said, “I’d like the face the wall.” Aaron hummed and positioned himself so his chest was against her back as she turned on her side. With his left arm, he wrapped his arm over her. It rested on her waistline. He didn’t add any pressure, but let the weight of his arm rest on the side of her body. y/n could feel that he was tense; he might even be flexing. She didn’t mention this and after a few minutes, he relaxed like her. When he did this she could fully feel him pressed against her. The soft area of this stomach pressed against her lower back. Before she fell asleep she said, “Thanks Hotch.” With that, she slipped into oblivion.

In the morning it wasn’t odd as they got up. Aaron checked in to see how she was, and y/n said, “I’m alright. I rested pretty well thanks to you. I really appreciate it, Hotch.” Aaron nodded and more nonchalantly than he really felt he said, “I’m just happy that I could help.” y/n moved to grab her towel, her work clothes, and her toiletries bag. She stepped into the shower and told JJ good morning. The media liaison was humming some county song behind her privacy curtain and told _y/n_ “Good morning,” as well. _y/n_ and JJ moved to the rec room together. The rest of the team was grabbing breakfast. As soon as Garcia saw _y/n_, she knew something had happened the previous night. The technical analyst and Emily approached y/n, and Penelope asked her, “Baby, did something happen? You don’t look well.” y/n shook her head and quietly told her friends, “I had another episode last night. It was a lot worse than the recent ones.” Garcia looked at y/n sympathetically and pulled her into a hug saying, “I’m so sorry, y/n. It’s gotta be this place. It’s giving me the heebie-jeebies too.”

Aaron watched on as Em, Garcia, and y/n had a quiet conversation near the serving table. He could just hear some of their conversation, and for a moment, he felt left out because y/n hadn’t told him about her sleep paralysis but had clearly let Penelope and Prentiss in on the secret. Aaron realized that immediately that was stupid because the conversation about her sleeping habits didn’t normally just pop up around him. What would she possibly say, “Oh yeah, every now and then a literal sleep demon shows up by my bed, and he doesn’t have a face. Also, I can’t move when it happens. And it could happen anytime I sleep.” Aaron chastised himself and stabbed another bite of eggs onto his fork. At least now he knew about one of the challenges that seemed to haunt y/n outside the job, and he now would do what he could to make her life easier while they were on cases.

The day moved quickly as some leads dropped cold and the pressure was on to get results. There hadn’t been a new attack which indicated that either the unsub was getting scared, or the fact that the team and the workmen being in teams of two and three had stopped them from being able to act. If the pattern of the last two killings heald, the unsub was likely to attack again today. During the afternoon, Spencer and y/n were discussing her theory and the idea that the unsub was impotent. Spencer said, “What if he’s not important at all, but has a pain fetish or something?” y/n looked at Spencer with apprehension, and she replied, “But the impotence matches with the profile. The bat or blunt object is clearly a replacement for the phallus. If the unsub has a pain fetish I think he would take much more time with the victim. Granted in a place like this, there can’t be a lot of time spent on each victim. I’m not sure, now it feels off.” Spencer leaned against the wall and said, “Let’s re-examine that part of the profile in a bit. I have some thoughts about your cabin fever theory.” y/n gave the genius a small smile and said, “Shot. I’m all ears.” What both agents were missing about the first subject of discussion was that it was possible that more than one person was influencing the way the victims were being killed.

It was late, again and Em and y/n were calling it a night. y/n had tried to get to bed before Hotch while they shared the bed. She hoped that if she was asleep when he got back, he would be more comfortable because they wouldn’t have to have any potential awkward ‘good nights’ or shifting around in the bed to try and get comfortable. y/n for one, took what felt like half an hour to find a comfy position and actually get to sleep. The hallway to their room was cloaked in oddly long shadows. For a second Emily thought she heard a dripping sound and looked around for the source of the noise, but she couldn’t see anything from the darkened hall. Emily looked over to y/n and said, “I don’t know about you, but I want to get the fuck off this rig.” y/n nodded in agreement and said, “That gets an Amen from me.” As Prentiss approached her door, she fished for her keys and muttered, “Shit,” under her breath. _y/n_ looked over to Emily and asked, “What is it?” Emily said, “I left my keys on the table.” y/n looked at her friend and then at her own door. It was only ten or so feet away and Em said, “You go to bed. I’ll be fine by myself getting my keys. JJ was planning on heading to bed soon too, so I’ll just walk back with her.” y/n said, “You’re sure?” Prentiss nodded and both women headed their separate ways. Emily moved with determination, wanting to get to bed as quickly as possible.

y/n moved down the hallway and passed the showers. Once she was past the site of the latest victim, a figure emerged from the entrance behind her. y/n wasn’t aware of the man’s presence until he spoke, saying, “Ma’am, you shouldn’t be walking alone.” y/n whipped around and saw the security guard, Joe. y/n suddenly felt a dread build in her stomach, and Joe stepped toward her saying, “Let me walk you to your room at least.” Just as y/n was about to say something, the man lunged at her. His strong hands found their grip on her neck and she choked as he restricted her airway and pushed her harshly against the metal wall. y/n tried to fight the unsub, but her lack of air was making it hard. In an act of desperation, she used her right hand to find the man’s groin and she took his manhood in her hand squeezing the area tightly. Joe removed his hands from her body and said, “Bitch,” as he moved back covering his groin with his hands.

y/n tried to catch her breath. She pulled for the gun in her holster with shaking fingers, but Joe was quicker with his nightstick. As he extended the weapon it gave a little swishing sound. Before y/n could fully protect her face with her hands the nightstick made painful contact on the side of her head. y/n reeled, and she saw stars for a second. y/n tried to stay upright, but the pain and confusion had her collapse against the wall. As she crumpled, she could hear Joe say, “How fucking dare you say I’m impotent. You’re going to regret that comment you little bitch.” y/n closed her eyes as she saw the man’s hand holding the weapon raise and lower with considerable force.

Hotch moved down the hallway and stairs that led to the first level of the rig. He was in desperate need of a shower and a distraction. The day had been rough on him. He had had to speak to Obermann about the men suddenly acting different, even with hostility toward the team. They were obviously all on edge, but that didn’t give them a right to badmouth his team. He had also had a very choppy call with Strauss and Mr. Ridgewell. Both were disappointed that he hadn’t found anything yet. Aaron had to explain to Ridgewell specifically how unique a case this was. Aaron wondered why Erin hadn’t told the Commissioner this information before. Was his boss angry with him as well? Making him do this sort of soft groveling as a sort of punishment? To prove that he and the team were valuable?

Aaron had also had a long conversation with Garcia about more messages that had been shared from the alleged foreign agents. Hotch was beginning to think that this part of the case was all a ruse by the unsub to distract the team’s time and energy. As Hotch got lower on the stairs, he heard a snapping sound and the small moan of pain that came after a particularly loud cracking sound had his hand on his gun in an instant. Aaron quietly moved down the final steps and he saw the younger security guard leaning over a prone figure that he recognized as y/n. Aaron authoritatively said, “I have a gun pointed at the back of your skull. Unless you want your brains decorating these walls, I’d put your hands behind your head and slowly stand.”

Unfortunately for Aaron, Joe had heard Hotch’s footsteps and had grabbed his knife, which he kept hidden in his back pocket, and pressed it close to y/n’s neck. Joe called back, “I wouldn’t if I were you, Agent Hotchner. I have my knife pressed to your agent's neck. So unless you want her bleeding out from her carotid artery, I’d put down your gun, and kick it toward me.” Aaron clenched his jaw but replied, “Alright. I’m doing it now.” Hotch would never jeopardize a member of his team. The fact that he couldn’t see how hurt y/n was and the fact that she wasn’t moving almost made him sick. He slowly moved his center of gravity down and set his handgun on the cold smooth floor. Aaron pushed the weapon toward Joe. The unsub felt behind himself until his hands found the gun. Joe moved to face Aaron, dropped the knife, and grabbed his nightstick instead. Joe commanded Aaron to raise his hands and put them behind his head. Hotch did as told and when the unsub was a foot away from Aaron, Joe quickly raised his nightstick and hit Aaron on the side of the face. The blow wasn’t well aimed, and it didn’t have as much power as he had used with y/n, but it was still enough to incapacitate the FBI agent for a moment. As Hotch slumped against the wall, Joe pushed past him and ran to an external door at the end of the hallway.

After a second, Aaron came back to himself and he clicked on his open communication channel with the team and he said, “The unsub is Joe Pabst. He just attacked y/n. He exited the southwest door. The channel came to life as Aaron moved toward y/n on unsteady feet. He collapsed next to y/n and checked her breathing and pulse. It was clear that she was unconscious and battered, but her pulse seemed alright. She seemed to be struggling to breathe due to the trauma on her nose. Aaron couldn’t tell if it was broken or not, but the blood leaking from it and the bruising already there told him that it was hard for her to breathe through it. Thankfully Rossi and Garcia came to his side in a second. Rossi motioned that he would stay with y/n and at seeing this, Aaron got to his feet to pursue the unsub. He listened as Derek, Spencer, and Emily approached the man who had harmed y/n.

Outside on the slick side of the rig, Aaron fought the wind. He moved up to the top platform and saw Derek and Spencer in a stand-off with the unsub who was on the rigging of the derrick itself. A light shone out, highlighting the unsubs form standing high above the waves. Hotch lined up a shot with the second gun he wore on his left ankle. Just as he was preparing to fire an incapacitating shot, Joe moved to the edge of the small platform, and by some twist of fate, or a simple design flaw, the chain railing slipped from one of its posts, and because Joe was leaning his weight on the barrier, he flailed wildly before plummeting into the choppy sea below. Aaron called Morgan on the secure channel and said, “Go see if you can find Joe. I’ll wake Obermann and let him know what’s just happened.” Derek confirmed Hotch’s directions. As much as Aaron would like the unsub to drown, it was still his job to make sure monsters like Joe faced the full weight of justice if possible.

A half-hour later Aaron was back by _y/n_’s side. Rossi had moved y/n to the rec room and the travel medical evacuation team was en route. y/n hadn’t woken yet and Aaron feared a bad concussion or worse, something like a brain bleed from the head trauma she had received. Aaron also couldn’t deny that he was feeling unwell. The lights were a bit bright for him, but he ignored his own pain to be seated next to y/n. When the helicopter came, Rossi insisted that Aaron ride with _y/n_ to the nearest hospital because he might also need medical care. Hotch acquiesced and boarded the helicopter with the paramedics and pilot. The sun was just rising above the horizon line as the chopper moved up and over where the Alaskan sea met the cold, hard land. At the hospital Aaron reluctantly submitted himself to an exam, but he only had thoughts for y/n who was seen a few rooms down.

When y/n woke a few hours later, her head pounded in pain. Even though she was hurting, she could sense that she was somewhere new. Her last memories were of Joe approaching her. As someone near her shifted, she opened her eyes and tried to see through the glare of her blurry vision. Aaron sat up as y/n stirred. His head was lightly bandaged to stop the bleeding from his temple. y/n struggled to say, “It was Joe.” Aaron nodded and said, “Yeah. Joe and Pete, but we can talk about that later. I’m going to call the doctor for you.” As Aaron waited for Dr. Ramirez to come and check in on y/n he looked her over again. Her face was deeply bruised. There were other sites of trauma on her body including a fractured wrist and some bruised ribs. The doctors assumed that she had a grade III concussion due to the fact that she had been unconscious for as long as she had.

Hotch could see the pain in her eyes, but even so, she said, “It’s nice to have someone I really like be beside my bad instead of shadow man.” y/n cringed slightly from the pain and how stupid ‘shadow man,’ sounded to her. She had never named her sleep paralysis demon. She refused to give it an identity. She looked at Aaron who was also a bit damaged. She wanted him to hold her again, but due to the fact that they were in a hospital, and he was her boss, that seemed a bit implausable. So she extended her hand out to him. Aaron took it in both of his hands, and his calloused fingers moved gently over her knuckles and palm. Before she closed her eyes against the brightness of the room, she saw a ghost of a smile on Hotch’s face. It always showed up in the crow's feet by his eyes.

A day and a half later, the team was headed back home. Joe’s body still hadn’t been found in the rough sea. It was possible that it may never be recovered. Aaron was fine with this. Pete, who had influenced Joe had been taken into custody and was awaiting a hearing. The doctors had recommended a three-day leave of absence for Aaron and a week-long recovery period for y/n for both of their healing. Aaron was going to insist on a longer break for y/n. And if he was medically forced from the office, that should give the team a bit of a reprieve as well.

As the team settled on the jet, Aaron found himself seated next to y/n. Discretely, his left hand found its place close to her thigh. The tips of his fingers softly touched y/n’s upper leg. y/n seemed to be asleep, and Aaron looked over her face which was bandaging on her nose, crown, and temple. At his touch, y/n shifted her body towards his in her sleep. The part of Aaron that was growing fonder and fonder for y/n contentedly filled his chest. He would have to do some self-reflection once he was home about these feelings. Once his hand was a bit more firmly planted on y/n’s leg, Hotch thought about how demons really were real. Either those who showed up unwanted in horrifying waking nightmares, or people like Joe, who had been influenced by the older, isolated, and impotent Pete, who had told his protege to enact violence for him. But as Aaron looked over the dimmed jet cabin at his team --all of whom were asleep except for Garcia and Rossi. Aaron thought of them as his gaze returned to y/n. Yes, demons were real, but he was there to take care of them, whatever form they took. And that gave him the strength to keep going.

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sjsmith56 · 2 months ago

Text

The Duality of Nature, Chapter 11 - First Day

Summary: In a flashback of the first day after Bucky and Noelle meet, they begin the process of learning more about each other.

Length: 5.2K

Characters: Bucky, Noelle, Sam, the One.

Warnings: Mild sexual content. Ex-husband issues, rude and sexist slurs.

Author notes: French phrases from Google Translate. Il est mon ami. S'il vous plaît, ne lui faites pas de mal. English translation from Google: He's my friend. Please don't hurt him.

<<Chapter 10

When Bucky awakened in his hotel room that morning and saw the tousled blonde hair of Noelle nestled against his shoulder his first reaction was one of relief. She didn't leave at first light. It meant he likely didn't have a nightmare and must have slept through the night after what had been an incredible experience with this beautiful woman. Breathing in the essence of her hair, he closed his eyes, savouring her presence while resisting the urge to run his hand through her tresses, and indulging himself in the touching of her.

In his journey of learning to be human again he had read a lot of different self-help and psychology books, as well as writings on relationships. One of his favourites; The Five Love Languages: How to Express Heartfelt Commitment to Your Mate had posited that five actions would determine how a person shared love with another; compliments, quality time, gifts, acts of service and physical touch. For Bucky, he strived for a combination of all of them, but he also acknowledged a marked preference for physical touch, both giving and receiving. Considering the years of physical abuse he received, and the amount of death he inflicted using his own hands it was both an effort to rewrite the programming which conditioned him to accept the abuse, but also to counter that abuse in the form of offering gentleness instead.

During his time in Wakanda he barely had the opportunity to put it into practice before they battled Thanos, and he became one of the decimated. After his return to the United States, there were more important things to take care of, his arrest, plea deal, and court ordered therapy, a process that was almost more regressive than helpful. Getting used to living in Brooklyn again had taken much of his energy and the fast-paced world of dating left him confused at best. Moving to Delacroix and living with Sam had been a way to step back and re-evaluate how he wanted to proceed with his life. Then, with the call to restart the Avengers, it seemed that fate was bringing him back to the New York area once more, even though it was just a short visit.

Noelle sighed in her sleep, and he hesitated for a moment, his fingertips poised beside that soft hair that beckoned to him. God, he wanted to touch it, wanted to see her eyes flutter open and the intake of breath she would make when she focused on him. He fantasized about her smile, as she realized that he had kept his word, pleasuring her and pleasing her in the several rounds of lovemaking they had indulged in, while always checking in on whether any of it made her uncomfortable. For a moment, he smiled, remembering how surprised Noelle had been when he drew her a bath, putting in lavender scented epsom salts, then offering to wait in the bedroom area until she was done. Instead, she had invited him to share the tub and showed her trust by leaning back on his chest, as he used the store-bought sea sponge to gently squeeze the warm soothing waters over them.

Their conversation during that downtime was quiet and contemplative but also informative. He learned her full name, Noelle Émilie Belanger, born in Paris to a French restauranteur couple who relocated to New York when she was five. She spoke fluent French and was pleased to learn that he did as well. Although she grew up in the restaurant business, she was more interested in science and medicine, choosing to go into nursing, now working as an ER nurse. Her older brother André took over the restaurant after her parents retired and moved back to France. After telling Bucky that much she caressed his left hand.

"Tell me something that isn't generally known about you," she asked.

He watched her fingers gently play with the vibranium plates on his hand, liking her light touch on him.

"I read, a lot," he admitted. "When I was young, I read a lot of science fiction and fantasy literature. I imagined what it would be like to live long enough to see rockets, and robots. I really hoped to live long enough to ride in a flying car. Of course, I always pictured myself as an old man when I did."

"Do you feel robbed of your life?" she asked, then she squeezed his hand. "Sorry, that's kind of personal."

"It's a valid question," he said. "During the years I was not myself, I was aware of time passing but I had no idea of the year or the current events that were happening. Although memories popped up there was no sense of how long it had been since the moment actually occurred. When I got away from HYDRA, I went through a period of time feeling out of place and out of sync with everything. I was angry about never seeing my parents or sister again. Steve was the only one that looked the same. Then he left me here."

For a moment Noelle thought he was upset, and she turned to face him. When their eyes met, she saw the incredible sadness in them, so she reached out, placing her hand on his cheek, then resting her forehead against his chin. Running his right hand up her back he said nothing until she looked at him again.

"Sam became my friend and that helped but when I saw you at the club it was like the perfect moment of clarity. Without even knowing your name or anything about you, something inside of me said I had finally found my home. It's crazy and totally unrealistic but it's how I feel, Noelle. All those years I spent enduring pain have led me to you."

Noelle said nothing but stayed in his arms, while keeping her hand on his cheek until she placed it on his chest. When the water became too cold to stay in, they got out, drying themselves off before returning to the bed. The nurse part of her straightened the sheets and covers of the bed, fluffing the pillows for good measure, then she curled into Bucky, placing her hand on his chest again as she rested her head on his shoulder.

"My ex-husband cheated on me," she said, with the softest of sighs. "I got sick and found out I had a sexually transmitted disease. Devastated doesn't even begin to describe what I felt when the doctor told me that. When I confronted Mark, he just shrugged then admitted he had been seeing street prostitutes, exploring his darker fantasies. He didn't even apologize for what he passed on to me. No one knows about that, not even my best friend. The worst of it is that although I was treated, and I don't have the disease anymore it damaged my fallopian tubes. I likely won't be able to get pregnant. You should know that before we go any further."

She turned away then, leaving space between them, and huddled into herself on the other side of the bed. Gently, Bucky pulled Noelle back towards him, so her back was firmly against his chest. He kissed her shoulder, then wrapped his arms around her, feeling the tenseness slowly leave her body.

"So, trust is hard for both of us," he whispered. "I'm not sure I can be a parent, not after what I've been through. If it's not meant to be then being only with you is enough, more than enough." He pulled her hair off of her neck, kissing the spot under her ear. "When do you have to go back to work?"

"Tuesday," she said. "I took today, tomorrow and Monday off."

"Spend that time with me," he suggested. "On Tuesday morning, if you decide we don't have a future I'll go back to Louisiana and I'll never bother you again."

"I have things to do," answered Noelle. "Laundry, grocery shopping, ordinary things."

She could feel Bucky smile against her neck. "Sounds perfect. I can do ordinary." He could feel her jaw move as she smiled in response. "Is that a yes?"

"Yes," she replied.

They slept after that and she was still sleeping, as Bucky watched, still amazed that she said yes to three days with him and was still here in his bed, still not believing that he didn't have a nightmare and strike out in his usual panic.

"You're staring," she murmured, then lifted her face to him, sleepily opening her soft brown eyes and making his heart flip at how she smiled.

"Sorry," he replied, then finally caressed her hair that had enticed him for the entire time he had been awake. "I'm a morning person."

"So am I," she answered then raised her head. "What time is it?"

"About 7:30, I think. Do you want to freshen up?"

"No morning sex?" Her impish grin went straight to his core. "I won't say no."

Not one to refuse such an offer, Bucky pulled her on top of him, so he could look at her as she hovered over him. The press of her breasts into his chest as she kissed him, while her hair tickled the sensors in his prosthetic shoulder in a way that he had never felt before, all added to the pleasure he was taking from the contact between their bodies. She pulled away briefly, noticing how he looked at her at that moment.

"What?" Her eyes scanned his whole face before settling on his lips.

"You're so beautiful." He bit his lower lip for a moment, hesitating to say anything more but she kissed him softly before pulling away again. "I want to wake up next to you for the next 50 years ... longer."

"You have such a way with you," she murmured as she began kissing him again.

He proved it over the next hour, in more than one manner. When they finally both fell onto their backs, the sheets almost pulled off, the duvet askew, and their bodies both bathed in a fine sheen that made them feel like they were glowing, she looked at this man she had barely known for 12 hours, thinking that he might be right about them.

"Est-ce que tu va bien, chérie?" he gasped then repeated in English. "Are you alright?"

"Je serai," she replied, touching his hand with her fingers and intertwining them together. "I will be." She rolled onto her side, so she was facing him. "Are we crazy for thinking we have a future?"

"Totally," he smiled.

His phone buzzed and he reached for it, holding it in his hand as he read the text. A small smile creased his face, and he looked up at Noelle.

"It's Sam, asking if we want to go for breakfast." He looked at her, expectantly. "It's up to you."

"Well, I would rather have the chance to go home and change," she replied. "That dress is kind of too much for a breakfast buffet."

"We can have it in the room. Tell me what you want, and I'll order it while you shower. I think he just wants to make sure you're not toying with my heart."

There was a soft grin on his face as he said it and Noelle laughed, a sound that he wanted to hear often.

"Alright." She snuggled in close again to him, watching as he texted Sam. "He's leaving today?"

"Yeah, for Delacroix, near New Orleans," said Bucky as he sent the text and turned towards her again. "He co-owns a fishing boat and promised his sister he would be back today."

"You live there as well?"

"I sleep on their couch, although her two boys are supposed to be doubling up so I can have one of their rooms. I did live in Brooklyn after we took care of Thanos. Part of it was for legal reasons but I also thought being there would be familiar." He breathed out. "It wasn't the same."

"I can understand that," stated Noelle. "I missed Paris when we moved here but when I helped my parents move back it wasn't the place I remembered. Maybe I became too Americanized, growing up here." She kissed him, then pulled the covers back and stepped out of the bed. "I should have that shower. You can join me if you promise to be good."

"Darlin', I'm always good," he grinned. "What should I order you?"

"A cheese omelet, glass of orange juice and some yogurt, please," she answered, as she walked away from him.

He watched her as she stopped at the counter and picked up some toiletries. Despite her protestations the previous evening about being a bigger woman, he thought she was perfect. She was obviously comfortable enough in her own skin to be naked in front of him, not trying to hide or cover herself from his view. When the bathroom door closed behind her, he phoned room service, having received a text from Sam regarding his food choice and ordered breakfast for all three of them. With a delivery time of 45 minutes, he notified Sam not to be early, then put the phone down and entered the bathroom.

Noelle had put her hair up in a bun, then put a shower cap on. At that moment she was washing her face in the shower, smoothing the cleansing lotion over it and rinsing it off, with her eyes closed.

"I'm here," announced Bucky. "Just getting in behind you."

"Okay," she answered, moving closer to the wall.

She opened her eyes, looking up at him as he stepped closer to her. Without her heels she came up to his eyes.

"How tall are you?" he asked.

"5 feet, 10 inches. Tall for a woman. How tall are you?"

"6 feet even." He lowered his face to her neck and brushed his lips against it. "Perfect for each other."

"You promised to be good."

"I'm keeping that promise. Doesn't mean I can't touch you."

The grin he gave her then became very familiar to her over the next three days, as he used it often. Not that she put up much resistance, as she felt the same level of attraction to him, as he did to her. When she finally got out of there, after he left, dried himself and stepped back into the room, she was surprised to see her clothes neatly folded on the bathroom counter, along with the moisturizer he insisted she buy and the hairbrush. She could hear voices from the other side of the door, interpreting it to mean they used most of the time they had for delivery of breakfast while in the shower. It was a wonder the water didn't run cold. Quickly, she dried, put the lotion on her face and got dressed, then brushed out her hair. She came out to a table set for three. Bucky was in jeans and a T-shirt that emphasized his physique, his metal arm on full display.

"I heard voices," she said.

"Room service," replied Bucky, coming over to her. "I just texted Sam to get over here." He pulled her into his arms and once again kissed her neck before kissing her on the lips. Noticing the smile on her face he smiled back. "You don't mind, do you? I just really like how you feel in my arms."

"I don't mind. You don't mind if I eat barefoot, do you? Breakfast is meant to be eaten in a sunny room, near a window, barefoot so I can bring one foot up on the chair if I want. It's part of my Parisian upbringing."

"Tous ce que vous voulez," he replied. "Whatever you want."

A gentle knock on the door revealed Sam on the other side, carrying a suit bag and satchel-style bag. He stepped in, dropping them off on the bed, which Bucky had taken the time to straighten a bit.

"Good morning," he said, waving his hand at Noelle. "How are you?"

"I'm good," she smiled. "It's all good. Please sit."

Like the gentlemen they were, they waited for her to sit first, then sat. She noticed the pot of coffee and picked it up, offering some to Sam first, then Bucky, while she stuck with her orange juice. Bucky lifted the covers off the plates, switching his and Noelle's, and the three of them began eating. They had quite a lively conversation, with a lot of teasing between the two men, but also a lot of laughter. When Sam checked his watch and said he had to get to the airport, Bucky cleaned up the table, pushing it out to the hallway. Sam took her hand, then kissed it respectfully, before looking seriously at her.

"Il est mon ami. S'il vous plaît, ne lui faites pas de mal."

"Your French is very good. How did you know I spoke it?"

"Your friends. I care about him, and he hasn't been with many women since he got away from HYDRA." He looked back at the door, noticing Bucky was talking to someone in the hallway. "I know you've been hurt as well."

"He's lucky to have you as his friend and I wouldn't hurt him," said Noelle. "As crazy as it sounds, I think he's the man I'm supposed to be with. These next three days will help decide that."

"I'll hold you to that," he said, then he kissed her on the cheeks, French style.

"You makin' moves on my girl?" asked Bucky, striding back inside. He stood proudly next to Noelle; his hand poised just above her lower back. "I'll see you Tuesday, okay?"

"You bet," replied Sam, picking his bags up. "Have fun."

With the table taken care of Bucky packed his two bags, while Noelle put the toiletries he bought her back in the shopping bag. With a final check of everything they left the room and headed down to the lobby where Bucky checked out. They headed out to the front, where the doorman opened the door to a taxi at Bucky's nod. Sitting in the back together he grasped Noelle's hand, as she gave the driver her address.

He pulled up in front of a building in the DUMBO neighbourhood in Brooklyn. Bucky raised his eyebrows slightly, as it was a desirable area to live that was more expensive than the area he had lived in. After paying the driver he helped her out and stood at the entrance.

"This was our home," she said. "Mark was a stockbroker. Although I got the condo in the divorce, he pays the HOA fees in lieu of alimony as I didn't want him thinking I needed his money to survive. Eventually, I will sell this place and I won't take another penny of his. He owns another condo in Manhattan where he entertained his ... friends."

She took his arm, and they entered the building after the doorman opened the door, greeting Noelle. Getting out on the fourth floor she unlocked the door and stepped inside the 1-bedroom open plan apartment, allowing Bucky to close the door behind him. It was very modern in appearance, although it was homey as well, without appearing staged. It looked lived in, and he smiled, feeling very comfortable there.

"Bathroom is here, and this is the bedroom, if you want to put your bags in here," she said. "I'm going to get changed into something comfortable."

Bucky dropped his bags onto a chair in the bedroom, then went out into the living room area of the apartment, looking at the view out the window. If he looked far enough to the left, he could just see the Brooklyn Bridge. A few minutes later he could hear the soft pad of bare feet on the hardwood floor, that became muffled as Noelle walked on the rug. Then he felt her arm snake around his waist, and he turned to her, liking how she looked in blue jeans and a tunic-style top.

"I like your place," he said. "It feels comfortable."

"Mark took his ultra-modern furniture with him, and I replaced them with pieces that I liked."

She went up on her toes to kiss him as he pulled her closer into his body, liking how she molded herself to him.

"So, what ordinary thing do you have to do first?"

"I need to get my laundry started, then I usually do groceries while the first load is going," she replied, pulling away from him.

He released her with a kiss on her forehead, waiting while she put her first load into the washing machine, located in a closet in her hallway. Then she unapologetically pulled out a fold-up shopping cart from a storage area, along with some cloth shopping bags and grabbed her purse, slipped on some sandals and presented herself at the hallway to the door. On the elevator down they said nothing, but he took the cart, shifting it to his left hand. They walked to Dumbo Market, located in the area between the Brooklyn Bridge and the Manhattan Bridge. It was busy as people were out enjoying the warm day or doing their own grocery shopping. Bucky drew a few glances, apparently recognized, but no one approached them, and they were soon on their way back, with the shopping cart loaded with fresh vegetables, fruit, pasta, and assorted cuts of chicken and beef.

Back in the apartment, Noelle transferred the clothes from the washer to the dryer and started another load, while Bucky took the food out of the shopping cart, placing the items on the kitchen counter.

"That seems like a lot for a single person," he noted.

"I have a guest," she replied, putting things away, as he watched her process. "I sure hope he likes my cooking."

"I'm sure he will." He stood behind her, wrapping his arms around her waist. "Perhaps he can cook something for you."

"Has he been schooled by Michelin starred chefs?" she asked, a cheeky grin on her face, that was kissed when she looked back at him. "Don't worry, I like a good cheeseburger every once in a while. There's a great diner nearby that will likely never earn a Michelin star but their burgers are to die for."

"Good to know. Now, come here. I've waited long enough."

Firmly, he turned her around, and placed his hands on her cheeks, kissing her on the lips before drawing her tightly into his arms. She held him just as closely, running her hands under his T-shirt and over his back. His body heat enveloped her, making her feel like she was baking on a sandy beach. When the kiss ended, somewhat reluctantly, Bucky gazed at her face, his blue eyes reflecting the blue skies outside. Stroking her face, Bucky placed his hand in hers and led her to the bedroom, where he began to undress them, methodically removing her top, then his T-shirt, followed by her bra, then his socks, and ending with their jeans. Gesturing to the bed, he climbed on after Noelle, caging her inside his arms before he lowered himself to kiss her. They were interrupted by the sound of a phone ringing and stopped to look at each other.

"That's mine," she said. "Ignore it."

Picking up from where they left off, they began making out again, taking the time to touch each other sensuously as things ramped up between them. The phone ringing again, interrupted them and Noelle sighed, shifting to get off the bed. Bucky took her hand.

"It could be work," she said apologetically. "Sorry."

She stepped out into the open plan area and answered the ringing cell phone.

"Oh, it's you," she said, her voice becoming coldly neutral. "What do you want, Mark? I'm kind of busy." She paused. "No, you can't come over. We're divorced. The final decree came in a month ago, the condo was officially transferred to me, and I expect to see the HOA payments continuing to be paid on my behalf." Bucky could hear her breathe out angrily. "So, what if I was? I'm supposed to stay at home and play the role of the martyred spouse? Which one of your sleazy friends was there?" Getting off the bed, Bucky came out to the doorway, leaning against it as he watched Noelle sitting on her couch, with her phone to her ear. She looked at him, trying to stay civil to the man on the other end of the line. "No, you can't. It's over and I'll never take you back."

A few seconds later she hung up and tossed the phone on the coffee table, then buried her face in her hands. Bucky sat next to her, pulling her onto his lap and cradling her as she cried. The phone rang again, and this time Bucky picked it up but didn't say anything. Instead, he just listened, putting the phone on speaker.

"Get this straight you bitch," said a man's voice. "Because of you, I lost my job. Because of you, I was made out to be some kind of psycho because I was exploring an alternative sexuality that you wanted no part of. If I want to come over to my condo and see my ex-wife, you're not going to stop me. Do you hear that, Noelle?" There was a pause as he waited for her to answer. "Noelle, answer me, you fucking bitch."

"If you come near here, you'll have me to deal with," said Bucky. "Do you hear that, Mark?"

"Who is this? Are you the guy who picked up my wife in the bar last night? You don't know who you're dealing with, Pal."

Bucky smiled slightly, looking Noelle in the eyes and nodding his head at her.

"You definitely don't know who you're dealing with," he replied. "I'm the man who's going to render you redundant. I'm the man who's going to worship Noelle like the queen she is and thank the heavens that I found her after you treated her like trash. I'm the man who made her come so many times last night that she lost count. I'm the man who knows a thousand ways to kill someone, but I wouldn't dirty my hands on a worm like you."

"Are you threatening me?" Mark was almost hysterical in his anger as Noelle looked alarmed at his tirade.

"Not at all, just stating a fact," said Bucky, calmly. "My name is Bucky Barnes. I used to be the Winter Soldier, but I don't do that anymore." There was dead silence on the other end. "You listening, Mark? Noelle isn't your possession. She's a living, breathing goddess who is much too good for slime like you, and for me too, for that matter. I just know it and accept it. Lick your wounds, pay her HOA fees like your divorce agreement detailed, and never call her again. If you do that, we're good. You come within a hundred feet of her, or call her in any way, I'll know, and we won't be good. That's not a threat ... that's a fact. You got it, Mark?" There was no sound. "I didn't hear you."

"Yeah, I got it." There was silence again. "Tell her I'm sorry and I won't bother her again."

"Good decision," replied Bucky and he ended the call, then looked at Noelle. "I'm sorry for intervening but I couldn't stand hearing him call you those names. Are we okay?"

She smiled. "Can I take you with me to shop for a car?"

Bucky laughed, with a smile so gorgeous that Noelle fell in love with him a bit more at that moment. He picked her up and carried her back to the bedroom where they remained for most of the day, alternating between making love, doing the laundry and making something to eat together.

That night Bucky woke up from a nightmare and pulled his boxers on, moving out to the living room area, lit only by reflected streetlight from outside. He looked out over the cityscape for a time until he eventually laid down on the couch. Soon, he fell asleep, curled up into a ball. Noelle woke to an empty bed about an hour later, and came out, seeing him on the couch. Gently, she draped a throw blanket over him. Then she returned to her bed, still feeling safe and secure because she knew the man in her living room had already committed to her. The idea of a relationship with Bucky wasn't an "if" anymore. It was a certainty.

🌃 🌉

The grey forms shifted as they absorbed the memories they had gathered from the three entities; one in this universe, two in the other. Their interactions, from the moment of their meeting in that place full of a sound, called music, had puzzled the One, at first. Unlike the first entities they took into their universe, those who initially pierced the barrier between them in an effort to lessen the One, these three entities had also intrigued them. The Barnes entity, who unknowingly shared a solid form with the Soldier, but both of them having a connection to the Noelle entity in a way that was previously unknown to the One. A thought rippled through the body of the One. Perhaps their shadow forms could be made more solid, like the duplicate form created for Barnes so that he would survive in their universe. If portions of the One could experience existence in a single form like Barnes, understanding could be possible. It might also improve communication which had been uneven and difficult so far.

"More data needed."

Yes, more data was needed, like the viewing of the small entity, Winnie. Barnes reaction to the Winnie entity had been alarming at first but the shadow form of the One who was with Barnes at that moment, cautioned against taking action against Barnes. They were close, so close to understanding what drove the inhabitants of that universe.

"What of the lost portion of the One?"

That thought spread throughout this universe, almost overwhelming the One which allowed the thought to undulate through and back again until it calmed. The lost portion had been ripped away so long ago and had likely undergone its own process of evolution in order to survive the inhospitable environment of that other universe. The presence of the solid form of Barnes in the other universe had proven that the sentient portion of the One may have survived. Despite the desire to reunite it with the main body, it might not be possible if it had evolved into a form that required a higher temperature to exist. That it was there in that other universe was certain but where exactly was unknown. More data was needed.

Chapter 12>>

Series Masterlist

Please support the author by reblogging.

#bucky barnes #bucky barnes fanfiction #buckybarnes original female character #james buchanan barnes fanfiction #bucky barnes romance #soft winter soldier #winter soldier fanfiction #vibranium universe

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thoughtportal · 3 months ago

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The etymology of the word translation—“to carry across”—conjures an image of physical labor. It is deeply relational, requiring at least two bodies, those of an author and of the person who carries the author’s words to a previously unvisited place. Let’s say we removed the laborer and replaced that person with a car. Or a train. Suddenly there is a feeling of weight lifted, certainly ease, and perhaps a little relief. But the intimacy of the earlier image no longer holds. Whether this matters has been the subject of recent debate as some publishers consider using machines to replace human translators and what that decision might mean for an ancient art.

In November, Dutch publisher Veen Bosch & Keuning (VBK), a subsidiary of Simon & Schuster, announced that it would trial the use of artificial intelligence (AI) “to assist the translation of a limited number of books.” Reactions rose in a flurry: Writers, publishers, and translators contended that AI would produce “bland” work. They lamented the possibility of lost jobs. The European Council of Literary Translators’ Associations resisted the standardization of an idiosyncratic process, stating that the imagination, understanding, and creativity that translation demands are “intrinsically human.”

VBK’s decision to incorporate AI into the editorial process may shock some but is not unprecedented. With a broad range of AI tools now available on the market, an increasing number of writers and publishers have turned to large language models (LLMs) to assist in, or contribute to, the production of creative work. As of February 2023, there were more than two hundred e-books in Amazon’s Kindle store that listed ChatGPT as an author or coauthor, according to Reuters. Maverick publishers like Spines, although small players in the global book market, plan to publish thousands of AI-generated books next year.

AI isn’t new to translation either. Literary translators sometimes input segments of their source text into AI-based technologies like Google Translate and DeepL to generate ideas for particularly thorny passages. But these tools have to be used “very carefully,” warns Seattle-based Finnish-to-English translator Lola Rogers, “because the translations it produces are error-ridden and devoid of flow or beauty.” Edward Tian, a cofounder of AI-detecting start-up GPTZero, adds that current LLMs not only do “a mediocre job at translations,” but also reflect the “majority white, English-dominated” nature of their source texts. Reiterating such worldviews and their biases runs contrary to the aim of much literary translation: to expose audiences to new perspectives. And Rogers, who was recently commissioned to use a translation tool to expedite a months-long translation process to five or six weeks, says that from her brief experiments, the time saved with machine assistance was “minimal.” French-to-English translator Louise Rogers Lalaurie shared a similarly underwhelmed account of editing poor machine-led translations.

So what’s the threat?

One area where translators are feeling the pinch is in creating samples, book excerpts translated to give general impressions of a text to potential publishers. Some publishers have been considering using AI to do this work instead. Though she is unsure whether this is because samples are being automated, Rogers says, “The number of samples I’m asked to translate has fallen precipitously in the past couple of years, making it much harder to earn a living.” A 2024 survey of Society of Authors members found that over a third of translators have lost work due to generative AI. Close to half of translators surveyed said that income from their work has decreased.

To illustrate how AI might ease the time and cost pressures inherent to translation from a publisher’s perspective, Ilan Stavans, the publisher and cofounder of Restless Books, an independent press in Amherst, Massachusetts, gives the example of a recently acquired eight-hundred-page book. To translate it, “substantial investment” would be necessary: Not only are “first-rate translators” for the source language scarce, he says, but the project would also require at least two years of dedicated work. By the time the book is translated and published, the demand the publisher once saw for the title might easily have changed. Meanwhile, the publisher would have incurred a cost much greater than if it had used LLMs, the most expensive of which—such as the premium version of ChatGPT, which costs $200 a month—is a tenth of the average cost of publishing a translation.

“It would be fast and easy,” Stavans admits, “but it would not be the right move.” Though Stavans is enthusiastic about AI’s potential and sees the value of using AI to translate samples, he emphasizes that he would never condone translating an entire book using a machine. The key to the heart of translation is “that intimate, subjective relationship between a text and the translator,” he says—the nebulous yet nonetheless living connection that translator Kate Briggs describes as the “uniquely relational, lived-out practice” of “this little art.”

Will Evans, founder of independent publisher Deep Vellum in Dallas, does not see a future in which machine-led translations supersede the human. “I do not believe AI-led translation will be competitive for works of the literary caliber we are interested in any time before the AI bubble bursts,” he says, “though I have no doubts the corporate publishers who are interested in serving the same books to the same readers over and over again will have no such qualms.”

In the realm of literature, there is still a sanctity around “the human and the humane,” as Stavans puts it. “Machines can’t read a book or experience any of the personal connections to language that give a book life,” adds Rogers, who became a translator after translating Finnish song lyrics for friends. “Machines don’t find themselves unexpectedly chuckling at a phrase, or repeating a string of words because its sounds are satisfying, or remembering being in a place like the place described in a book.” Though a cliché, it nevertheless rings true: The destination might pale in comparison to the joy of the journey, something a machine might never know.

Jimin Kang is a Seoul-born, Hong Kong–raised, and England-based journalist and writer. Her work has appeared in the New York Times, the Nation, the Kenyon Review and the Los Angeles Review of Books, among other publications.

#article #poets and writers magazine #AI #technology #translation #publishing #books #literature #workers #labor vs capital

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script-a-world · 9 months ago

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Submitted via Google Form:

Can rare/endangered languages lack new vocabulary? As in, as society has new technology and invents new words and slang, only the more wider used languages have those new words. The less used languages have moved toward home langauge only rather than at schools or in the wider community and certainly not international so they completely lack in such vocabulary because it's never progressed that far. Does that make sense?

Tex: Short answer: No.

Longer answer: New words are always added to a language every generation, which is how a language survives. When this happens, in combination with fewer native speakers, a language may either die off in isolation or become assimilated into a more popular language. It’s crucial that any new words are not simply taken from another language, because that’s how a language is often stifled and subsumed.

To take an example of well-known languages, English is often mined for new words, particularly for technology. In French, the word for computer is not some adaptation of “computer”, but rather the word is ordinateur (Larousse), which comes from the Latin ordinator (Larousse). Now, Latin used to be a lingua franca throughout most of Europe, and because of that there are a lot of words carried over without the extinction of the languages that adopted new words (more or less). English is now a lingua franca, to the same degree of exposure and adoption.

Utuabzu: As Tex said, short answer, no. One of the basic characteristics of natural languages is that they are infinite, that is to say that every natural language is capable of conveying any concept or idea. If a community does not need to discuss something often, their language might need to use a rather roundabout way to do so, but it can be done. If a concept does need to be discussed frequently, then the community will either create a word for it or borrow one from another language. If a concept no longer needs to be discussed frequently, then the word might be repurposed to mean something related or be dropped altogether. This happens all the time, constantly, in every living language. Smaller, more isolated communities tend to experience this more slowly than larger, more interconnected communities, simply because new concepts are introduced to the former more slowly and rarely than to the latter.

English spent the 16th-20th centuries borrowing and coining a huge number of words related to geography, plants and animals, foods and products, because the expansion of the British Empire (and the US), the development of global trade and the industrial revolution brought English speakers into contact with a vast array of new concepts that had never previously needed to be discussed in English. England, being cold and damp, didn’t really require words like ‘jungle’ (borrowed from Hindi) or ‘canyon’ (borrowed from Spanish), nor did a late medieval English speaker need to talk about a ‘bicycle’ or ‘smog’.

The same processes happen in every language, no matter how much some people (Académie Française) try to stop them. Language is ultimately a tool used by a community, and the community will alter it to suit its needs.

The phenomenon you’re describing where different languages are used in different areas of life (called domains*) is called polyglossia (or in older works/works dealing with only two languages/dialects, diglossia), and it’s pretty common. Outside of monolingual speakers of standard national languages (Anglophones tend to be the worst for this) most people in the world experience some degree of polyglossia - usually using their local language or dialect with family/friends and in casual social settings and the standard national language in formal settings - though the degree does vary.

Some polyglossic environments have up to 5 distinct languages in use by any given individual - the example I recall from my sociolinguistics textbook being a sixteen year old named Kalala, from Bukavu in eastern Congo(Democratic Republic of), who spoke an informal variety of Shi at home and with family, and with market vendors of his ethnic group, a formal variety of Shi at weddings and funerals, a kiSwahili dialect called Kingwana with people from other ethnic groups in informal situations, Standard Congolese kiSwahili in formal and workplace situations and with figures of authority, and a youth-coded dialect that draws on languages like French and English called Indoubil with his friends.**

*Important to note here that a domain is both a physical space, eg. the Home, School, Courtroom, and a conceptual space, eg. Family, Work, Business, Politics, Religion. There’s often overlap between these, but polyglossic communities do tend to arrive at a rough unspoken consensus on what language goes with what domain. Most community members would just say that using the wrong language for a domain would feel weird.

**note that this example is pretty old. So old that it still calls the country Zaire. The reference is in Holmes, J., 1992, An Introduction to Sociolinguistics, pp 21-22.

Blue: The USSR presents an interesting case study when it comes to rare languages. It started with Lenin and policies aimed to develop regional languages, down to creating whole writing systems for those that did not have one. Russian was de facto lingua franca and functioned as an official language, but de jure, it was not. The goal of this policy wasn’t just to support literacy and education for different ethnicities; it created, via translations, a common cultural background and was aimed to spread Marxist ideology. If you want people to understand you and accept you, you need to speak their own language.

After these policies shifted, the regional languages didn’t die; they’re still taught in schools and are in use. And one of the important aspects of a language being in use – it grows and develops: as our reality changes, languages have to adapt to it, otherwise they die. And even if there is a “hegemonic” lingua franca that is more used across the board, the government might still be motivated to develop endangered languages, to facilitate the blending of the cultures and to solidify new ideas.

#google submitted #language

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pranaywahi · 3 days ago

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From Keywords to Conversations: How Search Has Evolved

Fast forward to 2025, and search is no longer about keyword matching. It’s about understanding human conversations, context, and intent. Google doesn’t just crawl web pages anymore ; it thinks, it interprets, and it even responds. What we’re seeing is the shift from keyword based SEO to conversation driven search.

The Keyword Era: When Simplicity Was Enough

Back in the 2000s and early 2010s, SEO was largely reliable . If you wanted to rank for “best pizza in Delhi,” you just needed to include that phrase , in your title, your heading, and your body content — a few too many times. The system worked because search engines weren’t smart enough to question the user’s true intent. They only saw the literal text.

But the problem with keyword stuffing and mechanical optimization was that it never served the user. It served the algorithm. People landed on pages that didn’t quite answer their questions, didn’t speak their language, and didn’t understand what they really meant.

From Phrases to Intent: The Rise of Smarter Search

As AI became more integrated into search engines, the game changed. Google’s updates ; from Multitask Unified Mode and now SGE (Search Generative Experience) — have all been steps toward one goal: understanding what users are trying to say, not just what they’re typing.

That’s why, in 2025, your content needs to think like your audience. Instead of matching keywords, you need to mirror conversations. Your blogs, product pages, FAQs , all of them should sound like they’re part of a helpful chat. Because that’s how AI is processing them.

Platforms like SeoBix have quietly adapted to this shift. Rather than offering outdated keyword tools, they provide deep insights into how people actually phrase questions, how search engines interpret them, and how to build content that fits naturally into those evolving patterns.

Voice Search and AI Assistants Changed the Tone

Another major catalyst in this shift has been the rise of voice search and AI-driven virtual assistants.

Search engines had to evolve, and so did SEO strategies. Now, content that ranks is the content that converses. It reads naturally, anticipates follow-up questions, and creates a seamless flow from one idea to the next.

With SeoBix, creators don’t need to guess what that flow should be. The platform analyzes conversation trends, user behavior, and intent-based search journeys to help you craft content that’s not just findable, but meaningful.

AI Overviews and Zero-Click Results: New Rules, New Reality

In today’s search results, users often get what they need before they click. AI Overviews, answer boxes, and featured snippets now dominate the top of the page. That means your content doesn’t just need to rank — it needs to be concise, direct, and instantly valuable.

To show up in these spots, you have to structure your content like an expert yet make it feel like a casual explanation. That’s not always easy, especially when you’re dealing with complex topics.

This is where platforms like SeoBix prove their worth. They help structure your messaging for AI clarity without losing your brand’s voice or readability.

Search Today Is a Dialogue, Not a Directory

Search is no longer a static query that pulls up a list of links. It’s a dynamic dialogue , a back-and-forth between human curiosity and machine understanding. And the businesses that thrive in this environment are the ones that don’t just talk at users. They listen. They respond. They adapt.

SEO in 2025 isn’t dead. It’s just smarter, more human, and deeply integrated with the ways people speak, not just how they search. And if you’re using tools built for the old web, you’ll miss out on the new one.

Conclusion

If you want your brand to stay relevant, your content must go beyond keywords. It must feel like it’s part of the conversation already happening in the user’s mind.

With platforms like SeoBix helping you bridge the gap between AI understanding and human intention, you’re not just optimizing for search engines , you’re creating content that genuinely connects.

Because in the end, great SEO isn’t about chasing algorithms. It’s about joining the conversation.

#keywordresearch #keywordranking #marketingstrategy #keywordresearchtool #seo #ai overview #Keywordbasedseo

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soumenblogers · 7 days ago

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Free AI Blog Writer 2025: Top Tools to Write Blogs Effortlessly

Introduction: Blogging Just Got a Whole Lot Easier with AI

Let’s be real—writing blog posts regularly is hard work. You need ideas, time, and focus, all working together. But what if a Free AI Blog Writer could do the heavy lifting for you? These smart tools are changing the game by helping people write faster, better, and with less effort. They can take a simple topic and turn it into a polished, SEO-ready article in just a few minutes. Sounds like a dream, right?

As we move through 2025, AI blog writing tools have become more powerful and accessible than ever. The best part? Many of them are completely free to use. You don’t need to sign up for expensive subscriptions or be a tech expert to get started. Whether you’re a beginner, a solo blogger, a student, or a busy entrepreneur—these tools can make your life much easier.

Not only do they save time, but they also help you write content that ranks well on Google. Plus, they often come with built-in SEO tips, keyword suggestions, and formatting help. So, instead of staring at a blank screen for hours, you can have a complete blog ready to publish in just a few clicks.

In this guide, we’ll explain exactly what a Free AI Blog Writer is, why you should use one, and which tools are worth trying in 2025. We’ll also walk you through how to use them step-by-step.

Ready to write smarter, not harder? Let’s get started!

AI Thumbnail Generator for YouTube

What is an AI Blog Writer?

An AI Blog Writer is a smart tool that uses artificial intelligence to help you write blog posts quickly and easily. Instead of spending hours trying to come up with ideas, structure your content, and edit every sentence, you can let the AI handle most of the work. All you need to do is enter a topic or a few keywords, and within seconds, the tool generates well-structured, readable content.

But that’s not all. Many of these tools are trained on tons of data, so they understand how to write in a way that sounds natural and engaging. In fact, some AI blog writers are so good, it’s hard to tell the difference between a post written by a person and one created by a machine.

Even better, most AI blog writers today come with built-in SEO features. They suggest keywords, improve readability, and help you create content that can rank higher on Google. This means you’re not just writing faster—you’re also writing smarter.

Whether you’re a beginner or a busy marketer, using an AI blog writer can save you time, boost productivity, and take the stress out of blogging. And with so many free AI blog writer tools out there, it’s never been easier to get started!

🟢 Key Features:

Blog title and outline generation

Paragraph expansion

Tone customization (formal, friendly, etc.)

Keyword inclusion for SEO

Multi-language support

So, whether you’re a professional blogger or just starting out, AI blog writers can save you time and effort while keeping quality intact.

💡 Why Use a Free AI Blog Writer?

You might wonder, “If there are paid tools, why should I go for free AI blog writers?” Well, there are several compelling reasons you should consider:

Cost-Effective for Beginners Starting a blog doesn’t have to break the bank. Free AI blog writers help new creators publish content without investing upfront. Moreover, you get to test the power of AI before upgrading to a paid plan. Therefore, it’s a smart starting point for anyone.

Fast Content Generation With just a few keywords, you can get a blog post draft in under 5 minutes. That’s a game-changer for people with tight deadlines. Consequently, it makes the process more efficient and manageable.

Improves Productivity Even seasoned writers use AI tools to beat writer’s block and speed up their workflow. In fact, free tools offer just enough features to boost your daily writing tasks. Thus, productivity increases significantly.

SEO Optimization Most AI writers help you include relevant keywords naturally, structure your content well, and even write meta descriptions. As a result, your content performs better in search engines and attracts more readers.

Easy to Use Free AI tools are usually more simplified and beginner-friendly than their premium versions. This makes it easier to learn the ropes and start writing right away. Hence, anyone can start blogging confidently.

In short, a free AI blog writer gives you the power of automation without the price tag—perfect for casual bloggers, students, marketers, and small businesses who want to scale up.

Top 5 Free AI Blog Writers in 2025

Let’s explore the best free AI blog writing tools in 2025 that offer real value without costing a dime.

1. Write Cream

If you’re looking for a beginner-friendly Free AI Blog Writer, Write Cream is a great place to start. It’s designed to help users create high-quality content in minutes, without needing any writing experience. Whether you’re writing blog posts, product descriptions, or even cold emails, Write Cream has tools for all of it.

One thing that sets Write Cream apart is how easy it is to use. Just type in your topic or a few keywords, and the AI generates a well-structured article almost instantly. Plus, it offers features like voiceovers and social media captions, which is a big bonus for content creators.

Another great advantage? You don’t have to pay anything to get started. The free version gives you access to most of its features, making it perfect for bloggers on a budget. Overall, Write Cream is a solid choice for fast, reliable content creation.

Features:

Full blog generation

Email copy and voiceovers

Tone customization

Free credits every month

Best For: Beginners who want high-quality blog drafts in minutes.

2. Copy.ai

When it comes to creativity and variety, Copy.ai is hard to beat. This Free AI Blog Writer offers a wide range of writing tools that help you brainstorm blog ideas, write introductions, and build paragraphs with ease. It’s especially useful if you’re someone who values flexibility in tone.

What truly makes Copy.ai shine is its ability to adapt to your writing style. Whether you want your blog post to sound professional, friendly, witty, or even formal, this tool has you covered. Just choose your tone, enter a topic, and let the AI do the heavy lifting.

Additionally, the free version includes plenty of templates—perfect for short-form content, product descriptions, or even quick blog posts. So, if you're looking for an AI that’s fast, smart, and creative, Copy.ai is definitely worth trying. It’s a simple way to write more and stress less.

Features:

Fast content generation

Multiple templates (blogs, emails, ads)

Friendly UI

Great for brainstorming

Best For: Quick blog drafts, headlines, and idea generation.

3. Rytr

If you're looking for a Free AI Blog Writer that’s both easy to use and budget-friendly, Rytr is a solid choice. It’s especially helpful for those who want to write consistently without breaking the bank. Rytr supports over 30 languages, making it a flexible tool for global content creators.

One of Rytr’s standout features is its tone and format customization. Whether you want a casual blog post or a formal product review, you can easily adjust the tone to suit your audience. Plus, the user-friendly interface makes content creation feel smooth and effortless.

Moreover, Rytr’s free plan offers enough characters to write two to three full blog posts every month. So, if you're a beginner or just need occasional help, this tool delivers real value without any cost. In short, Rytr is a reliable assistant for structured blog writing and everyday content tasks.

Features:

30+ language support

SEO-friendly content

Tone selection

Easy formatting options

Best For: Beginners and multilingual writers who need clear structure.

4. Writesonic

When it comes to SEO-focused content creation, Writesonic is a real game-changer. This Free AI Blog Writer gives you up to 10,000 words per month on the free plan—more than enough to craft multiple long-form blog posts. That’s a huge plus for content creators who want high-volume output without spending a dime.

What sets Writesonic apart is its ability to turn a single keyword into a full article. You can generate everything from blog intros and outlines to complete drafts in just a few clicks. In other words, it takes your ideas and turns them into SEO-friendly content, fast.

Not only that, but the writing quality is impressive. Many digital marketers rely on Writesonic for its consistent tone and search engine–optimized results. So, if you're aiming for visibility and quality, this tool makes your content creation journey a whole lot easier.

Features:

SEO content generation

Ad copy, product descriptions, and more

Advanced GPT-based model

Multilingual options

Best For: Bloggers and marketers who focus on SEO.

5. Simplified

Simplified is more than just a Free AI Blog Writer—it's a full-fledged content creation suite designed for multitaskers. With Simplified, you can write blogs, design social media posts, and even edit videos—all in one place. This makes it an excellent choice for content creators, digital marketers, and small business owners who juggle multiple platforms.

What sets Simplified apart is its all-in-one workspace. You don’t need to switch between different tools anymore. Everything from writing long-form articles to creating eye-catching visuals can be done here.

Even though the free plan comes with a word limit, it still unlocks access to various helpful AI templates. These are perfect for generating ideas, writing introductions, or completing blog outlines quickly. So, if you're looking for a tool that does more than just writing, Simplified might just be the creative partner you’ve been searching for.

Features:

Blog, email, and ad writing

Graphic design tools included

Supports collaboration

Video & image integration

Best For: Bloggers who also design content for social media.

How to Use a Free AI Blog Writer (Step-by-Step)

If you’re new to AI blog tools, don’t worry! Here’s a simple guide to help you get started quickly and confidently:

🔹 Step 1: Choose the Right Tool Select a free AI writer based on your needs. For example:

For full blogs → WriteCream, Writesonic

For quick ideas → Copy.ai

For visual blogs → Simplified

🔹 Step 2: Create a Free Account Sign up using your email or Google account. Most platforms offer limited credits/month for free users. Hence, account creation is quick and hassle-free.

🔹 Step 3: Select a Template or Tool Pick a blog writing template like “Blog Outline,” “Blog Intro,” or “Full Article.” These are often listed under the “Blog” or “Writing” section. Consequently, you can begin in seconds and save time.

🔹 Step 4: Enter Your Topic or Keywords Add your blog topic or main keyword. Example: “How to start a dropshipping business.” Some tools allow tone and audience selection too. As a result, the output matches your intent more accurately.

🔹 Step 5: Generate Content Click the “Generate” or “Write” button. Wait for a few seconds. The AI will produce the content in sections. Then, you can review or regenerate as needed. Therefore, revisions are easy.

🔹 Step 6: Edit and Polish Always review the content. Add personal insights, fix grammar, and make it more authentic if needed. That way, your blog stays unique and engaging while reflecting your voice.

🔹 Step 7: Publish Once you're satisfied, copy the content to your blog platform (like WordPress, Blogger, or Medium) and hit publish! Therefore, your blog goes live in no time.

Final Thoughts: Is a Free AI Blog Writer Worth It?

If you’re still wondering whether a Free AI Blog Writer is worth trying, the answer is a big yes—especially if you’re a beginner, busy entrepreneur, student, or solo content creator. These tools are not just about saving time; they help you break through writer’s block, organize your thoughts, and produce quality blog posts faster than ever.

In fact, with just a few clicks, you can go from a rough idea to a complete blog draft. Plus, many of these tools offer templates, tone customization, and keyword suggestions, which make your writing sound natural and SEO-friendly. As a result, even non-writers can publish professional-sounding articles without hiring a content team.

Of course, a free plan may come with limitations—like word count caps or reduced feature access. But even so, most free AI blog writers are powerful enough to help you complete two to three solid blog posts each month, depending on your needs. And that’s a great start.

Moreover, using AI doesn’t mean giving up your voice. Instead, think of it as a creative assistant—someone who offers structure, suggestions, and inspiration. You still remain the editor-in-chief, with full control over the final output.

So, whether you’re blogging for business, passion, or personal branding, a Free AI Blog Writer can make your content journey smoother, faster, and less stressful. Try a few tools, test what works best for you, and start building your online presence today—with less effort and more confidence.

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aiseoexperteurope · 23 days ago

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WHAT IS VERTEX AI SEARCH

Vertex AI Search: A Comprehensive Analysis

1. Executive Summary

Vertex AI Search emerges as a pivotal component of Google Cloud's artificial intelligence portfolio, offering enterprises the capability to deploy search experiences with the quality and sophistication characteristic of Google's own search technologies. This service is fundamentally designed to handle diverse data types, both structured and unstructured, and is increasingly distinguished by its deep integration with generative AI, most notably through its out-of-the-box Retrieval Augmented Generation (RAG) functionalities. This RAG capability is central to its value proposition, enabling organizations to ground large language model (LLM) responses in their proprietary data, thereby enhancing accuracy, reliability, and contextual relevance while mitigating the risk of generating factually incorrect information.

The platform's strengths are manifold, stemming from Google's decades of expertise in semantic search and natural language processing. Vertex AI Search simplifies the traditionally complex workflows associated with building RAG systems, including data ingestion, processing, embedding, and indexing. It offers specialized solutions tailored for key industries such as retail, media, and healthcare, addressing their unique vernacular and operational needs. Furthermore, its integration within the broader Vertex AI ecosystem, including access to advanced models like Gemini, positions it as a comprehensive solution for building sophisticated AI-driven applications.

However, the adoption of Vertex AI Search is not without its considerations. The pricing model, while granular and offering a "pay-as-you-go" approach, can be complex, necessitating careful cost modeling, particularly for features like generative AI and always-on components such as Vector Search index serving. User experiences and technical documentation also point to potential implementation hurdles for highly specific or advanced use cases, including complexities in IAM permission management and evolving query behaviors with platform updates. The rapid pace of innovation, while a strength, also requires organizations to remain adaptable.

Ultimately, Vertex AI Search represents a strategic asset for organizations aiming to unlock the value of their enterprise data through advanced search and AI. It provides a pathway to not only enhance information retrieval but also to build a new generation of AI-powered applications that are deeply informed by and integrated with an organization's unique knowledge base. Its continued evolution suggests a trajectory towards becoming a core reasoning engine for enterprise AI, extending beyond search to power more autonomous and intelligent systems.

2. Introduction to Vertex AI Search

Vertex AI Search is establishing itself as a significant offering within Google Cloud's AI capabilities, designed to transform how enterprises access and utilize their information. Its strategic placement within the Google Cloud ecosystem and its core value proposition address critical needs in the evolving landscape of enterprise data management and artificial intelligence.

Defining Vertex AI Search

Vertex AI Search is a service integrated into Google Cloud's Vertex AI Agent Builder. Its primary function is to equip developers with the tools to create secure, high-quality search experiences comparable to Google's own, tailored for a wide array of applications. These applications span public-facing websites, internal corporate intranets, and, significantly, serve as the foundation for Retrieval Augmented Generation (RAG) systems that power generative AI agents and applications. The service achieves this by amalgamating deep information retrieval techniques, advanced natural language processing (NLP), and the latest innovations in large language model (LLM) processing. This combination allows Vertex AI Search to more accurately understand user intent and deliver the most pertinent results, marking a departure from traditional keyword-based search towards more sophisticated semantic and conversational search paradigms.

Strategic Position within Google Cloud AI Ecosystem

The service is not a standalone product but a core element of Vertex AI, Google Cloud's comprehensive and unified machine learning platform. This integration is crucial, as Vertex AI Search leverages and interoperates with other Vertex AI tools and services. Notable among these are Document AI, which facilitates the processing and understanding of diverse document formats , and direct access to Google's powerful foundation models, including the multimodal Gemini family. Its incorporation within the Vertex AI Agent Builder further underscores Google's strategy to provide an end-to-end toolkit for constructing advanced AI agents and applications, where robust search and retrieval capabilities are fundamental.

Core Purpose and Value Proposition

The fundamental aim of Vertex AI Search is to empower enterprises to construct search applications of Google's caliber, operating over their own controlled datasets, which can encompass both structured and unstructured information. A central pillar of its value proposition is its capacity to function as an "out-of-the-box" RAG system. This feature is critical for grounding LLM responses in an enterprise's specific data, a process that significantly improves the accuracy, reliability, and contextual relevance of AI-generated content, thereby reducing the propensity for LLMs to produce "hallucinations" or factually incorrect statements. The simplification of the intricate workflows typically associated with RAG systems—including Extract, Transform, Load (ETL) processes, Optical Character Recognition (OCR), data chunking, embedding generation, and indexing—is a major attraction for businesses.

Moreover, Vertex AI Search extends its utility through specialized, pre-tuned offerings designed for specific industries such as retail (Vertex AI Search for Commerce), media and entertainment (Vertex AI Search for Media), and healthcare and life sciences. These tailored solutions are engineered to address the unique terminologies, data structures, and operational requirements prevalent in these sectors.

The pronounced emphasis on "out-of-the-box RAG" and the simplification of data processing pipelines points towards a deliberate strategy by Google to lower the entry barrier for enterprises seeking to leverage advanced Generative AI capabilities. Many organizations may lack the specialized AI talent or resources to build such systems from the ground up. Vertex AI Search offers a managed, pre-configured solution, effectively democratizing access to sophisticated RAG technology. By making these capabilities more accessible, Google is not merely selling a search product; it is positioning Vertex AI Search as a foundational layer for a new wave of enterprise AI applications. This approach encourages broader adoption of Generative AI within businesses by mitigating some inherent risks, like LLM hallucinations, and reducing technical complexities. This, in turn, is likely to drive increased consumption of other Google Cloud services, such as storage, compute, and LLM APIs, fostering a more integrated and potentially "sticky" ecosystem.

Furthermore, Vertex AI Search serves as a conduit between traditional enterprise search mechanisms and the frontier of advanced AI. It is built upon "Google's deep expertise and decades of experience in semantic search technologies" , while concurrently incorporating "the latest in large language model (LLM) processing" and "Gemini generative AI". This dual nature allows it to support conventional search use cases, such as website and intranet search , alongside cutting-edge AI applications like RAG for generative AI agents and conversational AI systems. This design provides an evolutionary pathway for enterprises. Organizations can commence by enhancing existing search functionalities and then progressively adopt more advanced AI features as their internal AI maturity and comfort levels grow. This adaptability makes Vertex AI Search an attractive proposition for a diverse range of customers with varying immediate needs and long-term AI ambitions. Such an approach enables Google to capture market share in both the established enterprise search market and the rapidly expanding generative AI application platform market. It offers a smoother transition for businesses, diminishing the perceived risk of adopting state-of-the-art AI by building upon familiar search paradigms, thereby future-proofing their investment.

3. Core Capabilities and Architecture

Vertex AI Search is engineered with a rich set of features and a flexible architecture designed to handle diverse enterprise data and power sophisticated search and AI applications. Its capabilities span from foundational search quality to advanced generative AI enablement, supported by robust data handling mechanisms and extensive customization options.

Key Features

Vertex AI Search integrates several core functionalities that define its power and versatility:

Google-Quality Search: At its heart, the service leverages Google's profound experience in semantic search technologies. This foundation aims to deliver highly relevant search results across a wide array of content types, moving beyond simple keyword matching to incorporate advanced natural language understanding (NLU) and contextual awareness.

Out-of-the-Box Retrieval Augmented Generation (RAG): A cornerstone feature is its ability to simplify the traditionally complex RAG pipeline. Processes such as ETL, OCR, document chunking, embedding generation, indexing, storage, information retrieval, and summarization are streamlined, often requiring just a few clicks to configure. This capability is paramount for grounding LLM responses in enterprise-specific data, which significantly enhances the trustworthiness and accuracy of generative AI applications.

Document Understanding: The service benefits from integration with Google's Document AI suite, enabling sophisticated processing of both structured and unstructured documents. This allows for the conversion of raw documents into actionable data, including capabilities like layout parsing and entity extraction.

Vector Search: Vertex AI Search incorporates powerful vector search technology, essential for modern embeddings-based applications. While it offers out-of-the-box embedding generation and automatic fine-tuning, it also provides flexibility for advanced users. They can utilize custom embeddings and gain direct control over the underlying vector database for specialized use cases such as recommendation engines and ad serving. Recent enhancements include the ability to create and deploy indexes without writing code, and a significant reduction in indexing latency for smaller datasets, from hours down to minutes. However, it's important to note user feedback regarding Vector Search, which has highlighted concerns about operational costs (e.g., the need to keep compute resources active even when not querying), limitations with certain file types (e.g., .xlsx), and constraints on embedding dimensions for specific corpus configurations. This suggests a balance to be struck between the power of Vector Search and its operational overhead and flexibility.

Generative AI Features: The platform is designed to enable grounded answers by synthesizing information from multiple sources. It also supports the development of conversational AI capabilities , often powered by advanced models like Google's Gemini.

Comprehensive APIs: For developers who require fine-grained control or are building bespoke RAG solutions, Vertex AI Search exposes a suite of APIs. These include APIs for the Document AI Layout Parser, ranking algorithms, grounded generation, and the check grounding API, which verifies the factual basis of generated text.

Data Handling

Effective data management is crucial for any search system. Vertex AI Search provides several mechanisms for ingesting, storing, and organizing data:

Supported Data Sources:

Websites: Content can be indexed by simply providing site URLs.

Structured Data: The platform supports data from BigQuery tables and NDJSON files, enabling hybrid search (a combination of keyword and semantic search) or recommendation systems. Common examples include product catalogs, movie databases, or professional directories.

Unstructured Data: Documents in various formats (PDF, DOCX, etc.) and images can be ingested for hybrid search. Use cases include searching through private repositories of research publications or financial reports. Notably, some limitations, such as lack of support for .xlsx files, have been reported specifically for Vector Search.

Healthcare Data: FHIR R4 formatted data, often imported from the Cloud Healthcare API, can be used to enable hybrid search over clinical data and patient records.

Media Data: A specialized structured data schema is available for the media industry, catering to content like videos, news articles, music tracks, and podcasts.

Third-party Data Sources: Vertex AI Search offers connectors (some in Preview) to synchronize data from various third-party applications, such as Jira, Confluence, and Salesforce, ensuring that search results reflect the latest information from these systems.

Data Stores and Apps: A fundamental architectural concept in Vertex AI Search is the one-to-one relationship between an "app" (which can be a search or a recommendations app) and a "data store". Data is imported into a specific data store, where it is subsequently indexed. The platform provides different types of data stores, each optimized for a particular kind of data (e.g., website content, structured data, unstructured documents, healthcare records, media assets).

Indexing and Corpus: The term "corpus" refers to the underlying storage and indexing mechanism within Vertex AI Search. Even when users interact with data stores, which act as an abstraction layer, the corpus is the foundational component where data is stored and processed. It is important to understand that costs are associated with the corpus, primarily driven by the volume of indexed data, the amount of storage consumed, and the number of queries processed.

Schema Definition: Users have the ability to define a schema that specifies which metadata fields from their documents should be indexed. This schema also helps in understanding the structure of the indexed documents.

Real-time Ingestion: For datasets that change frequently, Vertex AI Search supports real-time ingestion. This can be implemented using a Pub/Sub topic to publish notifications about new or updated documents. A Cloud Function can then subscribe to this topic and use the Vertex AI Search API to ingest, update, or delete documents in the corresponding data store, thereby maintaining data freshness. This is a critical feature for dynamic environments.

Automated Processing for RAG: When used for Retrieval Augmented Generation, Vertex AI Search automates many of the complex data processing steps, including ETL, OCR, document chunking, embedding generation, and indexing.

The "corpus" serves as the foundational layer for both storage and indexing, and its management has direct cost implications. While data stores provide a user-friendly abstraction, the actual costs are tied to the size of this underlying corpus and the activity it handles. This means that effective data management strategies, such as determining what data to index and defining retention policies, are crucial for optimizing costs, even with the simplified interface of data stores. The "pay only for what you use" principle is directly linked to the activity and volume within this corpus. For large-scale deployments, particularly those involving substantial datasets like the 500GB use case mentioned by a user , the cost implications of the corpus can be a significant planning factor.

There is an observable interplay between the platform's "out-of-the-box" simplicity and the requirements of advanced customization. Vertex AI Search is heavily promoted for its ease of setup and pre-built RAG capabilities , with an emphasis on an "easy experience to get started". However, highly specific enterprise scenarios or complex user requirements—such as querying by unique document identifiers, maintaining multi-year conversational contexts, needing specific embedding dimensions, or handling unsupported file formats like XLSX —may necessitate delving into more intricate configurations, API utilization, and custom development work. For example, implementing real-time ingestion requires setting up Pub/Sub and Cloud Functions , and achieving certain filtering behaviors might involve workarounds like using metadata fields. While comprehensive APIs are available for "granular control or bespoke RAG solutions" , this means that the platform's inherent simplicity has boundaries, and deep technical expertise might still be essential for optimal or highly tailored implementations. This suggests a tiered user base: one that leverages Vertex AI Search as a turnkey solution, and another that uses it as a powerful, extensible toolkit for custom builds.

Querying and Customization

Vertex AI Search provides flexible ways to query data and customize the search experience:

Query Types: The platform supports Google-quality search, which represents an evolution from basic keyword matching to modern, conversational search experiences. It can be configured to return only a list of search results or to provide generative, AI-powered answers. A recent user-reported issue (May 2025) indicated that queries against JSON data in the latest release might require phrasing in natural language, suggesting an evolving query interpretation mechanism that prioritizes NLU.

Customization Options:

Vertex AI Search offers extensive capabilities to tailor search experiences to specific needs.

Metadata Filtering: A key customization feature is the ability to filter search results based on indexed metadata fields. For instance, if direct filtering by rag_file_ids is not supported by a particular API (like the Grounding API), adding a file_id to document metadata and filtering on that field can serve as an effective alternative.

Search Widget: Integration into websites can be achieved easily by embedding a JavaScript widget or an HTML component.

API Integration: For more profound control and custom integrations, the AI Applications API can be used.

LLM Feature Activation: Features that provide generative answers powered by LLMs typically need to be explicitly enabled.

Refinement Options: Users can preview search results and refine them by adding or modifying metadata (e.g., based on HTML structure for websites), boosting the ranking of certain results (e.g., based on publication date), or applying filters (e.g., based on URL patterns or other metadata).

Events-based Reranking and Autocomplete: The platform also supports advanced tuning options such as reranking results based on user interaction events and providing autocomplete suggestions for search queries.

Multi-Turn Conversation Support:

For conversational AI applications, the Grounding API can utilize the history of a conversation as context for generating subsequent responses.

To maintain context in multi-turn dialogues, it is recommended to store previous prompts and responses (e.g., in a database or cache) and include this history in the next prompt to the model, while being mindful of the context window limitations of the underlying LLMs.

The evolving nature of query interpretation, particularly the reported shift towards requiring natural language queries for JSON data , underscores a broader trend. If this change is indicative of a deliberate platform direction, it signals a significant alignment of the query experience with Google's core strengths in NLU and conversational AI, likely driven by models like Gemini. This could simplify interactions for end-users but may require developers accustomed to more structured query languages for structured data to adapt their approaches. Such a shift prioritizes natural language understanding across the platform. However, it could also introduce friction for existing applications or development teams that have built systems based on previous query behaviors. This highlights the dynamic nature of managed services, where underlying changes can impact functionality, necessitating user adaptation and diligent monitoring of release notes.

4. Applications and Use Cases

Vertex AI Search is designed to cater to a wide spectrum of applications, from enhancing traditional enterprise search to enabling sophisticated generative AI solutions across various industries. Its versatility allows organizations to leverage their data in novel and impactful ways.

Enterprise Search

A primary application of Vertex AI Search is the modernization and improvement of search functionalities within an organization:

Improving Search for Websites and Intranets: The platform empowers businesses to deploy Google-quality search capabilities on their external-facing websites and internal corporate portals or intranets. This can significantly enhance user experience by making information more discoverable. For basic implementations, this can be as straightforward as integrating a pre-built search widget.

Employee and Customer Search: Vertex AI Search provides a comprehensive toolkit for accessing, processing, and analyzing enterprise information. This can be used to create powerful search experiences for employees, helping them find internal documents, locate subject matter experts, or access company knowledge bases more efficiently. Similarly, it can improve customer-facing search for product discovery, support documentation, or FAQs.

Generative AI Enablement

Vertex AI Search plays a crucial role in the burgeoning field of generative AI by providing essential grounding capabilities:

Grounding LLM Responses (RAG): A key and frequently highlighted use case is its function as an out-of-the-box Retrieval Augmented Generation (RAG) system. In this capacity, Vertex AI Search retrieves relevant and factual information from an organization's own data repositories. This retrieved information is then used to "ground" the responses generated by Large Language Models (LLMs). This process is vital for improving the accuracy, reliability, and contextual relevance of LLM outputs, and critically, for reducing the incidence of "hallucinations"—the tendency of LLMs to generate plausible but incorrect or fabricated information.

Powering Generative AI Agents and Apps: By providing robust grounding capabilities, Vertex AI Search serves as a foundational component for building sophisticated generative AI agents and applications. These AI systems can then interact with and reason about company-specific data, leading to more intelligent and context-aware automated solutions.

Industry-Specific Solutions

Recognizing that different industries have unique data types, terminologies, and objectives, Google Cloud offers specialized versions of Vertex AI Search:

Vertex AI Search for Commerce (Retail): This version is specifically tuned to enhance the search, product recommendation, and browsing experiences on retail e-commerce channels. It employs AI to understand complex customer queries, interpret shopper intent (even when expressed using informal language or colloquialisms), and automatically provide dynamic spell correction and relevant synonym suggestions. Furthermore, it can optimize search results based on specific business objectives, such as click-through rates (CTR), revenue per session, and conversion rates.

Vertex AI Search for Media (Media and Entertainment): Tailored for the media industry, this solution aims to deliver more personalized content recommendations, often powered by generative AI. The strategic goal is to increase consumer engagement and time spent on media platforms, which can translate to higher advertising revenue, subscription retention, and overall platform loyalty. It supports structured data formats commonly used in the media sector for assets like videos, news articles, music, and podcasts.

Vertex AI Search for Healthcare and Life Sciences: This offering provides a medically tuned search engine designed to improve the experiences of both patients and healthcare providers. It can be used, for example, to search through vast clinical data repositories, electronic health records, or a patient's clinical history using exploratory queries. This solution is also built with compliance with healthcare data regulations like HIPAA in mind.

The development of these industry-specific versions like "Vertex AI Search for Commerce," "Vertex AI Search for Media," and "Vertex AI Search for Healthcare and Life Sciences" is not merely a cosmetic adaptation. It represents a strategic decision by Google to avoid a one-size-fits-all approach. These offerings are "tuned for unique industry requirements" , incorporating specialized terminologies, understanding industry-specific data structures, and aligning with distinct business objectives. This targeted approach significantly lowers the barrier to adoption for companies within these verticals, as the solution arrives pre-optimized for their particular needs, thereby reducing the requirement for extensive custom development or fine-tuning. This industry-specific strategy serves as a potent market penetration tactic, allowing Google to compete more effectively against niche players in each vertical and to demonstrate clear return on investment by addressing specific, high-value industry challenges. It also fosters deeper integration into the core business processes of these enterprises, positioning Vertex AI Search as a more strategic and less easily substitutable component of their technology infrastructure. This could, over time, lead to the development of distinct, industry-focused data ecosystems and best practices centered around Vertex AI Search.

Embeddings-Based Applications (via Vector Search)

The underlying Vector Search capability within Vertex AI Search also enables a range of applications that rely on semantic similarity of embeddings:

Recommendation Engines: Vector Search can be a core component in building recommendation engines. By generating numerical representations (embeddings) of items (e.g., products, articles, videos), it can find and suggest items that are semantically similar to what a user is currently viewing or has interacted with in the past.

Chatbots: For advanced chatbots that need to understand user intent deeply and retrieve relevant information from extensive knowledge bases, Vector Search provides powerful semantic matching capabilities. This allows chatbots to provide more accurate and contextually appropriate responses.

Ad Serving: In the domain of digital advertising, Vector Search can be employed for semantic matching to deliver more relevant advertisements to users based on content or user profiles.

The Vector Search component is presented both as an integral technology powering the semantic retrieval within the managed Vertex AI Search service and as a potent, standalone tool accessible via the broader Vertex AI platform. Snippet , for instance, outlines a methodology for constructing a recommendation engine using Vector Search directly. This dual role means that Vector Search is foundational to the core semantic retrieval capabilities of Vertex AI Search, and simultaneously, it is a powerful component that can be independently leveraged by developers to build other custom AI applications. Consequently, enhancements to Vector Search, such as the recently reported reductions in indexing latency , benefit not only the out-of-the-box Vertex AI Search experience but also any custom AI solutions that developers might construct using this underlying technology. Google is, in essence, offering a spectrum of access to its vector database technology. Enterprises can consume it indirectly and with ease through the managed Vertex AI Search offering, or they can harness it more directly for bespoke AI projects. This flexibility caters to varying levels of technical expertise and diverse application requirements. As more enterprises adopt embeddings for a multitude of AI tasks, a robust, scalable, and user-friendly Vector Search becomes an increasingly critical piece of infrastructure, likely driving further adoption of the entire Vertex AI ecosystem.

Document Processing and Analysis

Leveraging its integration with Document AI, Vertex AI Search offers significant capabilities in document processing:

The service can help extract valuable information, classify documents based on content, and split large documents into manageable chunks. This transforms static documents into actionable intelligence, which can streamline various business workflows and enable more data-driven decision-making. For example, it can be used for analyzing large volumes of textual data, such as customer feedback, product reviews, or research papers, to extract key themes and insights.

Case Studies (Illustrative Examples)

While specific case studies for "Vertex AI Search" are sometimes intertwined with broader "Vertex AI" successes, several examples illustrate the potential impact of AI grounded on enterprise data, a core principle of Vertex AI Search:

Genial Care (Healthcare): This organization implemented Vertex AI to improve the process of keeping session records for caregivers. This enhancement significantly aided in reviewing progress for autism care, demonstrating Vertex AI's value in managing and utilizing healthcare-related data.

AES (Manufacturing & Industrial): AES utilized generative AI agents, built with Vertex AI, to streamline energy safety audits. This application resulted in a remarkable 99% reduction in costs and a decrease in audit completion time from 14 days to just one hour. This case highlights the transformative potential of AI agents that are effectively grounded on enterprise-specific information, aligning closely with the RAG capabilities central to Vertex AI Search.

Xometry (Manufacturing): This company is reported to be revolutionizing custom manufacturing processes by leveraging Vertex AI.

LUXGEN (Automotive): LUXGEN employed Vertex AI to develop an AI-powered chatbot. This initiative led to improvements in both the car purchasing and driving experiences for customers, while also achieving a 30% reduction in customer service workloads.

These examples, though some may refer to the broader Vertex AI platform, underscore the types of business outcomes achievable when AI is effectively applied to enterprise data and processes—a domain where Vertex AI Search is designed to excel.

5. Implementation and Management Considerations

Successfully deploying and managing Vertex AI Search involves understanding its setup processes, data ingestion mechanisms, security features, and user access controls. These aspects are critical for ensuring the platform operates efficiently, securely, and in alignment with enterprise requirements.

Setup and Deployment

Vertex AI Search offers flexibility in how it can be implemented and integrated into existing systems:

Google Cloud Console vs. API: Implementation can be approached in two main ways. The Google Cloud console provides a web-based interface for a quick-start experience, allowing users to create applications, import data, test search functionality, and view analytics without extensive coding. Alternatively, for deeper integration into websites or custom applications, the AI Applications API offers programmatic control. A common practice is a hybrid approach, where initial setup and data management are performed via the console, while integration and querying are handled through the API.

App and Data Store Creation: The typical workflow begins with creating a search or recommendations "app" and then attaching it to a "data store." Data relevant to the application is then imported into this data store and subsequently indexed to make it searchable.

Embedding JavaScript Widgets: For straightforward website integration, Vertex AI Search provides embeddable JavaScript widgets and API samples. These allow developers to quickly add search or recommendation functionalities to their web pages as HTML components.

Data Ingestion and Management

The platform provides robust mechanisms for ingesting data from various sources and keeping it up-to-date:

Corpus Management: As previously noted, the "corpus" is the fundamental underlying storage and indexing layer. While data stores offer an abstraction, it is crucial to understand that costs are directly related to the volume of data indexed in the corpus, the storage it consumes, and the query load it handles.

Pub/Sub for Real-time Updates: For environments with dynamic datasets where information changes frequently, Vertex AI Search supports real-time updates. This is typically achieved by setting up a Pub/Sub topic to which notifications about new or modified documents are published. A Cloud Function, acting as a subscriber to this topic, can then use the Vertex AI Search API to ingest, update, or delete the corresponding documents in the data store. This architecture ensures that the search index remains fresh and reflects the latest information. The capacity for real-time ingestion via Pub/Sub and Cloud Functions is a significant feature. This capability distinguishes it from systems reliant solely on batch indexing, which may not be adequate for environments with rapidly changing information. Real-time ingestion is vital for use cases where data freshness is paramount, such as e-commerce platforms with frequently updated product inventories, news portals, live financial data feeds, or internal systems tracking real-time operational metrics. Without this, search results could quickly become stale and potentially misleading. This feature substantially broadens the applicability of Vertex AI Search, positioning it as a viable solution for dynamic, operational systems where search must accurately reflect the current state of data. However, implementing this real-time pipeline introduces additional architectural components (Pub/Sub topics, Cloud Functions) and associated costs, which organizations must consider in their planning. It also implies a need for robust monitoring of the ingestion pipeline to ensure its reliability.

Metadata for Filtering and Control: During the schema definition process, specific metadata fields can be designated for indexing. This indexed metadata is critical for enabling powerful filtering of search results. For example, if an application requires users to search within a specific subset of documents identified by a unique ID, and direct filtering by a system-generated rag_file_id is not supported in a particular API context, a workaround involves adding a custom file_id field to each document's metadata. This custom field can then be used as a filter criterion during search queries.

Data Connectors: To facilitate the ingestion of data from a variety of sources, including first-party systems, other Google services, and third-party applications (such as Jira, Confluence, and Salesforce), Vertex AI Search offers data connectors. These connectors provide read-only access to external applications and help ensure that the data within the search index remains current and synchronized with these source systems.

Security and Compliance

Google Cloud places a strong emphasis on security and compliance for its services, and Vertex AI Search incorporates several features to address these enterprise needs:

Data Privacy: A core tenet is that user data ingested into Vertex AI Search is secured within the customer's dedicated cloud instance. Google explicitly states that it does not access or use this customer data for training its general-purpose models or for any other unauthorized purposes.

Industry Compliance: Vertex AI Search is designed to adhere to various recognized industry standards and regulations. These include HIPAA (Health Insurance Portability and Accountability Act) for healthcare data, the ISO 27000-series for information security management, and SOC (System and Organization Controls) attestations (SOC-1, SOC-2, SOC-3). This compliance is particularly relevant for the specialized versions of Vertex AI Search, such as the one for Healthcare and Life Sciences.

Access Transparency: This feature, when enabled, provides customers with logs of actions taken by Google personnel if they access customer systems (typically for support purposes), offering a degree of visibility into such interactions.

Virtual Private Cloud (VPC) Service Controls: To enhance data security and prevent unauthorized data exfiltration or infiltration, customers can use VPC Service Controls to define security perimeters around their Google Cloud resources, including Vertex AI Search.

Customer-Managed Encryption Keys (CMEK): Available in Preview, CMEK allows customers to use their own cryptographic keys (managed through Cloud Key Management Service) to encrypt data at rest within Vertex AI Search. This gives organizations greater control over their data's encryption.

User Access and Permissions (IAM)

Proper configuration of Identity and Access Management (IAM) permissions is fundamental to securing Vertex AI Search and ensuring that users only have access to appropriate data and functionalities:

Effective IAM policies are critical. However, some users have reported encountering challenges when trying to identify and configure the specific "Discovery Engine search permissions" required for Vertex AI Search. Difficulties have been noted in determining factors such as principal access boundaries or the impact of deny policies, even when utilizing tools like the IAM Policy Troubleshooter. This suggests that the permission model can be granular and may require careful attention to detail and potentially specialized knowledge to implement correctly, especially for complex scenarios involving fine-grained access control.

The power of Vertex AI Search lies in its capacity to index and make searchable vast quantities of potentially sensitive enterprise data drawn from diverse sources. While Google Cloud provides a robust suite of security features like VPC Service Controls and CMEK , the responsibility for meticulous IAM configuration and overarching data governance rests heavily with the customer. The user-reported difficulties in navigating IAM permissions for "Discovery Engine search permissions" underscore that the permission model, while offering granular control, might also present complexity. Implementing a least-privilege access model effectively, especially when dealing with nuanced requirements such as filtering search results based on user identity or specific document IDs , may require specialized expertise. Failure to establish and maintain correct IAM policies could inadvertently lead to security vulnerabilities or compliance breaches, thereby undermining the very benefits the search platform aims to provide. Consequently, the "ease of use" often highlighted for search setup must be counterbalanced with rigorous and continuous attention to security and access control from the outset of any deployment. The platform's capability to filter search results based on metadata becomes not just a functional feature but a key security control point if designed and implemented with security considerations in mind.

6. Pricing and Commercials

Understanding the pricing structure of Vertex AI Search is essential for organizations evaluating its adoption and for ongoing cost management. The model is designed around the principle of "pay only for what you use" , offering flexibility but also requiring careful consideration of various cost components. Google Cloud typically provides a free trial, often including $300 in credits for new customers to explore services. Additionally, a free tier is available for some services, notably a 10 GiB per month free quota for Index Data Storage, which is shared across AI Applications.

The pricing for Vertex AI Search can be broken down into several key areas:

Core Search Editions and Query Costs

Search Standard Edition: This edition is priced based on the number of queries processed, typically per 1,000 queries. For example, a common rate is $1.50 per 1,000 queries.

Search Enterprise Edition: This edition includes Core Generative Answers (AI Mode) and is priced at a higher rate per 1,000 queries, such as $4.00 per 1,000 queries.

Advanced Generative Answers (AI Mode): This is an optional add-on available for both Standard and Enterprise Editions. It incurs an additional cost per 1,000 user input queries, for instance, an extra $4.00 per 1,000 user input queries.

Data Indexing Costs

Index Storage: Costs for storing indexed data are charged per GiB of raw data per month. A typical rate is $5.00 per GiB per month. As mentioned, a free quota (e.g., 10 GiB per month) is usually provided. This cost is directly associated with the underlying "corpus" where data is stored and managed.

Grounding and Generative AI Cost Components

When utilizing the generative AI capabilities, particularly for grounding LLM responses, several components contribute to the overall cost :

Input Prompt (for grounding): The cost is determined by the number of characters in the input prompt provided for the grounding process, including any grounding facts. An example rate is $0.000125 per 1,000 characters.

Output (generated by model): The cost for the output generated by the LLM is also based on character count. An example rate is $0.000375 per 1,000 characters.

Grounded Generation (for grounding on own retrieved data): There is a cost per 1,000 requests for utilizing the grounding functionality itself, for example, $2.50 per 1,000 requests.

Data Retrieval (Vertex AI Search - Enterprise edition): When Vertex AI Search (Enterprise edition) is used to retrieve documents for grounding, a query cost applies, such as $4.00 per 1,000 requests.

Check Grounding API: This API allows users to assess how well a piece of text (an answer candidate) is grounded in a given set of reference texts (facts). The cost is per 1,000 answer characters, for instance, $0.00075 per 1,000 answer characters.

Industry-Specific Pricing

Vertex AI Search offers specialized pricing for its industry-tailored solutions:

Vertex AI Search for Healthcare: This version has a distinct, typically higher, query cost, such as $20.00 per 1,000 queries. It includes features like GenAI-powered answers and streaming updates to the index, some of which may be in Preview status. Data indexing costs are generally expected to align with standard rates.

Vertex AI Search for Media:

Media Search API Request Count: A specific query cost applies, for example, $2.00 per 1,000 queries.

Data Index: Standard data indexing rates, such as $5.00 per GB per month, typically apply.

Media Recommendations: Pricing for media recommendations is often tiered based on the volume of prediction requests per month (e.g., $0.27 per 1,000 predictions for up to 20 million, $0.18 for the next 280 million, and so on). Additionally, training and tuning of recommendation models are charged per node per hour, for example, $2.50 per node per hour.

Document AI Feature Pricing (when integrated)

If Vertex AI Search utilizes integrated Document AI features for processing documents, these will incur their own costs:

Enterprise Document OCR Processor: Pricing is typically tiered based on the number of pages processed per month, for example, $1.50 per 1,000 pages for 1 to 5 million pages per month.

Layout Parser (includes initial chunking): This feature is priced per 1,000 pages, for instance, $10.00 per 1,000 pages.

Vector Search Cost Considerations

Specific cost considerations apply to Vertex AI Vector Search, particularly highlighted by user feedback :

A user found Vector Search to be "costly" due to the necessity of keeping compute resources (machines) continuously running for index serving, even during periods of no query activity. This implies ongoing costs for provisioned resources, distinct from per-query charges.

Supporting documentation confirms this model, with "Index Serving" costs that vary by machine type and region, and "Index Building" costs, such as $3.00 per GiB of data processed.

Pricing Examples

Illustrative pricing examples provided in sources like and demonstrate how these various components can combine to form the total cost for different usage scenarios, including general availability (GA) search functionality, media recommendations, and grounding operations.

The following table summarizes key pricing components for Vertex AI Search:

Vertex AI Search Pricing SummaryService ComponentEdition/TypeUnitPrice (Example)Free Tier/NotesSearch QueriesStandard1,000 queries$1.5010k free trial queries often includedSearch QueriesEnterprise (with Core GenAI)1,000 queries$4.0010k free trial queries often includedAdvanced GenAI (Add-on)Standard or Enterprise1,000 user input queries+$4.00Index Data StorageAllGiB/month$5.0010 GiB/month free (shared across AI Applications)Grounding: Input PromptGenerative AI1,000 characters$0.000125Grounding: OutputGenerative AI1,000 characters$0.000375Grounding: Grounded GenerationGenerative AI1,000 requests$2.50For grounding on own retrieved dataGrounding: Data RetrievalEnterprise Search1,000 requests$4.00When using Vertex AI Search (Enterprise) for retrievalCheck Grounding APIAPI1,000 answer characters$0.00075Healthcare Search QueriesHealthcare1,000 queries$20.00Includes some Preview featuresMedia Search API QueriesMedia1,000 queries$2.00Media Recommendations (Predictions)Media1,000 predictions$0.27 (up to 20M/mo), $0.18 (next 280M/mo), $0.10 (after 300M/mo)Tiered pricingMedia Recs Training/TuningMediaNode/hour$2.50Document OCRDocument AI Integration1,000 pages$1.50 (1-5M pages/mo), $0.60 (>5M pages/mo)Tiered pricingLayout ParserDocument AI Integration1,000 pages$10.00Includes initial chunkingVector Search: Index BuildingVector SearchGiB processed$3.00Vector Search: Index ServingVector SearchVariesVaries by machine type & region (e.g., $0.094/node hour for e2-standard-2 in us-central1)Implies "always-on" costs for provisioned resourcesExport to Sheets

Note: Prices are illustrative examples based on provided research and are subject to change. Refer to official Google Cloud pricing documentation for current rates.

The multifaceted pricing structure, with costs broken down by queries, data volume, character counts for generative AI, specific APIs, and even underlying Document AI processors , reflects the feature richness and granularity of Vertex AI Search. This allows users to align costs with the specific features they consume, consistent with the "pay only for what you use" philosophy. However, this granularity also means that accurately estimating total costs can be a complex undertaking. Users must thoroughly understand their anticipated usage patterns across various dimensions—query volume, data size, frequency of generative AI interactions, document processing needs—to predict expenses with reasonable accuracy. The seemingly simple act of obtaining a generative answer, for instance, can involve multiple cost components: input prompt processing, output generation, the grounding operation itself, and the data retrieval query. Organizations, particularly those with large datasets, high query volumes, or plans for extensive use of generative features, may find it challenging to forecast costs without detailed analysis and potentially leveraging tools like the Google Cloud pricing calculator. This complexity could present a barrier for smaller organizations or those with less experience in managing cloud expenditures. It also underscores the importance of closely monitoring usage to prevent unexpected costs. The decision between Standard and Enterprise editions, and whether to incorporate Advanced Generative Answers, becomes a significant cost-benefit analysis.

Furthermore, a critical aspect of the pricing model for certain high-performance features like Vertex AI Vector Search is the "always-on" cost component. User feedback explicitly noted Vector Search as "costly" due to the requirement to "keep my machine on even when a user ain't querying". This is corroborated by pricing details that list "Index Serving" costs varying by machine type and region , which are distinct from purely consumption-based fees (like per-query charges) where costs would be zero if there were no activity. For features like Vector Search that necessitate provisioned infrastructure for index serving, a baseline operational cost exists regardless of query volume. This is a crucial distinction from on-demand pricing models and can significantly impact the total cost of ownership (TCO) for use cases that rely heavily on Vector Search but may experience intermittent query patterns. This continuous cost for certain features means that organizations must evaluate the ongoing value derived against their persistent expense. It might render Vector Search less economical for applications with very sporadic usage unless the benefits during active periods are substantial. This could also suggest that Google might, in the future, offer different tiers or configurations for Vector Search to cater to varying performance and cost needs, or users might need to architect solutions to de-provision and re-provision indexes if usage is highly predictable and infrequent, though this would add operational complexity.

7. Comparative Analysis

Vertex AI Search operates in a competitive landscape of enterprise search and AI platforms. Understanding its position relative to alternatives is crucial for informed decision-making. Key comparisons include specialized product discovery solutions like Algolia and broader enterprise search platforms from other major cloud providers and niche vendors.

Vertex AI Search for Commerce vs. Algolia

For e-commerce and retail product discovery, Vertex AI Search for Commerce and Algolia are prominent solutions, each with distinct strengths :

Core Search Quality & Features:

Vertex AI Search for Commerce is built upon Google's extensive search algorithm expertise, enabling it to excel at interpreting complex queries by understanding user context, intent, and even informal language. It features dynamic spell correction and synonym suggestions, consistently delivering high-quality, context-rich results. Its primary strengths lie in natural language understanding (NLU) and dynamic AI-driven corrections.

Algolia has established its reputation with a strong focus on semantic search and autocomplete functionalities, powered by its NeuralSearch capabilities. It adapts quickly to user intent. However, it may require more manual fine-tuning to address highly complex or context-rich queries effectively. Algolia is often prized for its speed, ease of configuration, and feature-rich autocomplete.

Customer Engagement & Personalization:

Vertex AI incorporates advanced recommendation models that adapt based on user interactions. It can optimize search results based on defined business objectives like click-through rates (CTR), revenue per session, and conversion rates. Its dynamic personalization capabilities mean search results evolve based on prior user behavior, making the browsing experience progressively more relevant. The deep integration of AI facilitates a more seamless, data-driven personalization experience.

Algolia offers an impressive suite of personalization tools with various recommendation models suitable for different retail scenarios. The platform allows businesses to customize search outcomes through configuration, aligning product listings, faceting, and autocomplete suggestions with their customer engagement strategy. However, its personalization features might require businesses to integrate additional services or perform more fine-tuning to achieve the level of dynamic personalization seen in Vertex AI.

Merchandising & Display Flexibility:

Vertex AI utilizes extensive AI models to enable dynamic ranking configurations that consider not only search relevance but also business performance metrics such as profitability and conversion data. The search engine automatically sorts products by match quality and considers which products are likely to drive the best business outcomes, reducing the burden on retail teams by continuously optimizing based on live data. It can also blend search results with curated collections and themes. A noted current limitation is that Google is still developing new merchandising tools, and the existing toolset is described as "fairly limited".

Algolia offers powerful faceting and grouping capabilities, allowing for the creation of curated displays for promotions, seasonal events, or special collections. Its flexible configuration options permit merchants to manually define boost and slotting rules to prioritize specific products for better visibility. These manual controls, however, might require more ongoing maintenance compared to Vertex AI's automated, outcome-based ranking. Algolia's configuration-centric approach may be better suited for businesses that prefer hands-on control over merchandising details.

Implementation, Integration & Operational Efficiency:

A key advantage of Vertex AI is its seamless integration within the broader Google Cloud ecosystem, making it a natural choice for retailers already utilizing Google Merchant Center, Google Cloud Storage, or BigQuery. Its sophisticated AI models mean that even a simple initial setup can yield high-quality results, with the system automatically learning from user interactions over time. A potential limitation is its significant data requirements; businesses lacking large volumes of product or interaction data might not fully leverage its advanced capabilities, and smaller brands may find themselves in lower Data Quality tiers.

Algolia is renowned for its ease of use and rapid deployment, offering a user-friendly interface, comprehensive documentation, and a free tier suitable for early-stage projects. It is designed to integrate with various e-commerce systems and provides a flexible API for straightforward customization. While simpler and more accessible for smaller businesses, this ease of use might necessitate additional configuration for very complex or data-intensive scenarios.

Analytics, Measurement & Future Innovations:

Vertex AI provides extensive insights into both search performance and business outcomes, tracking metrics like CTR, conversion rates, and profitability. The ability to export search and event data to BigQuery enhances its analytical power, offering possibilities for custom dashboards and deeper AI/ML insights. It is well-positioned to benefit from Google's ongoing investments in AI, integration with services like Google Vision API, and the evolution of large language models and conversational commerce.

Algolia offers detailed reporting on search performance, tracking visits, searches, clicks, and conversions, and includes views for data quality monitoring. Its analytics capabilities tend to focus more on immediate search performance rather than deeper business performance metrics like average order value or revenue impact. Algolia is also rapidly innovating, especially in enhancing its semantic search and autocomplete functions, though its evolution may be more incremental compared to Vertex AI's broader ecosystem integration.

In summary, Vertex AI Search for Commerce is often an ideal choice for large retailers with extensive datasets, particularly those already integrated into the Google or Shopify ecosystems, who are seeking advanced AI-driven optimization for customer engagement and business outcomes. Conversely, Algolia presents a strong option for businesses that prioritize rapid deployment, ease of use, and flexible semantic search and autocomplete functionalities, especially smaller retailers or those desiring more hands-on control over their search configuration.

Vertex AI Search vs. Other Enterprise Search Solutions

Beyond e-commerce, Vertex AI Search competes with a range of enterprise search solutions :

INDICA Enterprise Search: This solution utilizes a patented approach to index both structured and unstructured data, prioritizing results by relevance. It offers a sophisticated query builder and comprehensive filtering options. Both Vertex AI Search and INDICA Enterprise Search provide API access, free trials/versions, and similar deployment and support options. INDICA lists "Sensitive Data Discovery" as a feature, while Vertex AI Search highlights "eCommerce Search, Retrieval-Augmented Generation (RAG), Semantic Search, and Site Search" as additional capabilities. Both platforms integrate with services like Gemini, Google Cloud Document AI, Google Cloud Platform, HTML, and Vertex AI.

Azure AI Search: Microsoft's offering features a vector database specifically designed for advanced RAG and contemporary search functionalities. It emphasizes enterprise readiness, incorporating security, compliance, and ethical AI methodologies. Azure AI Search supports advanced retrieval techniques, integrates with various platforms and data sources, and offers comprehensive vector data processing (extraction, chunking, enrichment, vectorization). It supports diverse vector types, hybrid models, multilingual capabilities, metadata filtering, and extends beyond simple vector searches to include keyword match scoring, reranking, geospatial search, and autocomplete features. The strong emphasis on RAG and vector capabilities by both Vertex AI Search and Azure AI Search positions them as direct competitors in the AI-powered enterprise search market.

IBM Watson Discovery: This platform leverages AI-driven search to extract precise answers and identify trends from various documents and websites. It employs advanced NLP to comprehend industry-specific terminology, aiming to reduce research time significantly by contextualizing responses and citing source documents. Watson Discovery also uses machine learning to visually categorize text, tables, and images. Its focus on deep NLP and understanding industry-specific language mirrors claims made by Vertex AI, though Watson Discovery has a longer established presence in this particular enterprise AI niche.

Guru: An AI search and knowledge platform, Guru delivers trusted information from a company's scattered documents, applications, and chat platforms directly within users' existing workflows. It features a personalized AI assistant and can serve as a modern replacement for legacy wikis and intranets. Guru offers extensive native integrations with popular business tools like Slack, Google Workspace, Microsoft 365, Salesforce, and Atlassian products. Guru's primary focus on knowledge management and in-app assistance targets a potentially more specialized use case than the broader enterprise search capabilities of Vertex AI, though there is an overlap in accessing and utilizing internal knowledge.

AddSearch: Provides fast, customizable site search for websites and web applications, using a crawler or an Indexing API. It offers enterprise-level features such as autocomplete, synonyms, ranking tools, and progressive ranking, designed to scale from small businesses to large corporations.

Haystack: Aims to connect employees with the people, resources, and information they need. It offers intranet-like functionalities, including custom branding, a modular layout, multi-channel content delivery, analytics, knowledge sharing features, and rich employee profiles with a company directory.

Atolio: An AI-powered enterprise search engine designed to keep data securely within the customer's own cloud environment (AWS, Azure, or GCP). It provides intelligent, permission-based responses and ensures that intellectual property remains under control, with LLMs that do not train on customer data. Atolio integrates with tools like Office 365, Google Workspace, Slack, and Salesforce. A direct comparison indicates that both Atolio and Vertex AI Search offer similar deployment, support, and training options, and share core features like AI/ML, faceted search, and full-text search. Vertex AI Search additionally lists RAG, Semantic Search, and Site Search as features not specified for Atolio in that comparison.

The following table provides a high-level feature comparison:

Feature and Capability Comparison: Vertex AI Search vs. Key CompetitorsFeature/CapabilityVertex AI SearchAlgolia (Commerce)Azure AI SearchIBM Watson DiscoveryINDICA ESGuruAtolioPrimary FocusEnterprise Search + RAG, Industry SolutionsProduct Discovery, E-commerce SearchEnterprise Search + RAG, Vector DBNLP-driven Insight Extraction, Document AnalysisGeneral Enterprise Search, Data DiscoveryKnowledge Management, In-App SearchSecure Enterprise Search, Knowledge Discovery (Self-Hosted Focus)RAG CapabilitiesOut-of-the-box, Custom via APIsN/A (Focus on product search)Strong, Vector DB optimized for RAGDocument understanding supports RAG-like patternsAI/ML features, less explicit RAG focusSurfaces existing knowledge, less about new content generationAI-powered answers, less explicit RAG focusVector SearchYes, integrated & standaloneSemantic search (NeuralSearch)Yes, core feature (Vector Database)Semantic understanding, less focus on explicit vector DBAI/Machine LearningAI-powered searchAI-powered searchSemantic Search QualityHigh (Google tech)High (NeuralSearch)HighHigh (Advanced NLP)Relevance-based rankingHigh for knowledge assetsIntelligent responsesSupported Data TypesStructured, Unstructured, Web, Healthcare, MediaPrimarily Product DataStructured, Unstructured, VectorDocuments, WebsitesStructured, UnstructuredDocs, Apps, ChatsEnterprise knowledge base (docs, apps)Industry SpecializationsRetail, Media, HealthcareRetail/E-commerceGeneral PurposeTunable for industry terminologyGeneral PurposeGeneral Knowledge ManagementGeneral Enterprise SearchKey DifferentiatorsGoogle Search tech, Out-of-box RAG, Gemini IntegrationSpeed, Ease of Config, AutocompleteAzure Ecosystem Integration, Comprehensive Vector ToolsDeep NLP, Industry Terminology UnderstandingPatented indexing, Sensitive Data DiscoveryIn-app accessibility, Extensive IntegrationsData security (self-hosted, no LLM training on customer data)Generative AI IntegrationStrong (Gemini, Grounding API)Limited (focus on search relevance)Strong (for RAG with Azure OpenAI)Supports GenAI workflowsAI/ML capabilitiesAI assistant for answersLLM-powered answersPersonalizationAdvanced (AI-driven)Strong (Configurable)Via integration with other Azure servicesN/AN/APersonalized AI assistantN/AEase of ImplementationModerate to Complex (depends on use case)HighModerate to ComplexModerate to ComplexModerateHighModerate (focus on secure deployment)Data Security ApproachGCP Security (VPC-SC, CMEK), Data SegregationStandard SaaS securityAzure Security (Compliance, Ethical AI)IBM Cloud SecurityStandard Enterprise SecurityStandard SaaS securityStrong emphasis on self-hosting & data controlExport to Sheets

The enterprise search market appears to be evolving along two axes: general-purpose platforms that offer a wide array of capabilities, and more specialized solutions tailored to specific use cases or industries. Artificial intelligence, in various forms such as semantic search, NLP, and vector search, is becoming a common denominator across almost all modern offerings. This means customers often face a choice between adopting a best-of-breed specialized tool that excels in a particular area (like Algolia for e-commerce or Guru for internal knowledge management) or investing in a broader platform like Vertex AI Search or Azure AI Search. These platforms provide good-to-excellent capabilities across many domains but might require more customization or configuration to meet highly specific niche requirements. Vertex AI Search, with its combination of a general platform and distinct industry-specific versions, attempts to bridge this gap. The success of this strategy will likely depend on how effectively its specialized versions compete with dedicated niche solutions and how readily the general platform can be adapted for unique needs.

As enterprises increasingly deploy AI solutions over sensitive proprietary data, concerns regarding data privacy, security, and intellectual property protection are becoming paramount. Vendors are responding by highlighting their security and data governance features as key differentiators. Atolio, for instance, emphasizes that it "keeps data securely within your cloud environment" and that its "LLMs do not train on your data". Similarly, Vertex AI Search details its security measures, including securing user data within the customer's cloud instance, compliance with standards like HIPAA and ISO, and features like VPC Service Controls and Customer-Managed Encryption Keys (CMEK). Azure AI Search also underscores its commitment to "security, compliance, and ethical AI methodologies". This growing focus suggests that the ability to ensure data sovereignty, meticulously control data access, and prevent data leakage or misuse by AI models is becoming as critical as search relevance or operational speed. For customers, particularly those in highly regulated industries, these data governance and security aspects could become decisive factors when selecting an enterprise search solution, potentially outweighing minor differences in other features. The often "black box" nature of some AI models makes transparent data handling policies and robust security postures increasingly crucial.

8. Known Limitations, Challenges, and User Experiences

While Vertex AI Search offers powerful capabilities, user experiences and technical reviews have highlighted several limitations, challenges, and considerations that organizations should be aware of during evaluation and implementation.

Reported User Issues and Challenges

Direct user feedback and community discussions have surfaced specific operational issues:

"No results found" Errors / Inconsistent Search Behavior: A notable user experience involved consistently receiving "No results found" messages within the Vertex AI Search app preview. This occurred even when other members of the same organization could use the search functionality without issue, and IAM and Datastore permissions appeared to be identical for the affected user. Such issues point to potential user-specific, environment-related, or difficult-to-diagnose configuration problems that are not immediately apparent.

Cross-OS Inconsistencies / Browser Compatibility: The same user reported that following the Vertex AI Search tutorial yielded successful results on a Windows operating system, but attempting the same on macOS resulted in a 403 error during the search operation. This suggests possible browser compatibility problems, issues with cached data, or differences in how the application interacts with various operating systems.

IAM Permission Complexity: Users have expressed difficulty in accurately confirming specific "Discovery Engine search permissions" even when utilizing the IAM Policy Troubleshooter. There was ambiguity regarding the determination of principal access boundaries, the effect of deny policies, or the final resolution of permissions. This indicates that navigating and verifying the necessary IAM permissions for Vertex AI Search can be a complex undertaking.

Issues with JSON Data Input / Query Phrasing: A recent issue, reported in May 2025, indicates that the latest release of Vertex AI Search (referred to as AI Application) has introduced challenges with semantic search over JSON data. According to the report, the search engine now primarily processes queries phrased in a natural language style, similar to that used in the UI, rather than structured filter expressions. This means filters or conditions must be expressed as plain language questions (e.g., "How many findings have a severity level marked as HIGH in d3v-core?"). Furthermore, it was noted that sometimes, even when specific keys are designated as "searchable" in the datastore schema, the system fails to return results, causing significant problems for certain types of queries. This represents a potentially disruptive change in behavior for users accustomed to working with JSON data in a more structured query manner.

Lack of Clear Error Messages: In the scenario where a user consistently received "No results found," it was explicitly stated that "There are no console or network errors". The absence of clear, actionable error messages can significantly complicate and prolong the diagnostic process for such issues.

Potential Challenges from Technical Specifications and User Feedback

Beyond specific bug reports, technical deep-dives and early adopter feedback have revealed other considerations, particularly concerning the underlying Vector Search component :

Cost of Vector Search: A user found Vertex AI Vector Search to be "costly." This was attributed to the operational model requiring compute resources (machines) to remain active and provisioned for index serving, even during periods when no queries were being actively processed. This implies a continuous baseline cost associated with using Vector Search.

File Type Limitations (Vector Search): As of the user's experience documented in , Vertex AI Vector Search did not offer support for indexing .xlsx (Microsoft Excel) files.

Document Size Limitations (Vector Search): Concerns were raised about the platform's ability to effectively handle "bigger document sizes" within the Vector Search component.

Embedding Dimension Constraints (Vector Search): The user reported an inability to create a Vector Search index with embedding dimensions other than the default 768 if the "corpus doesn't support" alternative dimensions. This suggests a potential lack of flexibility in configuring embedding parameters for certain setups.

rag_file_ids Not Directly Supported for Filtering: For applications using the Grounding API, it was noted that direct filtering of results based on rag_file_ids (presumably identifiers for files used in RAG) is not supported. The suggested workaround involves adding a custom file_id to the document metadata and using that for filtering purposes.

Data Requirements for Advanced Features (Vertex AI Search for Commerce)

For specialized solutions like Vertex AI Search for Commerce, the effectiveness of advanced features can be contingent on the available data:

A potential limitation highlighted for Vertex AI Search for Commerce is its "significant data requirements." Businesses that lack large volumes of product data or user interaction data (e.g., clicks, purchases) might not be able to fully leverage its advanced AI capabilities for personalization and optimization. Smaller brands, in particular, may find themselves remaining in lower Data Quality tiers, which could impact the performance of these features.

Merchandising Toolset (Vertex AI Search for Commerce)

The maturity of all components is also a factor:

The current merchandising toolset available within Vertex AI Search for Commerce has been described as "fairly limited." It is noted that Google is still in the process of developing and releasing new tools for this area. Retailers with sophisticated merchandising needs might find the current offerings less comprehensive than desired.

The rapid evolution of platforms like Vertex AI Search, while bringing cutting-edge features, can also introduce challenges. Recent user reports, such as the significant change in how JSON data queries are handled in the "latest version" as of May 2025, and other unexpected behaviors , illustrate this point. Vertex AI Search is part of a dynamic AI landscape, with Google frequently rolling out updates and integrating new models like Gemini. While this pace of innovation is a key strength, it can also lead to modifications in existing functionalities or, occasionally, introduce temporary instabilities. Users, especially those with established applications built upon specific, previously observed behaviors of the platform, may find themselves needing to adapt their implementations swiftly when such changes occur. The JSON query issue serves as a prime example of a change that could be disruptive for some users. Consequently, organizations adopting Vertex AI Search, particularly for mission-critical applications, should establish robust processes for monitoring platform updates, thoroughly testing changes in staging or development environments, and adapting their code or configurations as required. This highlights an inherent trade-off: gaining access to state-of-the-art AI features comes with the responsibility of managing the impacts of a fast-moving and evolving platform. It also underscores the critical importance of comprehensive documentation and clear, proactive communication from Google regarding any changes in platform behavior.

Moreover, there can be a discrepancy between the marketed ease-of-use and the actual complexity encountered during real-world implementation, especially for specific or advanced scenarios. While Vertex AI Search is promoted for its straightforward setup and out-of-the-box functionalities , detailed user experiences, such as those documented in and , reveal significant challenges. These can include managing the costs of components like Vector Search, dealing with limitations in supported file types or embedding dimensions, navigating the intricacies of IAM permissions, and achieving highly specific filtering requirements (e.g., querying by a custom document_id). The user in , for example, was attempting to implement a relatively complex use case involving 500GB of documents, specific ID-based querying, multi-year conversational history, and real-time data ingestion. This suggests that while basic setup might indeed be simple, implementing advanced or highly tailored enterprise requirements can unearth complexities and limitations not immediately apparent from high-level descriptions. The "out-of-the-box" solution may necessitate considerable workarounds (such as using metadata for ID-based filtering ) or encounter hard limitations for particular needs. Therefore, prospective users should conduct thorough proof-of-concept projects tailored to their specific, complex use cases. This is essential to validate that Vertex AI Search and its constituent components, like Vector Search, can adequately meet their technical requirements and align with their cost constraints. Marketing claims of simplicity need to be balanced with a realistic assessment of the effort and expertise required for sophisticated deployments. This also points to a continuous need for more detailed best practices, advanced troubleshooting guides, and transparent documentation from Google for these complex scenarios.

9. Recent Developments and Future Outlook

Vertex AI Search is a rapidly evolving platform, with Google Cloud continuously integrating its latest AI research and model advancements. Recent developments, particularly highlighted during events like Google I/O and Google Cloud Next 2025, indicate a clear trajectory towards more powerful, integrated, and agentic AI capabilities.

Integration with Latest AI Models (Gemini)

A significant thrust in recent developments is the deepening integration of Vertex AI Search with Google's flagship Gemini models. These models are multimodal, capable of understanding and processing information from various formats (text, images, audio, video, code), and possess advanced reasoning and generation capabilities.

The Gemini 2.5 model, for example, is slated to be incorporated into Google Search for features like AI Mode and AI Overviews in the U.S. market. This often signals broader availability within Vertex AI for enterprise use cases.

Within the Vertex AI Agent Builder, Gemini can be utilized to enhance agent responses with information retrieved from Google Search, while Vertex AI Search (with its RAG capabilities) facilitates the seamless integration of enterprise-specific data to ground these advanced models.

Developers have access to Gemini models through Vertex AI Studio and the Model Garden, allowing for experimentation, fine-tuning, and deployment tailored to specific application needs.

Platform Enhancements (from Google I/O & Cloud Next 2025)

Key announcements from recent Google events underscore the expansion of the Vertex AI platform, which directly benefits Vertex AI Search:

Vertex AI Agent Builder: This initiative consolidates a suite of tools designed to help developers create enterprise-ready generative AI experiences, applications, and intelligent agents. Vertex AI Search plays a crucial role in this builder by providing the essential data grounding capabilities. The Agent Builder supports the creation of codeless conversational agents and facilitates low-code AI application development.

Expanded Model Garden: The Model Garden within Vertex AI now offers access to an extensive library of over 200 models. This includes Google's proprietary models (like Gemini and Imagen), models from third-party providers (such as Anthropic's Claude), and popular open-source models (including Gemma and Llama 3.2). This wide selection provides developers with greater flexibility in choosing the optimal model for diverse use cases.

Multi-agent Ecosystem: Google Cloud is fostering the development of collaborative AI agents with new tools such as the Agent Development Kit (ADK) and the Agent2Agent (A2A) protocol.

Generative Media Suite: Vertex AI is distinguishing itself by offering a comprehensive suite of generative media models. This includes models for video generation (Veo), image generation (Imagen), speech synthesis, and, with the addition of Lyria, music generation.

AI Hypercomputer: This revolutionary supercomputing architecture is designed to simplify AI deployment, significantly boost performance, and optimize costs for training and serving large-scale AI models. Services like Vertex AI are built upon and benefit from these infrastructure advancements.

Performance and Usability Improvements

Google continues to refine the performance and usability of Vertex AI components:

Vector Search Indexing Latency: A notable improvement is the significant reduction in indexing latency for Vector Search, particularly for smaller datasets. This process, which previously could take hours, has been brought down to minutes.

No-Code Index Deployment for Vector Search: To lower the barrier to entry for using vector databases, developers can now create and deploy Vector Search indexes without needing to write code.

Emerging Trends and Future Capabilities

The future direction of Vertex AI Search and related AI services points towards increasingly sophisticated and autonomous capabilities:

Agentic Capabilities: Google is actively working on infusing more autonomous, agent-like functionalities into its AI offerings. Project Mariner's "computer use" capabilities are being integrated into the Gemini API and Vertex AI. Furthermore, AI Mode in Google Search Labs is set to gain agentic capabilities for handling tasks such as booking event tickets and making restaurant reservations.

Deep Research and Live Interaction: For Google Search's AI Mode, "Deep Search" is being introduced in Labs to provide more thorough and comprehensive responses to complex queries. Additionally, "Search Live," stemming from Project Astra, will enable real-time, camera-based conversational interactions with Search.

Data Analysis and Visualization: Future enhancements to AI Mode in Labs include the ability to analyze complex datasets and automatically create custom graphics and visualizations to bring the data to life, initially focusing on sports and finance queries.

Thought Summaries: An upcoming feature for Gemini 2.5 Pro and Flash, available in the Gemini API and Vertex AI, is "thought summaries." This will organize the model's raw internal "thoughts" or processing steps into a clear, structured format with headers, key details, and information about model actions, such as when it utilizes external tools.

The consistent emphasis on integrating advanced multimodal models like Gemini , coupled with the strategic development of the Vertex AI Agent Builder and the introduction of "agentic capabilities" , suggests a significant evolution for Vertex AI Search. While RAG primarily focuses on retrieving information to ground LLMs, these newer developments point towards enabling these LLMs (often operating within an agentic framework) to perform more complex tasks, reason more deeply about the retrieved information, and even initiate actions based on that information. The planned inclusion of "thought summaries" further reinforces this direction by providing transparency into the model's reasoning process. This trajectory indicates that Vertex AI Search is moving beyond being a simple information retrieval system. It is increasingly positioned as a critical component that feeds and grounds more sophisticated AI reasoning processes within enterprise-specific agents and applications. The search capability, therefore, becomes the trusted and factual data interface upon which these advanced AI models can operate more reliably and effectively. This positions Vertex AI Search as a fundamental enabler for the next generation of enterprise AI, which will likely be characterized by more autonomous, intelligent agents capable of complex problem-solving and task execution. The quality, comprehensiveness, and freshness of the data indexed by Vertex AI Search will, therefore, directly and critically impact the performance and reliability of these future intelligent systems.

Furthermore, there is a discernible pattern of advanced AI features, initially tested and rolled out in Google's consumer-facing products, eventually trickling into its enterprise offerings. Many of the new AI features announced for Google Search (the consumer product) at events like I/O 2025—such as AI Mode, Deep Search, Search Live, and agentic capabilities for shopping or reservations —often rely on underlying technologies or paradigms that also find their way into Vertex AI for enterprise clients. Google has a well-established history of leveraging its innovations in consumer AI (like its core search algorithms and natural language processing breakthroughs) as the foundation for its enterprise cloud services. The Gemini family of models, for instance, powers both consumer experiences and enterprise solutions available through Vertex AI. This suggests that innovations and user experience paradigms that are validated and refined at the massive scale of Google's consumer products are likely to be adapted and integrated into Vertex AI Search and related enterprise AI tools. This allows enterprises to benefit from cutting-edge AI capabilities that have been battle-tested in high-volume environments. Consequently, enterprises can anticipate that user expectations for search and AI interaction within their own applications will be increasingly shaped by these advanced consumer experiences. Vertex AI Search, by incorporating these underlying technologies, helps businesses meet these rising expectations. However, this also implies that the pace of change in enterprise tools might be influenced by the rapid innovation cycle of consumer AI, once again underscoring the need for organizational adaptability and readiness to manage platform evolution.

10. Conclusion and Strategic Recommendations

Vertex AI Search stands as a powerful and strategic offering from Google Cloud, designed to bring Google-quality search and cutting-edge generative AI capabilities to enterprises. Its ability to leverage an organization's own data for grounding large language models, coupled with its integration into the broader Vertex AI ecosystem, positions it as a transformative tool for businesses seeking to unlock greater value from their information assets and build next-generation AI applications.

Summary of Key Benefits and Differentiators

Vertex AI Search offers several compelling advantages:

Leveraging Google's AI Prowess: It is built on Google's decades of experience in search, natural language processing, and AI, promising high relevance and sophisticated understanding of user intent.

Powerful Out-of-the-Box RAG: Simplifies the complex process of building Retrieval Augmented Generation systems, enabling more accurate, reliable, and contextually relevant generative AI applications grounded in enterprise data.

Integration with Gemini and Vertex AI Ecosystem: Seamless access to Google's latest foundation models like Gemini and integration with a comprehensive suite of MLOps tools within Vertex AI provide a unified platform for AI development and deployment.

Industry-Specific Solutions: Tailored offerings for retail, media, and healthcare address unique industry needs, accelerating time-to-value.

Robust Security and Compliance: Enterprise-grade security features and adherence to industry compliance standards provide a trusted environment for sensitive data.

Continuous Innovation: Rapid incorporation of Google's latest AI research ensures the platform remains at the forefront of AI-powered search technology.

Guidance on When Vertex AI Search is a Suitable Choice

Vertex AI Search is particularly well-suited for organizations with the following objectives and characteristics:

Enterprises aiming to build sophisticated, AI-powered search applications that operate over their proprietary structured and unstructured data.

Businesses looking to implement reliable RAG systems to ground their generative AI applications, reduce LLM hallucinations, and ensure responses are based on factual company information.

Companies in the retail, media, and healthcare sectors that can benefit from specialized, pre-tuned search and recommendation solutions.

Organizations already invested in the Google Cloud Platform ecosystem, seeking seamless integration and a unified AI/ML environment.

Businesses that require scalable, enterprise-grade search capabilities incorporating advanced features like vector search, semantic understanding, and conversational AI.

Strategic Considerations for Adoption and Implementation

To maximize the benefits and mitigate potential challenges of adopting Vertex AI Search, organizations should consider the following:

Thorough Proof-of-Concept (PoC) for Complex Use Cases: Given that advanced or highly specific scenarios may encounter limitations or complexities not immediately apparent , conducting rigorous PoC testing tailored to these unique requirements is crucial before full-scale deployment.

Detailed Cost Modeling: The granular pricing model, which includes charges for queries, data storage, generative AI processing, and potentially always-on resources for components like Vector Search , necessitates careful and detailed cost forecasting. Utilize Google Cloud's pricing calculator and monitor usage closely.

Prioritize Data Governance and IAM: Due to the platform's ability to access and index vast amounts of enterprise data, investing in meticulous planning and implementation of data governance policies and IAM configurations is paramount. This ensures data security, privacy, and compliance.

Develop Team Skills and Foster Adaptability: While Vertex AI Search is designed for ease of use in many aspects, advanced customization, troubleshooting, or managing the impact of its rapid evolution may require specialized skills within the implementation team. The platform is constantly changing, so a culture of continuous learning and adaptability is beneficial.

Consider a Phased Approach: Organizations can begin by leveraging Vertex AI Search to improve existing search functionalities, gaining early wins and familiarity. Subsequently, they can progressively adopt more advanced AI features like RAG and conversational AI as their internal AI maturity and comfort levels grow.

Monitor and Maintain Data Quality: The performance of Vertex AI Search, especially its industry-specific solutions like Vertex AI Search for Commerce, is highly dependent on the quality and volume of the input data. Establish processes for monitoring and maintaining data quality.

Final Thoughts on Future Trajectory

Vertex AI Search is on a clear path to becoming more than just an enterprise search tool. Its deepening integration with advanced AI models like Gemini, its role within the Vertex AI Agent Builder, and the emergence of agentic capabilities suggest its evolution into a core "reasoning engine" for enterprise AI. It is well-positioned to serve as a fundamental data grounding and contextualization layer for a new generation of intelligent applications and autonomous agents. As Google continues to infuse its latest AI research and model innovations into the platform, Vertex AI Search will likely remain a key enabler for businesses aiming to harness the full potential of their data in the AI era.

The platform's design, offering a spectrum of capabilities from enhancing basic website search to enabling complex RAG systems and supporting future agentic functionalities , allows organizations to engage with it at various levels of AI readiness. This characteristic positions Vertex AI Search as a potential catalyst for an organization's overall AI maturity journey. Companies can embark on this journey by addressing tangible, lower-risk search improvement needs and then, using the same underlying platform, progressively explore and implement more advanced AI applications. This iterative approach can help build internal confidence, develop requisite skills, and demonstrate value incrementally. In this sense, Vertex AI Search can be viewed not merely as a software product but as a strategic platform that facilitates an organization's AI transformation. By providing an accessible yet powerful and evolving solution, Google encourages deeper and more sustained engagement with its comprehensive AI ecosystem, fostering long-term customer relationships and driving broader adoption of its cloud services. The ultimate success of this approach will hinge on Google's continued commitment to providing clear guidance, robust support, predictable platform evolution, and transparent communication with its users.

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