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sportsbilla · 17 days ago

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Best AI Tool to Create AI Characters: Explore the Future of Interactive Personalities

In the age of artificial intelligence, creating lifelike digital characters is no longer just for tech giants or game developers. With the rise of advanced AI tools, anyone—from storytellers and gamers to developers and marketers—can now create AI-powered characters that can converse, adapt, and even entertain. But among the many tools available, which is the best AI tool to create AI characters?

Let’s dive into the top choices and explore why Janitor AI is creating a buzz in the AI community.

What Are AI Characters?

AI characters are digital personas created using artificial intelligence algorithms. They can:

Chat naturally like humans using natural language processing (NLP)

Adapt to user input and learn from conversations

Embody personalities based on user-defined parameters

Perform roles in games, simulations, marketing, or entertainment

These characters are used across industries—from gaming and education to virtual assistants and storytelling.

Best AI Tools to Create AI Characters

1. Janitor AI

Janitor AI is gaining rapid popularity as one of the most flexible platforms for creating custom AI characters. It allows users to design virtual personas with distinct personalities, interests, and conversational tones.

Key Features of Janitor AI:

Highly customizable character creation

Intuitive dashboard for setting behavior and tone

Integration with powerful language models (like GPT)

Option to create NSFW or safe-for-work bots based on user preferences

Frequently used in roleplay communities and interactive fiction

Janitor AI shines when it comes to user-generated roleplay characters and personal companion bots, giving users control over how their AI behaves and responds.

2. Character.AI

Character.AI is another major player that allows users to interact with AI personalities and even create their own. With a large community and simple creation tools, it’s perfect for storytelling and interactive experiences.

3. Replika

Replika is a well-known AI companion app, focused more on emotional support and friendship. While it may not offer deep character customizability like Janitor AI, it excels in forming relationships and learning user preferences over time.

4. Inworld AI

Inworld AI is built with game developers in mind. It helps create intelligent NPCs (non-playable characters) that can be integrated into game engines like Unity and Unreal. It's ideal for immersive gaming experiences.

5. Botpress & Rasa

For developers wanting more control over logic and integration, open-source tools like Botpress and Rasa offer the backbone for building sophisticated AI characters and chatbots with complete customization.

Why Janitor AI Stands Out

Janitor AI combines simplicity with depth—a rare trait among AI character platforms. Whether you're a casual user creating a fun digital friend, or a writer looking to simulate complex dialogue, Janitor AI offers:

Ease of use: No coding required

Community support: Explore characters created by others

Freedom of expression: From wholesome chats to edgy roleplays

If you're new to AI characters or seeking a highly customizable tool that lets you build personalities from scratch, Janitor AI is an excellent starting point.

Final Thoughts

AI characters are transforming how we interact with technology. From gaming and storytelling to virtual friendships and brand experiences, the tools available today make it easier than ever to build digital personas that feel real.

While there are several platforms out there, Janitor AI continues to attract attention for its balance of user-friendliness, flexibility, and creative freedom. If you're looking for the best AI tool to create AI characters, it's definitely worth exploring.

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braininventoryusa · 5 months ago

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TypeScript Development Services: Elevate Your Web Development Projects

In the fast-paced world of software development, TypeScript has emerged as a game-changer, offering robust features that simplify coding, improve code quality, and enhance collaboration. Whether you’re developing large-scale enterprise applications or dynamic web interfaces, TypeScript Development Services provide the tools and expertise needed to succeed.

What is TypeScript?

TypeScript is a strongly typed programming language developed by Microsoft, built on JavaScript. It offers features like static typing, enhanced code completion, and better tooling support, making it the preferred choice for developers aiming for scalability and maintainability in their projects.

Benefits of TypeScript Development Services

Enhanced Code Quality: With static typing, developers can identify errors at compile time, reducing bugs and runtime errors.

Improved Collaboration: TypeScript’s clear and structured syntax enhances team collaboration, especially on large projects.

Seamless Integration: TypeScript integrates seamlessly with popular frameworks and libraries, including Angular, React, and Node.js.

Future-Ready Solutions: Its compatibility with JavaScript ensures future-proof applications that are easy to maintain and upgrade.

Why Choose TypeScript for Your Next Project?

If you’re looking to build scalable, maintainable, and high-performance applications, TypeScript is a top choice. From startups to established enterprises, businesses worldwide leverage TypeScript to accelerate their development cycles and deliver reliable software solutions.

Complementary Development Services

To maximize the potential of your projects, consider combining TypeScript development with specialized development services. Here’s how:

Hire Dedicated MEAN Stack Developers

The MEAN stack—MongoDB, Express.js, Angular, and Node.js—is a powerful framework for building dynamic web applications. By hire dedicated MEAN stack developers, you can:

Leverage expertise in full-stack development.

Ensure seamless integration of TypeScript with Angular and Node.js.

Accelerate project timelines with agile methodologies.

Hire Dedicated Chatbot Developers

Chatbots are transforming customer engagement across industries. With hire dedicated chatbot developers, you can:

Build intelligent, conversational AI solutions using frameworks like Botpress and Microsoft Bot Framework.

Integrate chatbots seamlessly into your web applications developed with TypeScript.

Enhance user experience with personalized and responsive interactions.

Hire Dedicated Next.js Developers

Next.js is a cutting-edge framework for building server-rendered React applications. By hiring dedicated Next.js developers, you can:

Develop highly optimized, SEO-friendly web applications.

Combine the power of Next.js and TypeScript for scalable, high-performance solutions.

Stay ahead of the competition with modern web development practices.

Why Partner with a Professional Development Company?

When you partner with a professional development company offering TypeScript Development Services, you gain access to a team of skilled developers who understand the intricacies of modern web development. These professionals can:

Deliver custom solutions tailored to your business needs.

Ensure best practices in coding and deployment.

Provide ongoing support and maintenance for your applications.

Conclusion

TypeScript Development Services empower businesses to build robust, scalable, and efficient applications. By integrating TypeScript with complementary technologies like MEAN stack, chatbots, and Next.js, you can achieve unparalleled success in your projects. Ready to take your web development to the next level? Hire dedicated MEAN stack developers, chatbot developers, and Next.js developers today and bring your vision to life.

#typescript development company #typescript development services

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karanchadda · 7 months ago

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What are Open Source Chatbot Tools for AI Chatbot Development?

Several open-source chatbot tools are available for ai chatbot development, including Botpenguin, Botpress, Rasa, Dialogflow, and ChatterBot. These tools provide various characteristics, like natural language processing, machine learning, and conversation management.

Botpenguin is a popular open-source chatbot tool with a drag-and-drop interface for creating chatbots. It also supports NLP and ML algorithms, making it a good choice for AI-based chatbots.

It is important to consider all the features and capabilities needed for your project and the technical ability necessary to use the tool when selecting an open-source platform for chatbot creation.

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aiagent · 7 months ago

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Essential Skills Every Chatbot Developer Should Master

Chatbots have become integral to modern businesses, revolutionizing customer service, e-commerce, healthcare, and many other industries. As conversational AI technology continues to advance, the demand for skilled chatbot developers has skyrocketed.

If you're considering a career in chatbot development, it’s essential to build a robust set of skills. Below, we’ll explore the core skills every chatbot developer should master to succeed in this exciting and dynamic field.

Programming Languages and Frameworks

The backbone of any chatbot is the code that drives its behavior. While there are various platforms available to simplify chatbot development, a chatbot developer should still have a solid grasp of key programming languages and frameworks.

Python: Python is the most popular language for developing chatbots due to its simplicity and the vast number of libraries and frameworks available. Libraries like NLTK (Natural Language Toolkit) and spaCy are invaluable for natural language processing (NLP), while Flask and Django can help with building the backend for chatbots.

JavaScript/Node.js: JavaScript is the language of the web, and many chatbot applications are deployed on websites. Node.js allows developers to write server-side code in JavaScript, making it an essential tool for building web-based chatbots.

Java: Java is another widely used language in enterprise chatbot development, especially in systems requiring high scalability. Developers often use Java with frameworks like Spring Boot to develop robust, secure, and scalable chatbot solutions.

Chatbot Frameworks: Platforms like Microsoft Bot Framework, Dialogflow, Rasa, and Botpress provide pre-built components and tools to speed up development. Familiarity with these frameworks is crucial for building efficient, maintainable bots.

Natural Language Processing (NLP)

NLP is the core technology behind chatbots, enabling them to understand and generate human language. Chatbot developers need to understand NLP principles to create intelligent bots capable of understanding context, processing queries, and providing meaningful responses.

Text Preprocessing: Cleaning and structuring text data is essential for chatbot accuracy. Developers must master techniques like tokenization, lemmatization, stemming, and removing stop words to prepare text for analysis.

Intent Recognition: One of the most important tasks in NLP is identifying user intent. Developers need to train chatbots to understand a variety of expressions, meaning the bot can interpret the user’s purpose behind the message. Tools like Dialogflow or Rasa provide intent recognition features, but understanding how these tools work under the hood is critical.

Entity Recognition: Entities are key pieces of information that chatbots must extract from user queries, such as dates, locations, or product names. Mastery of named entity recognition (NER) techniques allows chatbots to extract this information accurately.

Context Management: To build a conversational chatbot that can handle complex conversations, developers must manage context—tracking user inputs and maintaining the flow of the conversation. This can involve implementing memory features or utilizing frameworks that allow for multi-turn conversations.

Machine Learning and Deep Learning

Chatbots that incorporate machine learning (ML) and deep learning (DL) can evolve over time, improving their responses based on user interactions. For more advanced chatbots, developers should have an understanding of ML algorithms and DL models to enhance their bots’ capabilities.

Supervised and Unsupervised Learning: By applying ML techniques, developers can train chatbots to predict user behavior, identify patterns in interactions, and improve the bot’s performance based on the data gathered. Supervised learning techniques like classification (for intent recognition) are especially useful in chatbot development.

Reinforcement Learning: This method can help chatbots improve by learning from interactions. In reinforcement learning, a chatbot gets rewarded or penalized based on its actions, allowing it to fine-tune responses over time.

Deep Neural Networks (DNN): For highly complex tasks like sentiment analysis, text generation, or speech recognition, understanding deep learning models, such as recurrent neural networks (RNNs) and transformers, can be advantageous.

Understanding of Conversational UX/UI Design

Building a chatbot isn’t just about writing code—it’s also about creating an engaging user experience (UX). Developers must understand conversational design principles to ensure that their bots are user-friendly and easy to interact with.

Natural Flow: Chatbots should simulate human-like conversation. This requires an understanding of turn-taking, appropriate response times, and the ability to handle unexpected user inputs.

Personalization: Personalized chatbots that remember user preferences and provide relevant responses can significantly improve user engagement. Developing personalized user experiences requires integrating the chatbot with databases and systems to track user history.

Error Handling: A critical part of UX design is ensuring that the bot gracefully handles errors. If a chatbot doesn’t understand a query or encounters an issue, it should respond in a way that minimizes user frustration, perhaps by offering clarification or suggesting alternative queries.

APIs and Integrations

Chatbots often need to interact with other systems to provide useful responses or perform actions. Whether it’s pulling data from a third-party API, querying a database, or interacting with other platforms like Facebook Messenger or Slack, API integration is an essential skill.

RESTful APIs: Knowledge of how to work with RESTful APIs is essential, as many chatbots will need to retrieve data from external sources or send data to different platforms. For example, a chatbot on a website may need to access inventory data from an e-commerce platform.

OAuth and Security: Understanding authentication protocols like OAuth ensures that your chatbot can securely access external services without exposing sensitive user data.

Cloud Platforms and Hosting

Cloud platforms like AWS, Google Cloud, and Microsoft Azure are often used for hosting chatbots, especially for those that require scalability. Mastery of these platforms is essential to ensure a smooth deployment and maintain the chatbot’s performance.

Serverless Computing: Serverless architectures (e.g., AWS Lambda) can help chatbot developers scale their applications without managing servers, allowing for efficient and cost-effective resource allocation.

Containerization: Knowledge of container technologies like Docker is useful for packaging and deploying chatbot applications across various environments.

Data Privacy and Security

Data privacy is a critical concern, especially with chatbots handling sensitive user information. A chatbot developer must have a thorough understanding of data protection regulations (such as GDPR) and how to ensure that chatbots comply with these rules.

Encryption: Developers should implement encryption techniques to protect user data during interactions and ensure secure communication between the user and the bot.

User Consent: Chatbot developers should ensure their bots request user consent to collect personal information, and they should be transparent about how data is used and stored.

Continuous Testing and Optimization

Testing is a crucial part of the chatbot development process. Developers need to consistently evaluate their chatbots' performance and make necessary improvements. Key areas of focus include:

User Testing: Conducting user tests is essential to understand how the bot is performing in real-world scenarios. This involves monitoring conversations, identifying pain points, and fine-tuning the bot’s responses.

Performance Optimization: As a chatbot scales and interacts with more users, optimizing its performance becomes crucial. This includes improving response times, enhancing accuracy, and ensuring that the system remains stable under heavy traffic.

Analytics: Leveraging chatbot analytics allows developers to monitor user engagement, identify drop-off points, and optimize the chatbot’s flow and effectiveness.

Conclusion

Becoming a proficient chatbot developer requires a diverse skill set that spans programming, machine learning, natural language processing, UX/UI design, and more. By mastering these essential skills, developers can create intelligent, engaging, and secure chatbot development capable of transforming user experiences and driving business success. Whether you’re just starting in the field or are an experienced developer looking to enhance your abilities, continuing to learn and adapt to new technologies will keep you at the forefront of the chatbot revolution.

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ardhra2000 · 1 year ago

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AI Chatbots

Contextual chatbots are designed to maintain context throughout a conversation. They can remember previous interactions with a user and use that information to provide more personalized assistance. These chatbots are particularly useful in customer service and support scenarios.

AI chatbots can provide real-time analytics and insights into customer behavior and preferences. This includes data on customer inquiries, interactions, and feedback.

By analyzing this data, businesses can gain valuable insights into customer needs and preferences, allowing them to improve their products and services to better meet customer expectations.

AI chatbots can be used in e-commerce to handle product recommendations, shopping cart management, and order tracking.

To generate accurate responses, the H&M chatbot uses machine learning algorithms that are trained on thousands of conversations. Over time, the chatbot learns from these conversations and improves its responses based on user feedback.

AI chatbots can provide 24/7 customer support, improve response times, handle multiple customer inquiries simultaneously, and offer personalized experiences, leading to increased customer satisfaction and loyalty.

Businesses can create AI chatbots using chatbot development platforms such as Dialogflow, Botpress, and IBM Watson, which offer tools and resources to build, test, and deploy chatbots with natural language processing and machine learning capabilities.

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webapp358 · 4 days ago

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Botpress vs Dialogflow: Which AI Chatbot Platform Is Better in 2025?

This blog compares Botpress and Dialogflow to determine which AI chatbot platform is better in 2025. It explores features, flexibility, and why Botpress AI chatbot development stands out for custom automation and scalability.

#Botpress Chatbot #Botpress #Botpress development

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leonfrancisblog · 4 years ago

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Europe Conversational Computing Platform Market Industry Analysis Size, Share, Trends and Profitable Segments Breakdown and Detailed Analysis of Current and Future Industry Figures till 2026|Key Players Alphabet Inc. (Google), IBM Corporation, Microsoft, Nuance Communications, Inc., Tresm Labs, Apexchat

Conversational computing platform market competitive landscape provides details by competitor. Details included are company overview, company financials, revenue generated, market potential, investment in research and development, new market initiatives, Europe presence, production sites and facilities, company strengths and weaknesses, product launch, product trials pipelines, product approvals, patents, product width and breath, application dominance, technology lifeline curve. The below data points provided are only related to the company’s focus related to Europe conversational computing platform market. This conversational computing platform market report provides details of market share, new developments, and product pipeline analysis, impact of domestic and localised market players, analyses opportunities in terms of emerging revenue pockets, changes in market regulations, product approvals, strategic decisions, product launches, geographic expansions, and technological innovations in the market. To understand the analysis and the market scenario contact us for an Analyst Brief, our team will help you create a revenue impact solution to achieve your desired goal.

Chabot’s are user interface of conversational platforms and its related assistants, where conversational platforms enable chatbots to operate and decode the natural language. SMS, social media and other interactive platforms are integrated in these conversational platforms. APIs (application programming interfaces) are provided by conversational platform so as to integrate other interactive platforms. The growing utilization of Chabot in the E-commerce sector is prominent factor drive the growth of the market. For instance the Germany based healthy food supermarket chain had introduced a chat bot that utilize for finding super market easy. Thus the utilization of Chabot will contribute in improving customer services. This factor will in turn increase the customer base for the company.

Europe conversational computing platform market By Type (Solution, Service), Technology (Natural Language Processing, Natura Language Understanding, Machine Learning and Deep Learning, Automated Speech Recognition), Deployment Type (Cloud, On-Premise), Application (Customer Support, Personal Assistance, Branding and Advertisement, Customer Engagement and Retention, Booking Travel Arrangements, Onboarding and Employee Engagement, Data Privacy and Compliance, Others), Vertical (Banking, Financial Services, and Insurance, Retail and Ecommerce, Healthcare and Life Sciences, Telecom, Media and Entertainment, Travel and Hospitality, Others), Country (Germany, Italy, U.K., France, Spain, Netherlands, Belgium, Switzerland, Turkey, Russia, Rest of Europe), Market Trends and Forecast to 2027. Conversational computing platform market is expected to gain market growth in the forecast period of 2020 to 2027. Data Bridge Market Research analyses that the market is growing with a CAGR of 31.7% in the forecast period of 2020 to 2027. Growing expansion of application base of AI solution in the various vertical is expected to drive growth of the market Conversational computing platform can be defines as platform where computer interact with human either with text or voice. The platform use artificial intelligence tool for processing language. For instance Chabot is conversational computing platform that widely used in all the sector for helping customer.

Get More Info Sample Request on Europe conversational computing platform market @ https://www.databridgemarketresearch.com/request-a-sample/?dbmr=europe-conversational-computing-platform-market

Conversational Computing Platform Market Country Level Analysis:

Europe conversational computing platform market is analyzed and market size information is provided by country by type, technology, deployment type, application, and vertical as referenced above. The countries covered in Europe conversational computing platform market report are Germany, France, U.K., Italy, Spain, Poland, Ireland, Denmark, Austria, Sweden, Finland, rest of Europe

Growing Concern of Business towards Minimizing Operational Cost of the Business:

Conversational computing platform market also provides you with detailed market analysis for every country growth in cloud based industry with conversational computing platform sales, services, impact of technological development in software and changes in regulatory scenarios with their support for the conversational computing platform market. The data is available for historic period 2010 to 2018.

Europe Conversational Computing Platform Market Scope and Market Size:

Europe conversational computing platform market is segmented on the basis of type, technology, deployment type, application, and vertical. The growth among segments helps you analyse niche pockets of growth and strategies to approach the market and determine your core application areas and the difference in your target markets. On the basis of type, the market is segmented into solution and services. The solution segment accounted largest market share is due to growing concern of business towards improving customer experience has increase the adoption of various solution in the business such as virtual assistant, Chabot and many more. On the basis of technology, the market is segmented into natural language processing, natural language understanding, machine learning and deep learning, automated speech recognition. Natural language processing segment is dominating the market while machine learning and deep learning are expected to grow with highest CAGR for forecasted of 2027. The growing utilization of artificial intelligence in the finance sector for solving complex problem. For instance Ayasdi had created the cloud-based and on- premise machine intelligence solutions for business to solve the complex problem. The deployment of this solution allows the finance sector to control all the fraud case associated with money.

The major players covered in the report are Alphabet Inc. (Google), IBM Corporation, Microsoft, Nuance Communications, Inc., Tresm Labs, Apexchat, Artificial Solutions, Conversica, Inc., Haptik, Inc., Rulai, Cognizant, PolyAI Ltd., Avaamo, SAP SE, Cognigy GmbH, Botpress, Inc., 42Chat, Accenture, Amazon.com, Inc., Oracle, Omilia Natural Language Solutions Ltd, among other players domestic and Europe. Conversational computing platform market share data is available for Europe, North America, Europe, Asia-Pacific, Middle East and Africa and South America separately. DBMR analysts understand competitive strengths and provide competitive analysis for each competitor separately.

Customization Available: Europe Conversational Computing Platform Market:

Data Bridge Market Research is a leader in advanced formative research. We take pride in servicing our existing and new customers with data and analysis that match and suits their goal. The report can be customized to include price trend analysis of target brands understanding the market for additional countries (ask for the list of countries), clinical trial results data, literature review, refurbished market and product base analysis. Market analysis of target competitors can be analyzed from technology-based analysis to market portfolio strategies. We can add as many competitors that you require data about in the format and data style you are looking for. Our team of analysts can also provide you data in crude raw excel files pivot tables (Factbook) or can assist you in creating presentations from the data sets available in the report.

Get Table of Content on Request @ https://www.databridgemarketresearch.com/toc/?dbmr=europe-conversational-computing-platform-market

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Browse Related Report:

Asia-Pacific Conversational Computing Platform Market

Middle East and Africa Conversational Computing Platform Market

North America Conversational Computing Platform Market

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ehteshamuniverse · 5 years ago

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Conversational Computing Platform Market Emerging Trends, Opportunities and Growth Analysis to 2025 | Impact of Corona-Virus

Market Highlights

With the surge in COVID-19 impact despite lockdowns along with social distancing measures, academia, governments, along with tech giants are developing solutions that can help reduce the pandemic aftereffects. While the world is facing economic crisis, the demand for next gen technologies such as conversational computing platforms has risen considerably as a response to the SARS-CoV-2.

Conversational computing platforms, especially chatbots, are being used by organizations as an interactive medium for several applications, such as offering updates and information about novel coronavirus, facilitating communication within firms, and more. The efforts being put to achieve a COVID-19 breakthrough can speed up with the help of conversational computing platforms, backed by various modern technologies in the healthcare industry.

Following the enforcement of lockdowns due to the pandemic, tech giants are doing everything they can to mitigate the negative impact of COVID-19, while boosting the capabilities of their conversational computing platforms. Given the widespread demand, more and more tech giants are now investing considerably in their conversational computing platforms, which can help the market stay resilient in the face of COVID-19.

As per the analysis by Market Research Future Reports (MRFR), the global conversational computing platform market is likely to expand significantly during the forecast period from 2019 to 2025. The global conversational computing platform market 2020 is experiencing a high growth because of the increasing demand from various sectors, which are travel & hospitality, I.T. & telecommunications, BFSI, entertainment & media, and e-commerce & retail. The rapid adoption in digital transformation and the usage of the latest technologies have resulted to the expansion of the market. The conversational computing platform is being adopted at a very pace because it offers a transparent and advanced conversation between the A.I. and the customer. Moreover, digital marketing managers are using AI-based technology to locate their potential targets for marketing campaigns. Apart from this, the conversational computing platform is mostly adopted by the media and entertainment sector for advertising, information dissemination, product discovery, promotions, and other applications.

Key Drivers and Primary Deterrents

The strong online presence of a number of companies along with the surge in digital methods of staying in touch with customers has boosted the value of chatbots. Chatbots are increasingly being deployed to address all types of queries of the website users. Chatbots also help boost the marketing strategies that are employed by organizations; therefore, the rising use of advanced chatbots for various purposes is anticipated to induce growth of the conversational computing platform market in the approaching years.

Virtual digital assistants are also in great demand, finding extensive use in the management of smart home devices to help understand and respond to simple as well as complex commands. The favorable scenario has encouraged several players to introduce smartphones and smart speakers that feature virtual digital assistants. With this, these players are not only able to expand their customer base and boost their profits but also benefit the conversational computing platform market in the process.

Segmentation:

The Global Conversational Computing Platform Market Analysis can be classified on the basis of Vertical, Application, Deployment Type, Type, Technology, and Region.

On the basis of vertical, the Global Conversational Computing Platform Market can be classified into banking, retail & e-commerce, financial services & insurance (BFSI), entertainment & media, telecom, travel & hospitality, and others.

On the basis of application, the Global Conversational Computing Platform Market can be classified into personal assistance, customer support, branding & advertisement, booking travel arrangements, customer engagement & retention, data privacy & compliance, onboarding & employee engagement, and others.

On the basis of deployment type, the Global Conversational Computing Platform Market can be classified into on-premise and cloud.

On the basis of type, the Global Conversational Computing Platform Market can be classified into service and solution.

On the basis of technology, the Global Conversational Computing Platform Market can be classified into automated speech recognition, natural language understanding, natural language processing, and machine learning & deep learning.

On the basis of region, the Global Conversational Computing Platform Market can be classified into North America, Europe, Asia-Pacific, the Middle East & Africa, and South America.

Regional Insight

The key regions across which the market is projected to expand during the assessment period include Europe, North America, the Middle East & Africa/MEA, South America and Asia Pacific/APAC.

North America has been the biggest gainer in the global market, standing on the shoulders of highly celebrated players like IBM Corporation, Microsoft Corporation, Nuance Communications, Inc., Apexchat, Amazon.com, Inc., Conversica, Inc., Cognizant, Oracle, Alphabet Inc., to name a few. In addition to these players, a number of start-ups with expertise in advanced conversational computing platform solutions are expanding their presence in the region. The United States (US) ranks first in the region, thanks to the high uptake of digitization; and the widespread deployment of the latest technologies such as analytics and big data as well as the rising adoption of cloud-based solutions.

The European market is broadly segmented into Germany, the UK and France. It is the second-most profitable market on a global scale, with the UK in the lead and Germany presumed to clinch the fastest growth rate in the years to follow. The region holds a lot of attractive opportunities including AI-based conversational platforms that are finding extensive use among leading graph conversational computing firms of the region.

APAC has the potential to be the fastest expanding market during the conjectured timeline, and Japan, India and China at the vanguard. The fast growing economy of the countries along with the rapid digital transformation across enterprises is proving to be significant promoters in the regional market. The conversational computing solutions and services are being increasingly deployed by the IT and telecom and BFSI industries in the region, which can also boost the market profits in the long run.

The MEA and South American market can rise steadily in the next several years, because of the surge in optimized and efficient business conversational needs. Various enterprises in BFSI, and retail & e-commerce and telecommunication & IT verticals are fast adopting these solutions, which can emerge as a significant opportunity for the leading players in the region.

Significant Players

Significant players that are collaborating and entering into partnerships to gain a bigger share in the conversational computing platform market are Alphabet, Inc. (US), Microsoft Corporation (US), Amazon.com, Inc. (US), IBM Corporation (US), Cognigy GmbH (Germany), Conversica, Inc. (US), Jio Haptik Technologies Limited (India), Accenture (Ireland), Artificial Solutions (Spain), Botpress, Inc. (Canada), Oracle (US), Omilia Natural Language Solutions Ltd. (Cyprus), Apexchat (US), Nuance Communications, Inc. (US), Cognizant (US), to mention a few.

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manantrivedi · 5 years ago

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8 Chatbot Development Frameworks: Building a Better Bot for Your Business

There has been an explosion in the use of chatbots across both business websites and messaging applications, mainly because businesses want to cater to their customers and customers have a lot of queries that need to be answered. Managing these queries is difficult and cannot be done on a 24/7 basis unless you have a rotating team. One way to cut down operation costs and still provide a personalized customer experience is with chatbots. So, when it comes to the numerous chatbot development frameworks, knowing which one is right for your business can be a bit of a conundrum. This is why we have compiled a list of the most popular chatbot development frameworks that can help you build intelligent, adaptable, and productive chatbots. Whichever platform you choose, you will get a chatbot that is cost-effective, scales as you grow, and provides a personalized customer experience.

Which Platforms Are The Best, moving into 2020?

Microsoft Bot Framework – Build & Connect Intelligent Chatbots: The Microsoft Bot Framework that is used around the world by developers looking to build secure, scalable, solutions that integrate with current information technology ecosystems. The idea behind it is to help enterprises extend or expand their brand without losing control over data ownership. It is a rich framework that allows developers to develop, publish, and manage their bots all in one place, as it comes with two major components. First, the platform offers channel connectors, allowing you to connect the chatbot to messaging channels, and second, it comes with SDKs for implementing business logic into your conversations. Pros include pre-built options, machine learning speech to text implementation, is multilingual, has technical computer support, and works in multiple computer languages. The one con is that you have to choose to develop your chatbot in C# or Node.Js. can integrate with popular messaging applications like Facebook, Messenger, Slack, Skype, Cortana, and even websites.

Wit.AI – An NLP That’s Free to Use: The Wit.ai chatbot development framework is free to use, even for commercial entities, is open-source, and leverages community-based input to better the platform. While it is under Facebook’s branding, it started out as a Y Combinator Startup, which is an American seed accelerator company that invests funding into small companies. Due to the bot being open-source, over 200,000 developers have used it, allowing new developers to create chatbots with human-level interaction and intelligence. A lot of time is saved this way as the basics of human conversations do not need to be taught. Pros include being open source, has an incredible natural language processing engine, offers SDKs for IOS, Python, Ruby, and Node.Js, and supports over 80 languages. Plus, due to it being owned by Facebook, it is easily deployable on Facebook Messenger. The con with it is that some developers find that missing parameters are hard to retrieve. Can be integrated into any application, any website, Facebook Messenger, into home automation systems, into wearable devices and Slack.

DialogFlow – For Conversational Bots. The DialogFlow chatbot development framework is designed specifically around conversations, allowing developers to create highly intelligent chatbots and voice applications that can grasp the nuances of language. Over time, these chatbots continue to improve because they are supported by Google’s Cloud Natural Language, making it very easy for developers to train the chatbot to understand the finer details of human conversations. Yes, this includes human emotions and their connecting sentiments. With DialogFlow being a subsidiary of Google, it is built on Google’s infrastructure, allowing you to scale to millions of users and build actions for more than 400 million Google Assistant devices. Pros include the framework supporting voice and text-based assistants, is easy to learn from a development standpoint, provides rich conversations, has SDKs for 14 platforms, supports 20+ languages, has an in-line editor, provides sentiment analysis, and can even be programmed to carry out jokes, event searches, and payment handling. It has IoT integration for home automation as well. The con is that programmers do not have access to control over dialogue processing. Can integrate with Google Assistant, Facebook Messenger, Cortana, Kik, Skype, Telegram, Viber, Alexa, Slack and more.

IBM Watson – Perfect for Internal Use: The IBM Watson chatbot development framework is industry-leading, well-known, and one of the best platforms to use if you want to develop a retail, banking, Slack or voice-enabled Android chatbot. The platform comes with pre-configured content for customer care, banking, eCommerce, and utility content, making it extremely flexible. It is built on a neural network that is comprised of one billion words from Wikipedia and it uses machine learning to respond naturally to human queries. Pros include a highly advanced machine learning engine, automated predictive analysis, a Watson GUI for non-technical users, development can be stored on a private cloud, it comes with visual recognition security, supports 10 languages and has a built-in translator, and comes with a tone analyzer for understanding negative and positive responses. The con is that it can be a bit confusing to use if you are looking to create a very simple, non-AI powered chatbot, due to the number of tools available on the platform. Can integrate with WordPress websites, Intercom, Slack, and Facebook Messenger.

WordPress – A Module Based Option: The BotPress chatbot development framework takes quite a different approach in that it doesn’t require developers to implement their own dialogue manager, channels, or natural language understanding process because it comes with them all. This platform was built by developers as an open-source option with a user-interface so that non-technical individuals can manage the chatbots after they are deployed. It works on a module system which makes it fully customizable, and comes with a conversational flow management system, an NLU, actionable analytics, an authoring UI, and is multichannel. It can integrate with platforms like Skype, Telegram, Twilio, BotFrameWork, WebChat, Facebook Messenger, and SMS.

Rasa Stack – A Python-based Platform: The Rasa Stack framework is for developers, companies, and businesses that require contextual-based chatbots that can answer, understand, and execute on contextual circumstances. This platform is used widely in large companies within the banking sector, the sports industry, with job recruitment, and healthcare providers. Rasa is open source, automated text and voice assistants, and is made up of two major components. The first is the Rasa NLU which is their natural language processing engine, and the second is the Rasa Core, which uses intents and entities to understand queries. The pros of Rasa Stack are that it can manage contextual dialogues, can recognize intent, provides full data control, and allows you to create custom models. It can be integrated with Rocket. Chat, Slack, Twilio, Facebook Messenger, and Telegram.

ChatterBot – Based on Adaptability: If you are looking for a chatbot that can be trained in any desired language, ChatterBot is a fantastic option. It is powered by Node.Js and works by creating a Python library. While this chatbot will start off with no knowledge of how to communicate and with every human query, the chatbot saves the text that was entered and the text that the statement was issued for. The more input there is, the more accurate each response becomes as the chatbot learns how to communicate. Essentially, the chatbot will always choose the closest matching response by searching for the closest matching statement within its library and then returns the most likely response back based on the statement. Or in short, learns to communicate based on a collection of conversations in combination with machine learning. This is a good option for developers that need a bot to adapt based on conversation and continuous learning.

Amazon Lex. The Amazon Lex chatbot development platform is a part of the Amazon Web Services and comes with sophisticated bot-building tools. Like a few other platforms, it comes with built-in natural language understanding, machine learning, and numerous SDKs for different platforms. It allows the developer to input automated speech recognition that can be converted into text, can integrate with other Amazon Web Services and is free to use. Unfortunately, it is only available in American English at this time.

While all of these

chatbot development platforms

have their use-cases, it is important to note that the first few that you try may not be the right fit, as you will need to use one that best suits the kind of business that you have. If you have any questions about any of the above

chatbot development frameworks

or believe that one of these frameworks would work well for your business, please feel free to open up a conversation with us. Here at Lets Nurture, we build intelligent, conversational chatbots that help serve your customers around the globe with a personalized and tailored experience. The end result is a chatbot that can uplift your day-to-day operations, leaving you with more room to attend to critical business matters, while still providing excellent customer care. If you’d like to get in touch with us about an idea or with questions, please contact us

or chat us up at +1-902-620-9098 . We’d love to help with your next project!

For more info kindly visit us at www.letsnurture.com, feel free to contact us

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harshaddmr · 5 years ago

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Europe Conversational Computing Platform Market: Pin-Point Analysis For Changing Competitive Dynamics 2027

Know More - https://www.databridgemarketresearch.com/reports/europe-conversational-computing-platform-market

Market Analysis and Insights: Europe Conversational Computing Platform Market

Conversational computing platform market is expected to gain market growth in the forecast period of 2020 to 2027. Data Bridge Market Research analyses that the market is growing with a CAGR of 31.7% in the forecast period of 2020 to 2027. Growing expansion of application base of AI solution in the various vertical is expected to drive growth of the market

The growing utilization of Chabot in the E-commerce sector is prominent factor drive the growth of the market. For instance the Germany based healthy food supermarket chain had introduced a chat bot that utilize for finding super market easy. Thus the utilization of Chabot will contribute in improving customer services. This factor will in turn increase the customer base for the company.

This conversational computing platform market report provides details of market share, new developments, and product pipeline analysis, impact of domestic and localised market players, analyses opportunities in terms of emerging revenue pockets, changes in market regulations, product approvals, strategic decisions, product launches, geographic expansions, and technological innovations in the market. To understand the analysis and the market scenario contact us for an Analyst Brief, our team will help you create a revenue impact solution to achieve your desired goal.

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Europe Conversational Computing Platform Market Scope and Market Size

On the basis of type, the market is segmented into solution and services. The solution segment accounted largest market share is due to growing concern of business towards improving customer experience has increase the adoption of various solution in the business such as virtual assistant, Chabot and many more.

On the basis of technology, the market is segmented into natural language processing, natural language understanding, machine learning and deep learning, automated speech recognition. Natural language processing segment is dominating the market while machine learning and deep learning are expected to grow with highest CAGR for forecasted of 2027. The growing utilization of artificial intelligence in the finance sector for solving complex problem. For instance Ayasdi had created the cloud-based and on- premise machine intelligence solutions for business to solve the complex problem. The deployment of this solution allows the finance sector to control all the fraud case associated with money.

On the basis of deployment type, the market is segmented into cloud and on-premise. Cloud accounted largest market share as it cost effective compare to the On-premise. The major benefit of using cloud is that it is saves company administrative cost. For instance it has witness that by 45% of the enterprise will prefer to store data in cloud

On the basis of application, the market is segmented into personal assistance, branding and advertisement, data privacy and compliance, customer engagement and retention, customer support, onboarding and employee engagement, booking travel arrangements, others). Personal assistance accounted largest market share growing utilization of personal assistant software in the finance sector allows the finance sector to enhance their customer services.

On the basis of vertical, the market is segmented into banking, financial services, and insurance, retail and ecommerce, healthcare and life sciences, telecom, media and entertainment, travel and hospitality, others. Retail & Ecommerce segment account largest market share due to growing adoption of AI tools in the retail sector allow the sector to improve their customer services. This factor will in turn increase customer base for the company.

Conversational Computing Platform Market Country Level Analysis

Europe conversational computing platform market is analysed and market size information is provided by country by type, technology, deployment type, application, and vertical as referenced above.

The countries covered in Europe conversational computing platform market report are Germany, France, U.K., Italy, Spain, Poland, Ireland, Denmark, Austria, Sweden, Finland, rest of Europe

Growing Concern of Business towards Minimizing Operational Cost of the Business

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Competitive Landscape and Conversational Computing Platform Market Share Analysis

Conversational computing platform market competitive landscape provides details by competitor. details included are company overview, company financials, revenue generated, market potential, investment in research and development, new market initiatives, Europe presence, production sites and facilities, company strengths and weaknesses, product launch, product trials pipelines, product approvals, patents, product width and breath, application dominance, technology lifeline curve. The above data points provided are only related to the company’s focus related to Europe conversational computing platform market.

Many joint ventures and developments are also initiated by the companies worldwide which are also accelerating the global conversational computing platform market.

For instance,

In March 2019, PolyAI Ltd. had raised USD 12 million funding for series A. This investment will help the company to develop more sophisticated AI technology for its customers which in turn enhance the product portfolio for the company.

The company market share with increased coverage and presence. It also provides the benefit for organization to improve their offering for conversational computing platforms through expanded model range.

About Us: Data Bridge Market Research set forth itself as an unconventional and neoteric Market research and consulting firm with unparalleled level of resilience and integrated approaches. We are determined to unearth the best market opportunities and foster efficient information for your business to thrive in the market. Data Bridge Market Research provides appropriate solutions to the complex business challenges and initiates an effortless decision-making process.

Contact: Data Bridge Market Research Tel: +1–888–387–2818 Email: [email protected]

#Europe Conversational Computing Platform Market Global Europe Conversational Computing Platform Market Europe Conversational Computing Platf

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endenogatai · 6 years ago

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Hubtype raises $1.1M to help developers build richer chat support

Barcelona-based Hubtype has raised a €1 million (~$1.1M) seed round led by Madrid-based early stage VC firm, K Fund. The team last raised when the business was founded, back in February 2016, when they took in €235,000 in a mix of public and angel funding.

Hubtype targets enterprises and developers with customer service focused tools to help build and scale what it describes as “conversational messaging experiences” — aka messaging interfaces that go beyond more basic chatbot-style offerings to support richer interactions and ‘smart’ automation, such as knowing when to hand off to a human agent.

“We know very well that chatbots aren’t enough on their own, as we’ve been building bots for three years. To provide effective and meaningful interactions, companies need to go beyond bots and provide conversational experiences: Micro-apps within the messaging channels that everyone uses daily,” say co-founders, Eric Marcos and Marc Caballé, explaining the wider context around the space as they see it.

“Conversational experiences take the best of chat (conversational user interfaces) and combine elements of graphical user interfaces like websites, apps, etc. Effective ones aggregate AI, decision trees, webviews, human agent hand-off and more. Furthermore, enterprise companies need integration with other APIs and systems, from back-end inventory and order tracking to booking engines, analytics, NLP services and more.”

They argue that orchestrating all these different elements can be “extremely difficult and time-consuming” for businesses lacking a dedicated tech team to handle building and maintaining smooth chat-based customer interactions.

That’s where Hubtype sees an opportunity to elbow in, starting with a b2b focus but aiming to tilt fully at developers over the long term.

Hubtype’s opensource framework for building conversational apps, called Botonic, is based on React.js. Using this it claims a single developer can build, deploy and scale conversational apps across multiple messaging channels (including webchat) — doing away with the need for a full dev team to build and maintain everything.

The team’s goal is to become “the reference platform” for developers to create conversational apps using React.js. Some of the seed funding is therefore pegged for building out Hubtype’s developer relationship program, as well as ploughing into product development and scaling the sales team.

“We’re currently a b2b company and our target customers are enterprise-level companies mainly in banking, insurance and e-commerce/retail,” the co-founders note, adding: “Eight out of more than 20 customers are in the Forbes Global 2000 List, with some ranking in the top 20 such as Volkswagen, Inditex, HP, and Bankia.”

“With this funding round we’re investing in further developments of our framework, including AI capabilities which will allow clients to train their chatbot in one language and roll out automatically in about 100 languages. We’ll also be building our developer relationship program and scaling our sales team,” adds Caballé in a statement.

Hubtype tells us it expects to reach 100 customers this year — though they’re not disclosing exact customer numbers yet.

“We have a strong presence in the Spanish enterprise ecosystem and within international brands that operate in Europe. We provide our service globally but we’re currently focused on the EU and testing some emerging markets where WhatsApp is prevalent, as we are one of the few official solution providers for the platform globally,” the co-founders add.

Asked about the competitive landscape, Hubtype names Accel-backed Rasa as an “AI centric” bot-builder framework rival with a similar “bottom up” focus on marshalling developers to build adoption.

Another competitor the co-founders point to is Botpress, saying it has a somewhat similar approach while flagging a different business model (focused on “consulting/services centric”).

Microsoft Bot Framework and Dialogflow are two other rival frameworks they name — but again the suggestion is both are AI centric, rather than supporting a richer mix of conversational components.

“The difference between us and our closest competition is that we have a very clear niche (React developers) and we are pioneers in advocating for conversational apps (text+GUI and using NLP and AI as elements) rather than AI or NLP-centric experiences. Most of our competitors are focused on AI and NLP,” they add.

“Our tools focus on building applications that sit at the intersection between text-based and graphic interfaces. We take into account NLP, AI, interactive messages, webviews, managing context, human handoffs and multichannel integrations. Additionally, we aggregate more messaging channels than all or most competitors.”

Commenting on the seed raise in a statement, Jaime Novoa, associate at K Fund, added: “The chatbot industry has undergone a major transformation from text to conversational apps, and Hubtype is leading enterprise companies to build the best customer experiences in a scalable way by using automation. Companies must move from traditional phone and email communication and into a new era of multichannel conversational messaging. Hubtype is an important addition to our investment portfolio, and timing is key.”

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tortuga-aak · 8 years ago

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Open source and API driven chatbots allow businesses to build cross-platform chatbots with ease

The increased usage of chat applications opens the door for more businesses to utilize the ease of developing chatbots to reach more of their audience.

Chatbots are still an emerging technology, but they have shown that as the more tech-savvy generations grow, so does the usage and opportunities for chatbots.

For businesses, chatbots can help bridge the communication gap between a business and their audience. Chatbots have already penetrated industries such as retail, customer service, airlines, banking and finance, news and media, and healthcare.

There is an abundant amount of options businesses can utilize to build a chatbot specific to its company. The integrations of artificial intelligence within chatbots give more dynamic and robust self-serving channels for better customer engagement. The developments in AI will eventually push chatbots to become the solution for standardized communication channels and the single voice to solve consumer’s needs.

Chatbot Framework & Open Source Projects

There are a number of approaches to building a chatbot. Open source projects are programs developed collaboratively by a group of coders and made available for use or modification as users or other developers see fit for free. Open source software is intended to be freely shared and possibly improved upon and redistributed to anyone else without restriction.

A chatbot framework is a set of predefined functions and classes that are used by developers and coders to build bots from scratch using programming languages such as Python, PHP, Java, or Ruby. The frameworks are where chatbots behavior is defined with a set of tools that help developers write code more quickly and efficiently. Facebook Bot Engine, which owns Wit.ai, can extract certain predefined entities such as time and dates. It extracts user’s intent and then processes and defines the data given.

Chatbot platforms (a term often used interchangeably yet incorrectly with frameworks) are online ecosystems where chatbots can be deployed and interact with users or other platforms. Typically, platforms are used by non-technical users to develop bots without coding.

Chatfuel and Facebook Messenger Platform are a couple of platforms that were developed to make building a bot easier for users by linking to external sources through plugins. It provides a base to deploy and run the chatbot, whereas a chatbot framework helps develop and bind together various components to the application.

For businesses, platforms eliminate the need to hire developers to build a chatbot and allows users to quickly create robust chatbots without any coding. Platforms usually include a toolkit to create a chatbot, deploy it on any available messaging platform, and connect it to APIs. Google’s Cloud Natural Language API, Microsoft’s Cognitive Services APIs, and IBM Watson Conversation provide commercial NLU (Natural Learning Understanding) services that could be used to optimize chatbot efficiencies in self-service.

Chatbot Maker Software

FacebookChatbots can be built through hard coding by developers, but machine learning typically requires a large amount of streaming data so that the system learns on its own.

However, building chatbots is not exclusive to developers. There are a number of platforms accessible for businesses to start building one without writing a line of code. Nowadays, a business would only need to design the conversation flow and structure within a chatbot platform.

Chatfuel started in 2015 with the intention to make it easy to build chatbots for Facebook Messenger. It allows users to provide features such as content cards that are automatically shared with their customers, collect information within Messenger chats with forms and quick response buttons, and use AI to recognize customers' answers and respond appropriately. Companies such as Adidas, MTV, British Airways, and Volkswagen use Chatfuel to power their chatbot.

Botpress provides developers with an abundant number of open-source chatbot projects that saves them time. They provide a collection of specialized, open-source modules and offer most projects for free to create transparency. The focus is on the ultimate enterprise bot development that aims to satisfy serious bot developers. It offers countless software development tools for creating and managing code, as well as visual tools that are essential for efficient coding.

Facebook Messenger Platform allows users to build a chatbot via Facebook's official page, but it requires more functionality that the user will have to set up themselves. Facebook provides a guide for users to setup the Messenger plugin, Messenger codes and links, customer matching, structured templates, and a Welcome Screen. CNN and Poncho are popular chatbots that use Facebook Messenger as their chatbot platform.

Chatbots for Businesses a Growing Market

BI IntelligenceChatbots have a number of advantages over mobile apps. They are considerably simpler and faster to develop, release, and maintain than mobile applications. The increase in investments by big companies such as IBM, Facebook, and Google have released a number of free advanced development tools and frameworks and large amounts of research. Now, development of a high functioning chatbot that utilizes AI, NLP, speech recognition and other technologies can be done at a relatively low cost.

The increased demand for chatbots stems from the increasing usage of chat messenger applications. Mobile messengers such as Facebook, WhatsApp, WeChat, and others have become the preferred means of communication between mobile devices. Facebook Messenger alone has more than 20 million active business users. It's expected that chatbots will continue to serve and solve common issues and repetitive tasks within various industries.

More to Learn

Chatbots for business will continue to improve in the coming years. Chatbot architecture and design will evolve to the point that interactive AI will become standard for customer service. But there are numerous applications for chatbots across a variety of sectors.

That's why BI Intelligence, Business Insider's premium research service, has put together a bundle of detailed reports on chatbots:

The Chatbots Explainer

Chatbots in Banking

Chatbot Monetization

Conversational Commerce

To get all four reports, subscribe to an All-Access pass to BI Intelligence and gain immediate access to this report and more than 250 other expertly researched reports. As an added bonus, you'll also gain access to all future reports and daily newsletters to ensure you stay ahead of the curve and benefit personally and professionally. >> Learn More Now

You can also purchase and download the full reports using the links above.

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leonfrancisblog · 4 years ago

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Europe Conversational Computing Platform Market Market Trends, Growth, Opportunities, Market Size Forecast to 2026| Major Competitors Alphabet Inc. (Google), IBM Corporation, Microsoft, Nuance Communications, Inc., Tresm Labs, Apexchat

Conversational computing platform market competitive landscape provides details by competitor. Details included are company overview, company financials, revenue generated, market potential, investment in research and development, new market initiatives, Europe presence, production sites and facilities, company strengths and weaknesses, product launch, product trials pipelines, product approvals, patents, product width and breath, application dominance, technology lifeline curve. The above data points provided are only related to the company’s focus related to Europe conversational computing platform market. This conversational computing platform market report provides details of market share, new developments, and product pipeline analysis, impact of domestic and localized market players, analyses opportunities in terms of emerging revenue pockets, changes in market regulations, product approvals, strategic decisions, product launches, geographic expansions, and technological innovations in the market. To understand the analysis and the market scenario contact us for an Analyst Brief, our team will help you create a revenue impact solution to achieve your desired goal.

Conversational computing platform can be defines as platform where computer interact with human either with text or voice. The platform use artificial intelligence tool for processing language. For instance Chabot is conversational computing platform that widely used in all the sector for helping customer. Chabot’s are user interface of conversational platforms and its related assistants, where conversational platforms enable chatbots to operate and decode the natural language. SMS, social media and other interactive platforms are integrated in these conversational platforms. APIs (application programming interfaces) are provided by conversational platform so as to integrate other interactive platforms. The growing utilization of Chabot in the E-commerce sector is prominent factor drive the growth of the market. For instance the Germany based healthy food supermarket chain had introduced a chat bot that utilize for finding super market easy. Thus the utilization of Chabot will contribute in improving customer services. This factor will in turn increase the customer base for the company.

Europe conversational computing platform market By Type (Solution, Service), Technology (Natural Language Processing, Natural Language Understanding, Machine Learning and Deep Learning, Automated Speech Recognition), Deployment Type (Cloud, On-Premise), Application (Customer Support, Personal Assistance, Branding and Advertisement, Customer Engagement and Retention, Booking Travel Arrangements, Onboarding and Employee Engagement, Data Privacy and Compliance, Others), Vertical (Banking, Financial Services, and Insurance, Retail and Ecommerce, Healthcare and Life Sciences, Telecom, Media and Entertainment, Travel and Hospitality, Others), Country (Germany, Italy, U.K., France, Spain, Netherlands, Belgium, Switzerland, Turkey, Russia, Rest of Europe), Market Trends and Forecast to 2027. Conversational computing platform market is expected to gain market growth in the forecast period of 2020 to 2027. Data Bridge Market Research analyses that the market is growing with a CAGR of 31.7% in the forecast period of 2020 to 2027. Growing expansion of application base of AI solution in the various vertical is expected to drive growth of the market

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