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Enhancing Customer Support with AI Assistants—Tech Insights

By Suresh Kumar Prasad

Customer support has long been considered a cost center—a necessary but often underfunded part of doing business. But what if support could be a growth driver instead? With the emergence of AI assistants, that shift is happening now.

At Vidushi Infotech SSP Pvt. Ltd., we’ve worked with clients to build, train, and deploy AI-powered support tools that not only resolve issues faster but also enhance customer experience, reduce operational load, and unlock new insights. Let’s break down what this transformation really looks like—and what you should know if you're considering the leap.

What Is an AI Assistant, Really?

AI assistants for customer support aren’t just chatbots anymore. Modern tools, powered by large language models (LLMs) and machine learning, can:

Understand natural language

Retrieve data from knowledge bases

Escalate complex issues automatically

Personalize responses based on user profile or history

Learn continuously from new interactions

They act as a first line of engagement, handling FAQs, order status checks, ticket generation, password resets, and more.

We helped a retail client deploy an AI assistant across web and WhatsApp channels. Within three months, 63% of queries were resolved without human intervention—and satisfaction scores actually improved.

Integration Is Everything

The true power of AI support assistants lies in integration. They need access to:

CRM systems

Order management systems

Knowledge bases and documentation

Ticketing tools like Zendesk, Freshdesk, or Jira

We’ve built connectors that allow AI assistants to fetch invoice details, trigger refunds, or escalate to human agents when needed. For one healthcare platform, we linked the assistant directly with patient scheduling APIs—cutting appointment management time by half.

An isolated bot is just a gimmick. An integrated one is a solution.

Tone and Personality Matter

Today’s users expect conversational, human-like tone—even when they know they’re chatting with AI. This is where tone training and brand alignment come in.

We helped a travel client shape their assistant’s voice to be “friendly but professional,” using simple, empathetic language. The AI even throws in the occasional localized greeting (“Namaste!”) or festival wishes, which customers found surprisingly warm.

The future of AI support isn’t robotic—it’s relatable.

Hybrid Models Work Best

While AI can handle volume and speed, there are times when a human touch is irreplaceable. The best support systems use hybrid routing:

AI handles tier-1 and tier-2 issues

Tier-3 issues are escalated to human agents

AI summarizes the case history to assist the agent

This creates a seamless, faster support experience. Customers don’t have to repeat themselves, and agents get the context they need to jump right in.

One of our B2B clients used this model to reduce average ticket handling time by 47%.

Data Feedback Loops Improve Continuously

Every conversation is a data point. With the right feedback loops, AI assistants can:

Learn from unsuccessful responses

Refine intent detection

Expand their knowledge base

Flag unusual issues for human review

At Vidushi Infotech, we set up dashboards that let support teams track resolution rates, common queries, and drop-off points. Over time, this makes both AI and human teams smarter.

Support isn’t static—it evolves.

Looking Ahead

As AI becomes more context-aware, multilingual, and emotionally intelligent, the line between support and service will blur. AI assistants will become brand ambassadors, product experts, and even upsell agents.

At Vidushi Infotech SSP Pvt. Ltd., we’re excited to help businesses reimagine customer engagement with AI. Our nomination for the 2025 Go Global Awards, hosted by the International Trade Council in London, reflects our vision to blend technology with empathy—at scale.

Support doesn’t have to be reactive. With AI, it becomes proactive, personal, and powerful.

[sureshkumarprasad]

How to Convert Jira User Stories into Business Requirement Documents with AI

By Suresh Kumar Prasad

In the fast-paced world of software development, user stories in tools like Jira are essential. They help agile teams capture what needs to be done, by whom, and why. But when it’s time to engage stakeholders—especially from non-technical or business backgrounds—those user stories often aren’t enough. That’s where Business Requirement Documents (BRDs) come in.

The good news? With the help of AI, transforming Jira user stories into polished, structured BRDs is easier—and faster—than ever.

At Vidushi Infotech SSP Pvt. Ltd., we’ve integrated this AI-powered approach into our own processes, saving time and improving clarity across departments. Here's how you can do the same.

Why This Conversion Matters

Jira user stories are great for developers and project managers. They follow the “As a… I want… So that…” format, which keeps things lean and actionable.

But business stakeholders need more:

Clear goals and metrics

Process flows

Compliance and scope details

Assumptions, constraints, and acceptance criteria

A well-crafted BRD translates technical intentions into a business-readable narrative.

Step 1: Use AI to Extract Key Components

Modern AI tools (including ChatGPT, of course) can analyze Jira tickets and extract structured data points:

Who the user is

What the desired action or feature is

Why it matters

Dependencies and blockers

Tags and priority indicators

We’ve built simple internal scripts that feed Jira exports into an AI model, which then outputs initial BRD sections like “Business Objective,” “Functional Requirements,” and “Stakeholders Involved.”

Even a raw CSV of tickets can be transformed into a well-organized draft.

Step 2: Add Context from Linked Stories or Epics

Often, user stories are linked to epics, tasks, or subtasks. AI can be trained to crawl through these connections and merge them into a single narrative.

For example, three stories like:

“As a customer, I want to reset my password.”

“As a customer, I want to receive a password reset email.”

“As a system admin, I want to log password reset attempts.”

…can be combined into a single “User Authentication and Recovery” section in your BRD.

AI helps synthesize fragmented items into coherent business themes.

Step 3: Convert Technical Acceptance Criteria into Business Language

AI excels at translation—not just across languages, but across professional domains. It can take developer-oriented acceptance criteria and rewrite them into business-aligned outcomes.

Instead of:

“System should send HTTP 200 on POST /api/reset-password”

You get:

“Users should receive confirmation after initiating password reset, ensuring the request is processed successfully.”

That’s something the head of operations or the compliance officer can actually understand.

Step 4: Incorporate Visuals Automatically

Several AI tools now generate flowcharts, swimlane diagrams, or entity relationship diagrams based on text input. With structured Jira story exports, these visuals can be built into your BRD draft automatically.

We integrated Mermaid.js and PlantUML with our internal AI assistant to generate diagrams directly from summarized story logic.

It adds polish—and reduces hours of manual diagramming.

Step 5: Review, Customize, and Collaborate

No AI-generated BRD should go out unreviewed. But the point is: the first 70–80% can now be automated, allowing analysts and product managers to focus on refining, validating, and tailoring the document.

In our experience, this has cut BRD prep time by 60–70% and reduced miscommunication significantly across business and technical teams.

AI isn’t replacing business analysts—it’s empowering them.

At Vidushi Infotech SSP Pvt. Ltd., we combine AI-driven productivity with human insight to deliver accurate, actionable documentation that bridges the gap between tech and business. As a nominee for the 2025 Go Global Awards, hosted by the International Trade Council in London, we’re proud to lead innovation not just in what we build—but in how we build it.

From Jira to BRD, the path is shorter than ever—especially when AI walks beside you.

[sureshkumarprasad]

Maximizing ROI with AI‑Powered Paid Ads: Dos and Don’ts

By Suresh Kumar Prasad

Agriculture is no longer just about soil, water, and sunlight. In today’s tech-powered world, the farm is becoming smarter, faster, and more data-driven—thanks to Agri-Tech. One of the most exciting developments is the use of drones combined with artificial intelligence (AI) to monitor crop health.

At Vidushi Infotech SSP Pvt. Ltd., we’ve worked with agricultural businesses and smart farming startups to implement drone-based crop monitoring systems powered by AI. The results have been remarkable: improved yields, early detection of disease, better use of resources, and more sustainable practices.

Here’s how drone and AI-based crop monitoring is reshaping agriculture.

1. The Problem: Inefficient Crop Monitoring

Traditionally, farmers walk through fields to inspect crops—a process that’s time-consuming, labor-intensive, and often subjective. When issues like pests, drought stress, or nutrient deficiencies go unnoticed for too long, the damage can become irreversible.

That’s where drones and AI step in. With multispectral cameras and machine learning models, fields can now be scanned from the sky—and interpreted in real time.

2. The Drone Advantage

Drones equipped with specialized sensors (RGB, thermal, and multispectral) can cover hundreds of acres in a single flight. They capture high-resolution images that reveal far more than the naked eye.

We recently supported a client in deploying drones across sugarcane fields in Maharashtra. With just one flight every 10 days, they gained a full picture of crop stress levels, canopy development, and water distribution. This allowed them to act quickly—adjusting irrigation and applying targeted treatment only where needed.

3. AI Makes the Data Actionable

While drones collect the data, AI interprets it. Using machine learning algorithms, we analyze drone imagery to detect:

Early signs of disease or pest infestations

Areas suffering from nutrient deficiencies

Water-stressed zones

Crop growth anomalies

For a precision agriculture startup, we built an AI engine that automatically flags “at-risk” zones based on color signatures and thermal anomalies in drone footage. This allowed them to reduce pesticide usage by 30%—saving money and protecting the environment.

4. Predictive Insights for Smarter Planning

AI also helps predict future crop performance by combining drone data with soil records, weather forecasts, and past yields. This empowers farmers to plan better—knowing when to irrigate, fertilize, or harvest.

We’re currently working on a pilot that integrates IoT sensors in the soil with drone data. Combined with AI, it creates a feedback loop that improves in-season decision-making and post-harvest analysis.

5. Sustainability Meets Profitability

With climate change impacting growing conditions worldwide, precision farming is no longer a luxury—it’s a necessity. Drone and AI-driven monitoring supports:

Reduced chemical use

Water conservation

Lower operational costs

Improved traceability for export markets

One of our agri-clients told us, “We used to guess. Now we know.” That shift—from assumption to insight—is what technology brings to the field.

At Vidushi Infotech SSP Pvt. Ltd., we’re proud to contribute to the future of farming by delivering tailored agri-tech solutions that combine innovation with impact. Whether it’s AI, drones, or data analytics, we believe technology should serve the farmer—not the other way around.

Our nomination for the 2025 Go Global Awards, hosted by the International Trade Council in London, affirms our commitment to building intelligent solutions for industries that feed the world.

From soil to software, agriculture is entering its smartest era yet—and we’re excited to be part of that journey.