AI Chatbot Development

Custom AI Chatbots Grounded in Your Knowledge,Not the Model's

Off-the-shelf chatbots answer from the LLM’s training data and make up the rest. We build custom AI chatbots grounded in your documents, policies, product data, and ticket history — using RAG on Azure OpenAI, Snowflake Cortex, and open-source models. Deployed inside your tenant where the data stays private, with evaluation, fallback to humans, and monitoring that catches hallucinations before customers do.

Where Chatbots Usually Embarrass Their Owners

Your chatbot confidently answered a customer with a policy that doesn’t exist. You found out in a support escalation.

You built a chatbot on ChatGPT. It doesn’t know your product, your pricing, or your return policy — so customers still email support.

Every time you update a document, someone has to remember to update the chatbot’s training. They don’t always remember.

Customer data flows through a third-party LLM API. Your compliance team hasn’t signed off.

The chatbot answers the easy questions. The hard ones get a “let me connect you with support” loop the customer can’t escape.

Where Are You Starting From?

Customer-support chatbot grounded in our help center and policies

Customer Support Chatbot

Internal knowledge bot for employees across SharePoint, Confluence, wikis

Internal Knowledge Bot

Sales / product chatbot for the website

Website Conversational AI

HR or IT self-service chatbot for employees

HR / IT Service Desk Bot

Existing chatbot hallucinating or losing context — need a rebuild

RAG Re-Architecture

Need to deploy in Microsoft Copilot Studio for Microsoft-heavy stack

Copilot Studio Build

Regulated industry — chatbot must stay in our tenant

In-Tenant Chatbot

Multilingual chatbot across customer languages

Multilingual Chatbot

What can I help with ?

What Changes After We Engage

Six scenarios the buyer can picture.

Answers cite the source document

A customer asks about the return policy. The chatbot answers accurately and links to the exact policy page. If the customer disputes the answer, you have the source citation — not a guess.

Your documents update, the chatbot updates

Your product team edits the user guide on Monday. The chatbot reflects it by Tuesday because the retrieval index refreshes automatically. Nobody has to remember to retrain.

Hallucinations get caught before customers see them

The evaluation pipeline scores every answer type against ground truth weekly. Drift triggers an alert. Known-bad answer patterns get added to the test set so they can't come back.

Handoff to humans is smooth, not a loop

The chatbot escalates to support with full conversation context, customer identity, and the exact question. The agent picks up where the bot left off — no "can you repeat the issue for me."

Customer data stays in your tenant

The LLM call runs on Azure OpenAI inside your Azure subscription. Customer messages don't leave your environment. Compliance signs off once and the deployment stays compliant.

Support deflection is measurable

Your dashboard shows the number of conversations resolved by the bot, the handoff rate, customer satisfaction on bot-handled tickets, and the $ value of hours saved. Real numbers, not "AI-powered engagement."

How We Engage

1

Scope, source, and ground

We identify the documents and data the chatbot will ground in (SharePoint, Confluence, Zendesk, product docs, CRM), agree conversation scope and handoff points, pick the LLM and retrieval stack for your tenancy and compliance needs.

2

Build with evaluation from day one

Retrieval pipeline, prompt engineering, citation rendering, human-handoff logic, content safety filters, and an evaluation suite with ground-truth test cases — all wired in before the chatbot sees a real customer.

3

Deploy and operate

Deploy into your chosen channels (web, Teams, Slack, WhatsApp, Zendesk), run evaluation weekly, tune prompts and retrieval on real data, and monitor deflection rate and handoff quality on an ongoing basis.

Ship an Chatbot That Answers From Your Documents — Not From Its Training Data

Book a chatbot scoping call. We’ll map the knowledge sources, pick the RAG and LLM stack for your tenancy, and come back with a build plan that gets a grounded, evaluated chatbot live in weeks.

Frameworks & References

LLMs

RAG & Retrieval

Microsoft Bot Ecosystem

Deployment Channels

Evaluation & Safety

What Clients Say About Working With Exillar

Excellent work as always by Umair and team. Umair and team continue to provide excellent work product. Highly recommend, responsive and attention to detail. Umair + Exillar team continue to impress and innovate as business needs evolve

Thanks for the project. If you are an Executive, you need a PowerBI dashboard. Great working with the team. Many ongoing projects with Umair. Great person to work with.

These guys are true professionals, they helped me improve the idea of the work I wanted to develop, very kind and prepared. We will definitely do more work together. second work and I’m very statisfied

The guys were great to work with, very fast to reply and have a deep understanding of PowerBI. This become a learning experience for me as they shared best practices for PowerBI.

Thanks for the exceptional work!

It was a great experience.

Umair handled my problem timely and efficiently. He is easy to collaborate with and I will be using him again.

Super good explanation, patience and a good sense of indagatory about the data, sources, etc. The solutions suggested were very safisfactory.

It is always a pleasure to work with Umair and count on his skills to assist us. I highly recommend him. He has excellent communication skills, which makes my life much easier when conveying out needs to a plan, and executing it.

Honestly, this has been an outstanding experience from start to finish.The team went far beyond my expectations — not only did they understand a very complex real-world operation, but they were also able to translate it into a functional and well-structured system.

Working with Exillar has been amazing. Bhavisha has has gone above and beyond to get us what we need. Very pleased. ~Sherwin

It is always a pleasure to work with Umair and his team. Rock start service!

Industries We've Worked In

Got Questions?

How do you stop a chatbot from hallucinating?

Grounding first — retrieval-augmented generation pulls from your actual documents, so the answer has a source. System prompts constrain tone and scope. Evaluation suites test known failure cases every release. Content safety filters catch unsafe output. It’s layered; no single control is enough.

Can this stay inside our Azure or AWS tenant for data privacy?

Yes. Azure OpenAI and Snowflake Cortex run in your tenant. Self-hosted Llama or Mistral runs in your VPC. Customer text never hits a third-party API unless you explicitly choose that. Compliance signs off once on the architecture.

Azure OpenAI chatbot, Copilot Studio, or fully custom — which fits us?

Copilot Studio is fastest when you’re Microsoft-heavy and need a bot in Teams with minimal code. Azure OpenAI + custom orchestration is right when you need tighter control over RAG, tooling, or non-Microsoft channels. We run a scored comparison before recommending.

How do you measure chatbot success?

Deflection rate (tickets the bot resolved), handoff quality (satisfaction when escalating), ground-truth accuracy (evaluated weekly against test cases), and cost per resolved conversation. All four are baselined at launch and tracked monthly.

Custom AI Chatbots Grounded in Your Knowledge,Not the Model's

Where Chatbots Usually Embarrass Their Owners

Where Are You Starting From?

What Changes After We Engage

Answers cite the source document

Your documents update, the chatbot updates

Hallucinations get caught before customers see them

Handoff to humans is smooth, not a loop

Customer data stays in your tenant

Support deflection is measurable

How We Engage

1

2

3

Ship an Chatbot That Answers From Your Documents — Not From Its Training Data

Frameworks & References

What Clients Say About Working With Exillar

D&K

Growloup

willybesmart

Darcy

Hans

Miguel

Travis

Raul Rodriguez/F&K

Alex

Latamsa

Loudermilk Homes

Alex

Industries We've Worked In

Retail & E-Commerce

Healthcare

Finance & Banking

Real Estate & Construction

IoT & Technology

Manufacturing & Industrial

Got Questions?

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