Chatbot & RAG Development

AI chatbots that answer from your own data. Every citation linked.

Not another ChatGPT wrapper. We build RAG systems that read your policies, contracts, product docs, or knowledge base — and answer questions with sources you can click through. Same tech we used to index 50,000+ Indian court judgments for VakeelSaathi.

Book Free Feasibility Call See How VakeelSaathi Works

50,000+ docs indexed in prod

Sub-500ms answers

DPDP-ready by default

your-company.ai › support bot

What's the refund policy for enterprise plans?

Enterprise refunds are pro-rated for the unused period after a 30-day trial window. Full policy at Section 4.2 of the MSA.

Source: MSA_v3.2.pdf — Section 4.2 · 98% match

Every answer cites the source. Click to verify.

What Is RAG (In One Paragraph)

A chatbot that has to cite the source.

RAG stands for Retrieval-Augmented Generation. Before the AI answers your question, it searches an index of your documents, pulls the most relevant paragraphs, and gives them to the model as context. The model can only quote what you gave it. That's why a good RAG bot doesn't hallucinate — if the fact isn't in your docs, it says "I don't know" instead of inventing something.

The plain ChatGPT you use in your browser doesn't do this. It answers from training data it was frozen on. Which is why it makes up citations, dates, and policy details when you ask about your company.

How a RAG query flows

User asks a question

"What's our leave policy for probation employees?"

Question is converted to embeddings

A vector representation of meaning, not keywords.

Vector DB returns top 5 relevant chunks

From Pinecone, Weaviate, or pgvector — indexed on your docs.

LLM answers using only those chunks

GPT-4o, Claude 3.5, or a self-hosted Llama.

Answer returned with source links

User can click through to verify.

Proof of Work

How we built VakeelSaathi's legal RAG

India's Legal OS. 50,000+ Indian Kanoon judgments. Sub-500ms retrieval. Zero hallucinations.

The Problem

Lawyers were losing 4–6 hours a day to legal research.

Manual search on Manupatra or Indian Kanoon. Copy-paste citations. Miss the newer judgment because you didn't know to look for it. When you did use ChatGPT, it invented case numbers.

What We Built

A RAG index over every Supreme Court + High Court judgment.

Structured extraction of case number, citation, bench, court, subject, holdings. Semantic search over 50,000+ judgments. LLM answers with the actual case links.

The Numbers

97% top-3 accuracy. Sub-500ms. Zero invented cases.

Because every answer is grounded in real judgments with real citation numbers. If the case doesn't exist in the index, the bot says so. That's the point of RAG done right.

The stack that shipped it

LLM

Claude 3.5 Sonnet

GPT-4o fallback for cost

Embeddings

OpenAI text-embedding-3-large

Legal domain tuned

Vector DB

Pinecone

50,000+ vectors indexed

Framework

LangChain + LlamaIndex

Custom retrievers on top

See VakeelSaathi live

Chatbot Types We've Shipped

Six patterns. Pick the one that fits.

All six use RAG at the core. Each one gets a different UX layer on top.

Internal Knowledge Chatbot

"What's our leave policy?", "How do I raise a purchase request?", "Where's the SOP for X?" — trained on your wiki, HR docs, engineering runbooks.

Runs on Slack, MS Teams, or a private web app.

Customer Support Bot

Answers L1 tickets from your product docs and past resolved conversations. Hands off to a human when confidence drops. Cuts ticket volume 40–60%.

Sits inside Zendesk, Freshdesk, Intercom, or standalone.

WhatsApp AI Bot

A conversational agent on WhatsApp Business API. Answers, takes actions, hands over to human. VakeelSaathi's WhatsApp bot is in production. So is BookMySMS's messaging layer.

Session management + template messaging handled.

Document Q&A

Upload a 500-page contract, RFP, or regulation. Ask questions. Get answers with paragraph citations. Great for legal, compliance, and procurement teams.

PDFs, Word, Excel, scanned docs (with OCR).

Sales & Product Assistant

On your website. Answers pricing, feature, and comparison questions from your product docs. Books demos. Qualifies leads. Feeds them to your CRM.

Web widget with your brand, embedded in one line.

Private ChatGPT for Your Team

A ChatGPT-style interface deployed in your VPC or on-prem. Nothing leaves your walls. Backed by Llama, Mistral, or an Azure OpenAI private endpoint.

SSO, audit logs, role-based access included.

Process & Pricing

From idea to live users in 6–9 weeks.

Priced by phase. You know what you're paying for before we start.

Week 1

Discovery

Two calls. We look at your data, users, guardrails. Written feasibility doc.

Free

Weeks 2–3

Prototype

Working RAG bot on 500–2,000 of your actual docs. You test it. Two model options compared.

₹1.5–3 L

Weeks 4–8

Production Build

Full data ingestion, integrations, UI, SSO, audit logging, evals, security review, deployment.

₹6–15 L

Ongoing

Operate

Monitoring, cost control, quality evals, content re-indexing, patches. API costs included in fixed slabs.

From ₹40k/mo

FAQ

Questions we hear on almost every call.

A RAG chatbot (Retrieval-Augmented Generation) answers questions using your documents rather than the model's generic training data. Before generating a reply, the system searches an index of your content, retrieves the most relevant pieces, and gives them to the language model as context. The model can only answer from what you provided. If the information isn't in your documents, the bot says so instead of making something up.

ChatGPT answers from its general training data, which stops at a cutoff date and knows nothing about your company. A RAG chatbot answers from your documents, updated whenever you update the source. ChatGPT hallucinates when it doesn't know something. A RAG chatbot cites the source paragraph it used, so you can verify the answer.

Prototype phase is ₹1.5–3 lakh depending on data volume and integrations. Full production build is ₹6–15 lakh. Ongoing operations start at ₹40,000/month and include model API costs, monitoring, quality evals, and content re-indexing. Feasibility call is free.

A working prototype on your data takes 2–3 weeks. Production build takes 4–6 weeks after that. Total: 6–9 weeks from kickoff to live users. WhatsApp integration or multilingual support adds 1–2 weeks.

Yes. VakeelSaathi's legal drafting works in 8 Indian languages including Hindi, Marathi, Tamil, Bengali, Telugu, Kannada, Malayalam, and Gujarati. For your use case, we test multilingual models on a sample of your data first — no one model wins everywhere in Indian languages.

Your choice. We deploy on AWS Mumbai, Azure India, or self-hosted GPU inside your VPC. For sensitive data, we use on-prem Llama or Mistral so nothing leaves your infrastructure. When we use OpenAI or Claude, we sign a zero-data-retention agreement — your data is never used to train their models.

Three layers. First, RAG grounds every answer in retrieved source chunks — the model can only quote what we gave it. Second, we run automated evals on real user questions to catch drift. Third, we set a confidence threshold — below it, the bot says "I don't know" or routes to a human. Zero hallucination isn't a marketing claim, it's how we shipped VakeelSaathi's legal research.

Yes. We handle the WhatsApp Business API integration, session management, and message templates. VakeelSaathi has a WhatsApp bot in production. BookMySMS (our messaging product) handles the delivery layer.