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AI Chatbot & Automation

A RAG-powered knowledge assistant that answers from your docs, captures leads, and works on web and WhatsApp — 24/7.

Python LLM RAG REST API

Case study details

The Problem

A support team was drowning in the same questions every day — pricing, hours, product basics, status checks. Response times slipped during peak hours, and after closing, inbound leads and WhatsApp messages simply sat unanswered until morning.

Hiring more agents would not fix the pattern: most volume was repetitive, documented knowledge that a well-built assistant could handle. The business needed instant, accurate answers without losing the human handoff for complex cases.

Our Solution

AI is IndiaCMS’s biggest differentiator. From Electronic City, Bangalore, our team — drawing on 14+ years of enterprise delivery — built a production AI chatbot grounded in the client’s own documents via RAG, not generic chatbot scripts. It answers from approved knowledge, captures leads, and escalates when needed.

  • RAG knowledge assistant trained on business docs, FAQs, and policy content
  • Lead capture flows that collect intent and contact details into the CRM pipeline
  • LLM integration with guardrails for on-brand, source-grounded replies
  • Workflow automation for tickets, follow-ups, and handoff to live agents
  • Multi-channel deployment on website chat and WhatsApp

Technology Stack

Python LLM RAG REST API

The Result

Business outcome: Repetitive support volume is handled 24/7 by a RAG assistant grounded in company docs, freeing agents for complex cases and capturing after-hours leads.

Agents focused on exceptions instead of repeating the same answers. Prospects who arrived after hours still got help — and many converted because someone (the assistant) responded immediately.

65%
Queries Auto-Resolved
24/7
Instant Response
40%
More Leads Captured
3x
Faster Support
30+ hrs/week
Time Saved

This is a representative sample project demonstrating our capabilities. Actual scope and results vary by client.

Want a system like this?

Bring your FAQs and channels — we will show how a RAG assistant can cut repetitive support load for your team.

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