AI / RAG Architecture — 2024–25
Enterprise Knowledge AI Chatbot
Year
2024–25
Category
AI / RAG Architecture
Stack
- Python
- Gemini API
- AWS Lambda
- RAG
- Vector DB
- Zoho Cliq
Case Study
Download PDFThe Problem
Internal policies, SOPs, and product documentation are spread across files, wikis, and databases. Employees waste hours hunting for answers, and critical knowledge often lives in the head of one or two people.
The Solution
A RAG-powered knowledge assistant that ingests enterprise documents into a vector database and answers natural-language questions inside Zoho Cliq — giving employees instant, source-grounded answers where they already work.
Built an enterprise knowledge assistant on a serverless AWS Lambda backend. Documents are parsed, chunked, embedded, and stored in a vector database. When an employee asks a question in Zoho Cliq, the system retrieves the most relevant document passages and augments a Gemini-powered response with that context, so every answer is grounded in the actual source material. The chatbot lives directly inside the team’s existing chat workspace, removing the friction of switching tools or reading through 200-page manuals. Security guardrails and source attribution keep answers accurate, auditable, and on-policy.