Project

SaaS RAG Chatbot

A retrieval-augmented SaaS knowledge assistant that answers customer and internal support queries using trusted product content.

This project reflects how Mohammed Rafique Kuwari approaches AI automation engineering: define the operational bottleneck, design a reliable data flow, and build outputs that are useful inside real business systems.

Business problem

Support teams spent time repeatedly answering the same product and onboarding questions across channels.

Approach

Ingested product docs, changelogs, and support knowledge base articles.
Implemented chunking, embeddings, and vector retrieval with metadata filters.
Added citation-first response design with query rewriting and safety guardrails.
Created feedback loops to monitor answer quality and retrieval drift.

Tech stack

TypeScriptNode.jsLLMsVector DatabaseREST APIs

Architecture highlights

Content ingestion and indexing workers
Vector retrieval API with tenant-aware filters
RAG orchestration layer with citations
Monitoring dashboard for query and relevance metrics

Expected value

Improved support response speed and consistency while helping users self-serve answers from the SaaS knowledge base.