SaaS
RAG system for SaaS documentation Q&A
We built a retrieval-augmented generation system that answers questions over SaaS documentation, using tags and labels to keep retrieval scoped and responses accurate and citable.
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The challenge
A SaaS product had documentation users constantly searched through. Plain keyword search missed intent, and a naive LLM would hallucinate answers not in the docs.
The outcomeA documentation assistant that answers from the product's real content, scoped by tags, instead of guessing, cutting the time users spend hunting through docs.
What we built
- Ingested and chunked the documentation, then embedded it into a vector store.
- Built retrieval with tags and labels so results stay scoped to the right sections.
- Wired the LLM to answer from retrieved passages with a clear no-answer path.
- Delivered in TypeScript and Node.js with LangGraph orchestration.
Have an idea for an AI agent?
Tell us the outcome you want. We will come back with a clear scope, timeline, and quote, usually within a day.