Case Study
Enterprise AI
RAG Knowledge Engine for Enterprise Docs
Retrieval-augmented Q&A over an enterprise client's internal docs — accurate, fast, with verifiable citations on every answer.
At a glance
What this shipped
The numbers that mattered to the client — measured before and after.
The problem
What we were called in to fix
The client, a mid-market SaaS company, had 8,000+ pages of internal documentation across Notion, Google Drive, Confluence and a legacy SharePoint. New hires took three months to find anything; support engineers answered the same five questions every day.
Off-the-shelf chatbots hallucinated badly and offered no way to verify answers. Internal trust in any AI tool was at zero after a previous failed pilot.
Our approach
How we actually built it
No magic — just the right architectural calls in the right order.
We built a hybrid retrieval pipeline: semantic search (Pinecone) plus BM25 keyword search, with a re-ranker on top. Every answer cites the exact source chunks and links back to the original doc.
Source ingestion is incremental and deduplicated across systems — the same page in two places doesn't double-count.
An evals harness with 200 ground-truth Q&A pairs runs on every model or prompt change. We don't ship a regression in retrieval quality.
Per-user usage caps and per-team cost dashboards mean the CFO never gets surprised.
The outcome
What changed for the client
51% of internal support questions deflected from the human queue.
Median time-to-answer for new hires: 6 minutes (was 4 hours).
Hallucination rate measured at <2% on the evals harness — every answer cites its source, so users can verify.
Tech stack
Every meaningful piece
“We don't do generic case-study writeups. Want the unredacted version with names, screenshots and architecture diagrams? We share those on a call.”
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