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, so 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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