Custom Knowledge-Base Assistant
Deployed a private RAG assistant over the client’s internal docs and Jira tickets — developers find answers in seconds, not hours.
Client snapshot
The challenge
Onboarding new engineers took 6-8 weeks partly because "how do we do X here?" meant Slack-pinging 3 senior devs and digging through 5 Confluence spaces.
Even senior engineers wasted ~30 min/day searching old Jira tickets for "how did we solve this last time?" — the answers existed, they were just impossible to find.
ChatGPT could answer generic coding questions but had zero context on the client's internal conventions, past architecture decisions, or closed tickets.
Our approach
- 01
Index everything that matters
Crawled Confluence, Notion, closed Jira tickets, internal GitHub wikis, and engineering RFCs. 500+ documents, chunked intelligently with source metadata preserved.
- 02
Private embeddings + RAG
Used OpenAI embeddings + pgvector running in the client's own VPC — no data ever leaves their infrastructure. Retrieval-augmented generation with citations on every answer.
- 03
Slack-first UI
Engineers live in Slack, so the assistant lives there too. Ask in any channel with @kb-bot, get an answer with links back to the source docs.
- 04
Answer quality loop
Every answer has a thumbs up/down. Low-rated answers flag the underlying docs for review — so the knowledge base gets cleaned over time instead of decaying.
What we built
- Private RAG pipeline indexing Confluence, Notion, Jira, GitHub wikis
- Slack bot for natural-language Q&A with source citations
- Web dashboard for admins to browse sources, flag outdated docs
- Weekly refresh job to keep the index current
- Thumbs-up/down feedback loop surfacing stale or wrong docs
- Role-based access — engineers see engineering docs, support sees support docs
Results
- 45% less time spent searching for internal knowledge (self-reported)
- Answer accuracy at 91% based on 2,000+ rated responses
- New-hire onboarding shortened by ~2 weeks on average
- Quietly surfaced 40+ stale or contradictory docs for cleanup
Tech stack
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Client identity kept confidential under NDA. Metrics reflect the actual project at the time of delivery — full decks available on request.