AI Assist for Internal Teams
A purpose-built assistant connected to your internal docs, wikis, and operational data, so your team can ask a direct question and get a sourced answer instead of searching four different tools.
The problem this solves
Internal knowledge is almost always scattered: a policy doc in Notion, pricing logic in a spreadsheet, edge cases living in a Slack thread from six months ago. New hires ask the same questions repeatedly, and experienced staff spend real time being a human search engine for their own team.
Generic AI chat tools don't help much here because they don't know your product, your pricing exceptions, or your internal process. They either hallucinate an answer or shrug.
What a build typically includes
We build a retrieval layer over your actual internal sources, so answers come with a citation back to the source document, not just a confident-sounding guess. This gets deployed where your team already works, usually Slack or a lightweight internal web app.
- Indexing of internal docs, wikis, ticket history, and structured data (with permissions respected)
- A Slack or web interface your team can query directly, with source citations on every answer
- Scoped access so sensitive data stays visible only to the teams who should see it
- Feedback loop so incorrect or outdated answers get flagged and the source gets fixed
Where it plugs into your stack
Typical sources are Notion, Confluence, Google Drive, your ticketing system, and internal databases. It runs as a Slack app or a small internal tool depending on where your team already spends their day.
Where a human stays in the loop
The assistant answers from your indexed sources only and shows where the answer came from. Anything outside its indexed knowledge gets flagged rather than guessed at, and your team can correct answers directly.
See if this fits your stack.
A systems audit maps this against your actual tools before any code gets written.