Internal AI Assistant
The assistant your company would build if it had the team.
Large tech companies run private assistants that know their internal systems — and every other enterprise wants one. We deploy that assistant inside your environment: grounded in your own knowledge, bound by your existing permissions, and measured against questions your team actually asks.
Connects to
Connector coverage depends on the deployment. Systems without an off-the-shelf connector are integrated through their API.
What it does
Search is the easy half.
Any team can put a chat box in front of a vector database. The reason internal assistants fail is permissions, grounding, and quality nobody measures.
Answers grounded in your systems
Ask a question in plain language and get an answer assembled from your wiki, drives, tickets, and code — with citations back to the source document, not a confident guess.
Permission-aware from day one
The assistant inherits your existing access model. If someone can't open a document in the source system, it cannot surface in their answer. This is enforced at retrieval, not filtered afterward.
Agents that act, not just answer
Beyond search: agents that file the ticket, draft the response, update the record, or run the report — with a human approval step wherever the action is consequential.
Evaluated continuously
Every deployment ships with a graded question set drawn from your real internal questions, run as a regression suite on every prompt, model, or connector change.
Runs in your environment
Self-hosted in your cloud or on-premise, including air-gapped. Your documents and queries do not leave your boundary unless you decide they should.
Model-agnostic
Route to OpenAI, Anthropic, Gemini, or an open-weight model you host yourself — chosen per task on quality, latency, cost, and data-handling requirements.
Our approach
We don't rebuild what already works
The retrieval and connector layer is a solved problem with excellent open-source implementations, and we deploy on one rather than reinventing it. What we build is the part that decides whether the assistant is trusted: correct permission mapping, an evaluation suite tied to your real questions, the agent and approval layer, and the operational tooling to keep it working as your data and models change.
We deploy on mature open-source infrastructure and contribute back to it. We are not resellers, and we claim no affiliation with the projects we build on.
How we deploy it
Trusted before it's widespread.
The failure mode is launching to the whole company before anyone has checked whether the answers are right. We invert that.
Weeks 1–2
Connect and index
Stand up the platform in your environment, connect the first two or three sources, and validate that permission mirroring is correct before anyone else gets access.
Weeks 3–4
Build the evaluation set
Collect real questions your team actually asks, have your experts grade correct answers, and establish the baseline the assistant must beat.
Weeks 5–8
Pilot, measure, expand
Roll out to one team, measure answer quality and usage against the baseline, fix what the evals expose, then widen access and add sources.
Illustrative timeline — actual duration depends on the number of sources and the state of your permission model.