AI agents your team can actually trust
Agents that answer from your own documents, cite where every answer came from, and say "I don't know" instead of inventing one.
Why most AI pilots never reach production
The demo impresses everyone in the room. Three weeks later nobody uses it. The reason is almost always one of these three.
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It invents answers.
A generic assistant fills any gap with something plausible. One wrong answer in front of a client and the whole team stops trusting the tool.
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Nobody can verify it.
Without a citation pointing at the source document, checking an answer costs more than looking it up by hand. So nobody checks, and nobody adopts.
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The cost is invisible.
Tokens, latency and spend per query stay buried in a provider dashboard, so there is no way to tell whether the pilot is worth scaling.
None of these is a model problem. They are engineering problems, and they get solved before the first line of the prompt.
How we build them instead
We don't sell you a model. We ship the system around it, with the guardrails the business case actually needs.
Grounded retrieval
Every answer is built from your corpus and comes back with clickable citations to the exact fragments it used.
Explicit refusal
When the corpus doesn't support an answer, the agent says so. A calibrated threshold decides, tuned against your own evaluation set.
Cost and latency in the open
Every query reports its tokens, its spend and its response time, with account-level budget caps as the safety net.
What you get
An agent in production, plus everything needed to keep it honest.
Your knowledge, usable
Contracts, procedures, tickets and reports become something your team can ask questions to, in plain language.
Answers you can audit
Citations on every response and an evaluation suite that catches regressions before your users do.
Spend under control
Cost per query, budget alerts and a hard ceiling, so a pilot never turns into a surprise invoice.
See it working: Business Brain Copilot
The copilot is our own reference implementation. It shows the three behaviours we build into every agent.
The demo runs over the public FastAPI documentation, on purpose: it's a corpus anyone can open, so you can click any citation and check for yourself that the answer wasn't invented. On your project, the corpus is your own documents.
Retrieval with citations
Ask a question and the answer comes back with the source fragments it was built from, each one clickable.
Refusal when there's no ground
Ask something the corpus doesn't cover and the copilot says it can't answer, instead of improvising.
Cost and latency panel
Every run shows its tokens, its spend and its response time, so the economics are visible from day one.
Request access to the demo
Leave your name and email. We grant access one by one, so what you get is a working environment with a real corpus already loaded.