It's the first question everyone asks: how do you stop the AI from making things up? It's the right question. When an answer ends up on an invoice, an insurance claim, or a patient record, “usually right” isn't good enough. So we don't hand your work to one giant know-it-all model and hope. We build for “right, and able to prove it.”
Why general models guess
A large general-purpose model is built to always produce a fluent answer, even when it doesn't actually know. That tendency to fill the gap with something plausible is where hallucinations come from.
For an enterprise workflow, a confident wrong answer is worse than no answer at all.
Small models, trained for one job
We use small models fine-tuned for a single, well-defined task. A narrower job means a narrower space for the model to go wrong in, and far more predictable behaviour.
Fields, not free text
Where we can, the agent fills in set fields rather than writing loose paragraphs. Structured output leaves far less room for mistakes to hide, and makes the result easy to check against your rules.
Every answer cites its source
Every answer points back to the exact document and line it came from. If the agent can't find a source, it says so instead of inventing one. No source, no answer.
That single rule turns the agent from a black box into something an auditor can follow.
Rules check the work; people handle the edge cases
Business rules check the agent's output before anything is final. And when the agent isn't confident, it routes the case to a person rather than guessing.
The result is an agent that behaves the same way every time and can always show you why it did what it did. It's deterministic, and it's explainable.