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How we eliminate hallucination
Not with a better prompt. With an architecture that refuses to publish anything it cannot trace back to a source.
The reason most enterprise AI projects stop at pilot is not capability. It is that nobody will sign off on output they cannot verify. A leaflet with a wrong price is a legal problem. A market report with an invented statistic is a boardroom problem. So the question that matters is not how good the model is, but what happens between the model and the page.
Strict data grounding
Agents do not answer from model memory. They answer from your systems of record and from identified external sources, and every output carries the provenance of what it drew on.
Deterministic cross-referencing
Before anything is released, a separate validation agent checks the factual claims against source. In the retail campaign deployment this means every printed price is checked back against SAP PMR. The generation step and the checking step are independent by design, because a model marking its own homework is not a control.
Full audit trail
Every action an agent takes is logged: what it read, what it produced, what was checked, what was escalated. When somebody asks where a number came from, there is an answer.
Human escalation
Agents are configured with explicit limits. Where an answer would require improvisation — an unusual pricing question, an edge case in a policy — the agent hands to a person rather than guessing.
Result
- Zero hallucinations on published executive reports across production deployments to date.
- 100% response accuracy in the insurance deployment — no inconsistent answers on cover or price.
- Every claim in every intelligence report traceable to a real published source.
Bring one bottleneck.
Leave with an agent.
Tell us about one real process in your operation. We come back with a concrete agent design and a cost model for running it.