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Trust & safety

Reduce AI hallucinations in production

You reduce AI hallucinations in production by grounding every answer in retrieved source text, forcing the model to cite the passage it used, making "not in the source" a valid and encouraged answer, and verifying claims against the source before you return them. The demo below refuses to answer when the document does not contain the answer, which is the behavior that makes AI safe to ship.

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Document Q&A

Grounded answers over your document, with honest "not in the document" refusals.
95%
of enterprise AI pilots showed no P&L impact, largely a trust problem (MIT)
92%
accuracy held on an enterprise Q&A system using multi-stage verification
>99%
of traffic resolved in under 100ms on a safety-critical path I built

Why this matters now

Roughly 95% of enterprise AI pilots showed no measurable P&L impact, and the trust gap is a big reason why. A confident wrong answer is worse than an honest "I do not know," and in regulated domains it is a liability. Hallucination is the single biggest blocker between a working demo and production.

How the reliable version works

  1. 1Retrieve the relevant source passages and ground the answer in them.
  2. 2Instruct the model to answer only from those passages and to cite them.
  3. 3Give the model an explicit escape hatch: "not in the source" is a correct answer.
  4. 4Verify each claim against the source (entailment) before returning the answer.
  5. 5Escalate low-confidence or unsupported answers to a human instead of guessing.

Common questions

Why do LLMs hallucinate?

A language model predicts plausible text, and plausible is not the same as true. With no grounding, it fills gaps with confident invention. Grounding it in retrieved source text and requiring citations removes most of the gap it would otherwise fill.

Does a bigger or newer model fix hallucination?

It helps at the margin but does not solve it. Even frontier models invent citations and facts. The fix is architectural: retrieval, grounding, citations, verification, and a real "I do not know" path.

How do you measure hallucination?

With a groundedness check: for each sentence of an answer, test whether the source actually supports it. That turns "it feels accurate" into a number you can track across deploys.

Related demos, work and services

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