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In brief.
A hallucination is a false statement produced confidently by a generative AI. It is not a bug: the model generates the most statistically plausible text, not the truest. In a conversation, that is an inconvenience. In a tender or an audit questionnaire — documents in which every declaration binds the company that signs — an invented certification can mean disqualification, contractual penalties or exposure of the company’s liability.
A structural phenomenon, not a teething problem
An LLM is trained to produce a probable and satisfying sequence of words. When the requested information does not exist in its knowledge, it does not encounter a void: it fills it. It will happily write a convincing paragraph about your compliance with a standard you do not hold, because that paragraph resembles thousands of others seen during training. The smoother the wording, the harder the error is to spot on review.
The countermeasures are architectural
Restrict the answer universe to the company’s approved documents alone; require an identifiable source for every statement; attach a confidence score to every answer; and, above all, allow abstention — that is, flagging that the information is missing rather than inventing it. None of these mechanisms exists natively in an LLM used as is. One final, legal point: in most procedures the bidder is answerable for the accuracy of its offer; “the AI wrote it” is not a defence.
The Optivalue.ai approach
Optivalue.ai only allows an answer if it is backed by a source from the client’s content library, identified down to document and page. Where the evidence is missing, the platform says so, with a supporting score, and recommends how to close the gap before submission.
Can hallucinations be eliminated entirely?
They can be made rare and detectable: grounding on a closed corpus, mandatory sourcing, scores, abstention. The residual risk is managed through targeted review.
Who is liable for a hallucination sent to the client?
The signing company, in every case.
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