Scoring offers with AI without losing the audit trail

The fear with AI in evaluation is reasonable: if a model assigns a score, can you explain it to a losing bidder, or to an auditor, six months later? If the answer is no, the speed isn't worth it.

The resolution is to treat the AI as a reader, not a judge. It extracts the relevant evidence from each offer, maps it to the published criteria, and proposes a score — with the exact passage it relied on attached to every line.

Every score carries its receipt

A defensible AI score has three properties: it cites the source text it came from, it shows the weight applied, and it can be overridden by a human evaluator who owns the final call. Nothing is a black box, because every number traces back to a quotation and a rule you set in advance.

Done this way, AI doesn't dilute the audit trail — it strengthens it. The rationale that used to live in someone's head is now written down, consistently, for every supplier.

If a score can't show its source, it isn't a score. It's a guess with a number on it.

See how Procurato does it →