Check AI output before it reaches your records

An AI-generated assessment can sound credible while relying on a statement that is missing from the source document. If that assessment enters a business record, another person may act on it without knowing where the supporting evidence came from.

This creates a review problem for teams using AI to process applications, claims or other documents. They need a way to inspect the basis of a result before using it.

Our recruitment application, currently in development, addresses part of this problem by checking quoted evidence against the source résumé. Reviewers see the assessment alongside the source material and the result of that check.

Connect an assessment to its evidence

Consider an illustrative application review. An assessment says that the applicant has experience managing a team and supplies a quotation as evidence.

The application checks whether that quotation appears in the résumé. If it is not found, the interface identifies the missing match. The reviewer can then inspect the source and decide whether the statement has adequate support.

A matching quotation confirms that the quoted text occurs in the document. The reviewer still needs to assess whether it supports the conclusion and is relevant to the role.

Reduce unnecessary personal information

The application also replaces supported identity details before sending résumé text to the selected AI provider. This reduces the personal information included in the assessment request.

This measure does not make the document anonymous. Other identifying information can remain, and the assessment still requires appropriate access, disclosure and review arrangements.

Make review more focused

Reviewers can follow an assessment back to its stated evidence and the original document. A missing quotation gives them a specific discrepancy to investigate before they use the result.

The same principle appears in SplitYours receipt checking. There, the supporting evidence is numerical: extracted item amounts are compared with the receipt summary. Text recognition and AI assessment have different failure modes, but both benefit from checks that can be explained to a reviewer.

These checks support human review. They do not establish that an assessment is accurate, fair or suitable for an employment decision.

Apply evidence checks to other business records

Possible applications include:

• Insurance and claims operations: compare a proposed summary with the submitted documents and identify statements that need review.
• Procurement: connect extracted supplier information to the quotation or specification from which it was taken.
• Customer service: check a proposed case summary against the messages or records available to the team.

Each process needs a defined source, acceptance rules and a responsible reviewer. A quotation check is one part of that design. It cannot resolve missing documents or determine whether the source itself is correct.

For a custom implementation, we would begin with the decisions your team makes and the evidence it needs. That establishes which checks can be automated and where a person must assess the result.