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A government financial program needed to explore how AI could assist underwriters reviewing supplier submissions against defined eligibility criteria.
Every recommendation needed supporting evidence, a confidence score and a clear path back to the source document. Final authority remained with underwriting personnel.
Supplier information arrived in PDF and Word documents.
Relevant evidence had to be found across multiple document sections.
Recommendations had to follow defined eligibility criteria.
Each result needed evidence that reviewers could verify.
Missing or conflicting information needed to be identified for review.
Results had to remain clear for non-technical stakeholders.
workMETHODS developed a pilot that accepts supplier documents, extracts their content and prepares it for AI review. Retrieval-Augmented Generation retrieves relevant document sections before the AI evaluates the case against defined criteria.
The system returns a structured recommendation with a 0–100% confidence score, short explanation and supporting evidence. Evidence includes the source document and extracted text so reviewers can check how the recommendation was reached.
The pilot also identifies missing information, unclear evidence and conditions that may require manual verification. Underwriters remain responsible for the final decision.
See how RAG, source evidence and human oversight can support risk review, eligibility checks and other high-value business workflows.
Explore the complete case study for more context, details, and a closer look at the project.