Redefine intelligent document processing using native AI agents. Schedule a demo to try SIYA today

AI-Assisted Underwriting Case Study
Case Information
Industry:
Government and Public Sector
Business Type:
Government Financial Agency
Services:
AI-Assisted Underwriting, Agentic AI, RAG, Document Analysis, Evidence Retrieval and Confidence Scoring
Client Profile

Supporting Evidence-Based Government Underwriting

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.

Technical Challenges

Turning Supplier Documents Into Traceable Underwriting Evidence

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.

Our Solution

AI-Assisted Underwriting with Evidence-Based Recommendations

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.

Put Explainable AI Into Complex Business Reviews

See how RAG, source evidence and human oversight can support risk review, eligibility checks and other high-value business workflows.

Book a Free Demo

  • Evidence-backed recommendations for underwriting review
  • Source references and evidence snippets for each evaluation
  • Confidence scoring from 0–100%
  • Clear identification of missing or conflicting information
  • Human oversight retained for final underwriting decisions

  • Upload and process 1–5 PDF or Word documents per case
  • Automatic document text extraction and content chunking
  • Retrieval-Augmented Generation for document-based AI analysis
  • Vector-based search for relevant document sections
  • Structured recommendation and confidence score
  • Evidence snippets with source document references
  • Gap analysis for missing or unclear information
  • Risk flags for low confidence, conflicting evidence and insufficient documentation
  • Target response time of approximately 10–15 seconds
  • Lightweight agentic process for reading, retrieving, evaluating and reporting

  • Angular
  • Java with Spring Boot
  • Amazon Web Services (AWS)
  • Retrieval-Augmented Generation (RAG)
  • Vector Database
  • Large Language Model Integration

Download the Case Study to Learn More

Explore the complete case study for more context, details, and a closer look at the project.