How AgentFlow Cut Underwriting Cycle Times For a Regional Insurer

THE PROBLEM

Underwriters Struggled to Access Critical Knowledge Fast Enough

This regional insurer faced a growing operational challenge. Underwriters needed fast, accurate access to manuals, guidelines, and rules, yet the information lived across large PDF documents that were difficult to navigate. 

Experts spent valuable time searching and re-reading documents, which slowed decision-making and reduced consistency.

Market pressures intensified the issue. As regulatory requirements, document volume, and workflow complexity increased, the need for a cohesive knowledge base became urgent. Without effective knowledge management, underwriters risked inconsistent interpretations and delays. Daily work involved manual searches, interpretation differences, and repeated efforts to confirm answers across documents.

Traditional tools were not built for this environment .The insurer needed an AI solution that could deliver instant, accurate answers from company knowledge, enforce access controls, handle sensitive information, surface clean citations, generate audit-ready logs, and support trust and governance—requirements that made tools without explainability or precise sourcing unsuitable for adoption.

THE SOLUTION

A Secure AI Knowledge Assistant Built for Fast, Trustworthy Retrieval

The insurer partnered with Multimodal to run a four-week proof of concept focused on evaluating a conversational AI knowledge assistant for underwriting. 

The team ingested nearly 1,000 pages of underwriting documents into a secure knowledge base, creating the foundation for an assistant capable of answering underwriter questions in natural language .

Secure Knowledge Base Creation

Documents were processed and structured to make information accessible in seconds instead of minutes. Sensitive information remained protected through strict access controls.

Conversational Assistant With Explainable Answers

Users typed questions in natural language and received accurate answers with citations pointing directly to the most relevant documents. This reduced friction and improved decision consistency.

Citation Refinement For User Trust

During SME review, the team learned that too many citations created noise. Retrieval logic was adjusted to display only the three most relevant sources. This significantly increased trust and made validation easier.

SME-Driven Improvement Cycle

Underwriting experts reviewed answers and provided targeted feedback. Their insights refined the assistant’s performance, ensuring the system reflected the organization’s voice, reasoning, and underwriting standards.

Hands-On Evaluation Path

The next phase involved granting users direct access to the assistant to test scenarios and build confidence in its accuracy. This became a key requirement for final validation and future rollout.

THE RESULTS

Improved Accuracy, Faster Answers, and Greater Operational Confidence

The proof of concept confirmed that the AI assistant produced correct answers aligned with underwriting expectations. Subject matter experts validated the core accuracy of responses and confirmed that the assistant’s reasoning aligned with their guidelines and rules .

More Reliable Knowledge Retrieval

Clean, relevant citations increased trust. Underwriters could verify rules quickly and confidently, reducing the time spent manually searching documents.

Greater Consistency Across Underwriting Decisions

With a single, authoritative way to access company knowledge, the team applied rules more uniformly. This helped reduce interpretation gaps and supported stronger governance.

Time Savings Through Instant Answers

The assistant replaced slow manual searches with rapid retrieval, enabling underwriters to focus on decision-making rather than document digging.

A Foundation For Future Agentic AI

The success of our initial collaboration created internal momentum. The insurer identified clear next steps, including refining retrieval across states, enabling live hands-on use, and integrating with document repositories for automatic updates. These advancements prepared the organization for broader deployment into more complex workflows.

About The Customer

This regional insurer provides property and casualty coverage across multiple states. The company is committed to accurate underwriting, strong customer service, and modern operational practices. With a rapidly growing body of documents and increasing workflow demands, the organization is investing in AI-driven knowledge management to support speed, accuracy, and long-term scalability.