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Pace | Building Reliable Insurance Agents

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At Pace, we believe successful agents in production mirror how human experts actually work: selective attention, cross-referencing, spatial reasoning. Insurance workflows can be particularly challenging. Typical documents can span hundreds of pages with large amounts of context and tabular data. Relationships can be complex and subtle. To tackle this, we built COR: our Contextual Reasoning engine, enabling our agents to process thousands of insurance workflows accurately and reliably for our customers. We tried traditional RAG: semantic chunking, embedding similarity, vector databases, retrieve top-K passages. In some cases it performed better than providing full documents, but it still fell short for many use cases. RAG is great for document search, but our insurance workflows already have a fixed set of inputs. We didn't need broad document discovery, we needed strong contextual reasoning and retrieval within a defined, complex dataset. For example, semantic embeddings can recognize

Introduction At Pace, we believe successful agents in production mirror how human experts actually work: selective attention, cross-referencing, spatial reasoning. Insurance workflows can be particularly challenging. Typical documents can span hundreds of pages with large amounts of context and tabular data. Relationships can be complex and subtle. To tackle this, we built COR: our Contextual Reasoning engine, enabling our agents to process thousands of insurance workflows accurately and reliably for our customers. RAG Solves the Wrong Problem We tried traditional RAG: semantic chunking,…

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