Corrective Agentic RAG with Self-Reflection for Commercial Insurance Claims
Client identifiers anonymized under strict Non-Disclosure Agreements (NDAs). Performance metrics reflect architectural benchmark simulations.
About the Project
This platform was engineered for a commercial property and casualty insurance underwriter processing multi-million-dollar commercial liability claims, loss notices, and complex multi-hundred-page policy binders.
Claims adjusters needed to cross-reference submitted incident reports against policy endorsements, coverage riders, and statutory exclusions. The goal was to build an intelligent assistant that could surface definitive coverage terms while actively preventing hallucinations on exclusionary clauses.
Challenges We Faced
1. Subtle Exclusionary Clauses Missed by Naive Similarity Search
Commercial insurance binders are notorious for cross-referenced riders ('Exclusion 4.b(ii) supersedes Section 2.1'). Standard single-turn vector search retrieved the general coverage paragraph while completely missing the critical exclusion clause located 70 pages later, leading to false coverage confirmations.
2. Plausible Hallucinations Under Incomplete Documentation
When policy documents were missing from the index, standard language models tended to generate plausible-sounding coverage terms based on generic industry knowledge rather than explicitly stating that the relevant policy rider was missing.
3. Unbounded Latency in Multi-Hop Agentic Loops
Early prototypes that allowed autonomous agents to explore documentation unconstrained suffered from cascading latency spirals, taking up to 45 seconds per query and making the tool impractical for live adjuster workflows.
Solution Architecture
Acadify engineered a Corrective RAG (CRAG) architecture paired with an explicit graph verification layer, delivered under our custom AI development services practice.
The system treats retrieval as an iterative evaluation loop: documents are graded for semantic sufficiency, ambiguous queries are reformulated automatically, and policy riders are validated against an explicit graph of exclusions before any generative synthesis begins.
- Automated Document Relevance Grading: Integrated a distilled evaluation model that scores retrieved chunks for factual relevance. Chunks that fail relevance thresholds are discarded, and an automated query rewriter expands ambiguous terminology using industry synonym taxonomies.
- Deterministic Neo4j Exclusion Graph: Built a property graph mapping policy sections directly to their respective endorsements and exclusions. Whenever a general coverage clause is retrieved, the engine executes a deterministic graph traversal to inject all associated exclusion riders into the prompt context.
- Bounded Latency & Structured Refusal: Enforced a strict 3-iteration cap and a 4-second timeout. If the agent cannot substantiate an answer from retrieved evidence within the budget, it outputs a structured refusal identifying the exact missing policy binder rather than guessing.
Why Acadify's Engineering Approach Fit the Project
In high-liability insurance workflows, a single hallucinated coverage term can lead to bad-faith litigation or catastrophic claims exposure. Acadify replaced probabilistic guesswork with a dual-layer architecture: pairing statistical vector search with deterministic graph validation and corrective agentic grading.
System Architecture & Tech Stack
Related Acadify Solution Services
This case study demonstrates capabilities from Acadify Solution's AI Development practice, specifically multi-agent corrective RAG with self-evaluation loops. For teams building AI products, see our Build AI Product solution. For AI testing and evaluation approaches, explore AI Testing & Evaluation.