How We Built a Zero-Hallucination Claims Engine

In insurance, a false positive is expensive. A fabricated fraud flag triggers an investigation that wastes adjuster time, delays claim payment, and damages the customer relationship. A hallucinated medical assessment could lead to wrongful claim denial. The stakes are too high for 'good enough' AI.

This is why we built the Oasis with a zero-hallucination architecture. Every AI decision must be traceable to evidence in the claim data. Every flag must cite specific dates, amounts, codes, or documents. Every assessment must reference policy language. If the evidence isn't in the data, the flag doesn't exist.

The architecture has three key constraints. First, our FWA agents are limited to a maximum of 8 flags per claim. This forces quality over quantity — every flag must clear a high evidence bar. Second, our assessment agents must cite the specific policy clause that supports their decision. No clause, no decision. Third, all extracted data includes bounding box coordinates pointing to the exact location in the source document.

Most AI companies treat accuracy as a tuning problem — adjust the model until false positives are acceptable. We treat it as an architectural constraint. The system is designed so that hallucination is structurally impossible, not just statistically unlikely.

The practical impact: our agents process claims with the same evidence standards that a senior adjuster would apply, but at the speed of software. When they're uncertain, they escalate to human review — they don't guess. This is why insurers trust us with production claims, not just pilot programs.

The zero-hallucination guarantee isn't a marketing claim. It's an engineering decision that shapes every prompt, every tool, every validation step in our 17-agent pipeline. It's the foundation everything else is built on.