Data Hatch
An agentic system that reads, validates, and adjudicates healthcare claims. Routine cases follow policy rules; complex ones reach handlers with the evidence already assembled.
See the AI claims management software this became ↗Claims arrived as PDFs, emails, and scanned post. Each one was read, cross-checked across several systems, and decided by hand: days per claim, inconsistent outcomes, and a backlog that tied up senior handlers on routine work.
The trust needed routine claims to move without human touches, and complex ones to reach the right person with context attached.
An LLM extraction layer parses every incoming document into a structured claim record.
Cross-checked against membership, coverage, and tariff schedules. Missing information is requested automatically.
Each claim is scored for anomalies, tariff variance, and complexity. Every score is explainable.
Claims within thresholds set by the trust settle automatically: decision, rationale, and payment instruction drafted by the agent.
Flagged claims reach the right handler with evidence, risk profile, and a draft assessment attached. No claim arrives cold.
Every step is logged as it happens, to IG standards. Handler overrides feed back into the models.
Handlers only see claims that need judgement.
Policy rules guide the assessment, with exceptions routed for review.
Complex cases reach a handler with supporting evidence already assembled.
The assessment records its rationale and the sources used at decision time.