Machine-readable insurance product and coverage evidence
Agent-facing product data must join authoritative coverage definitions, exclusions, triggers, prices, local regulation, and eligibility; content pages alone are insufficient.
Executive question
Can independent agents reconstruct correct protection from our disclosures?
What to measure
- Coverage/exclusion factual accuracy
- Version and source provenance
- Unanswerable demands-and-needs questions
- Cross-channel contradiction counts
Relevant products and standards
ACORD's insurance data standards, including P&C (XML, AL3) and Global Reinsurance & Large Commercial (XML, JSON), covering placing, policy, claims and settlement data.
A working draft, stewarded by Marrow, for how AI agents quote, compare and buy UK regulated insurance, with rules on consent, disclosures, data use and audit.
Measures how AI systems recommend brands at the decision point across multi-turn conversations, identifies where a brand is found but not chosen (the Linkage Gap), and remediates it.
Lets AI agents get insurance quotes and hand the purchase back to the insurer with the required disclosures. Production use is Marrow's own claim. Marrow also stewards the AMI draft standard.
Relevant examples, not documented integrations, completeness, certification or endorsement. Listed alphabetically.
Evidence and current limits
Strategic pathway; not an observed market-wide adoption rate.
Draft analysis, under editorial review.