Agent-mediated coverage selection and demand-and-needs
Comparing headline premiums without matching exclusions, excess, claims triggers and eligibility creates inappropriate cover risk.
Executive question
Can the agent explain why a policy is suitable for this customer, not merely cheap?
What to measure
- Coverage mismatch rate in controlled briefs
- Evidence recall for exclusions
- Disclosure acknowledgment
- Reason-for-selection trace
Relevant products and standards
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.
Real-time car insurance quote comparison inside ChatGPT, live in 11 US states with a rollout to 40+ states planned for 2026. Choosing, advice and buying the policy remain separate steps.
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.