One audit. Three decisions.
Built for merchants and agencies that need to know what an AI agent can discover, what it may attempt, and what evidence actually proves commercial impact.
01 / FIND
Discovery and catalog truth
Inspect public agent instructions, robots policy, structured profiles, product identity, price scope, freshness, route availability, and the audience, use-case, differentiation, and value evidence an agent needs to make a recommendation.
02 / BOUND
Policy and authority
Separate technical capability from agent identity, customer-account ownership, delegated scopes, connector-specific access, buyer approval, publish/send/spend approval, rail-specific budget enforcement and revocation, provider automation and human-presence rules, policy effective dates, qualified review and disclosure duties, physical-action stop controls, AI-plan or credit consent, vendor fees, data sharing, payment authorization, replay-safe receipts, and intent-to-effect consistency.
03 / PROVE
Attribution and revenue
Distinguish paid placement, affiliate influence, sponsorship disclosure, recommendation independence, ad clicks, conversion data, incremental lift, cart readiness, completed checkout, delivery, settlement, reversals, costs, and collected net revenue.