ReferralProof
A DTC attribution layer that classifies AI-referral evidence, joins consented sessions to orders, reports uncertainty and model choices, and signs reproducible monthly channel ledgers.
AI-referred shopping traffic is growing, while default analytics groupings can bury it under direct or other channels. Research confirms several aggregate conversion and order-value premiums, expensive enterprise attribution tools, and no direct SMB product focused on AI-channel evidence. ReferralProof collects permitted referrer, campaign, landing, session, order, and server evidence, then reports results under explicit attribution rules. Aggregate market findings do not prove a premium for a specific merchant. Missing referrers, redirects, privacy controls, cross-device behavior, agent checkout, and self-referral make channel assignment uncertain. A signed ledger proves which data and method produced the report; it does not prove the AI surface caused the visit, order, conversion rate, average order value, or revenue.
The ecommerce growth, analytics, finance, or marketing operations leader at an SMB or mid-market DTC brand measuring AI-referred traffic.
Three 2026 datasets show strong traffic and conversion movement, creating a current reporting window.
Collection, classification, joins, reports, and signatures are code-scalable.
One cross-reference and no inbound connections provide limited convergence.
Multiple independent data sources support the traffic trend and aggregate premium, enterprise pricing leaves an SMB gap, and attribution reporting scales through software.
No interfaces were verified in the original stage, the buyer is underspecified, channel evidence is lossy, the moat is thin, and incumbents can add AI-referral grouping.
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