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Corgi Labs — Agent-channel attribution layer over Stripe payments: a dedicated agentic_transactions dataset that recovers the machine-initiated label Stripe destroys. Built by correlating Shared Payment Token objects with the PaymentIntents that consume them via webhook data, it persists SPT ID, PaymentIntent ID, merchant account and timestamp, and back-flags every downstream metric with an is_agentic indicator — including the historical back catalogue that merchant-side metadata tagging can never recover. It also captures SPT revocation and expiry events plus agent-side Radar risk signals, a…
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Agent-channel attribution layer over Stripe payments: a dedicated agentic_transactions dataset that recovers the machine-initiated label Stripe destroys. Built by correlating Shared Payment Token objects with the PaymentIntents that consume them via webhook data, it persists SPT ID, PaymentIntent ID, merchant account and timestamp, and back-flags every downstream metric with an is_agentic indicator — including the historical back catalogue that merchant-side metadata tagging can never recover. It also captures SPT revocation and expiry events plus agent-side Radar risk signals, and is explicitly building a dedicated agentic fraud model on those signals with agent-platform attribution (ChatGPT Instant Checkout, Microsoft Copilot Checkout, Perplexity)
Scale Low row countCoverage Financials · Consumer DiscretionaryAsset class Equities
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Corgi Labs — Agent-channel attribution layer over Stripe payments: a dedicated agentic_transactions dataset that recovers the machine-initiated label Stripe destroys. Built by correlating Shared Payment Token objects with the PaymentIntents that consume them via webhook data, it persists SPT ID, PaymentIntent ID, merchant account and timestamp, and back-flags every downstream metric with an is_agentic indicator — including the historical back catalogue that merchant-side metadata tagging can never recover. It also captures SPT revocation and expiry events plus agent-side Radar risk signals, a…
Corgi Labs offers (Alternative, Sentiment, Reference) — Low row count, uniquely high label value — row count is bounded by early-wave adoption, but each row carries a label that cannot be obtained elsewhere after settlement because the SPT is consumed. Two separately licensable assets: the transaction-level agent attribution itself, and the derived SPT risk-signal plus revocation/expiry event set feeding the planned agentic fraud model.
Training agentic-traffic fraud and risk models (the stated build target: a dedicated agentic fraud model on SPT risk signals, platform attribution and agent behaviour patterns); labelling agent-versus-human checkout for conversion, AOV and cohort analytics; benchmarking AI-shopping-agent commercial performance by platform; refund and chargeback models conditioned on agent origination; RL reward signals where an agent purchase has verified monetary outcome; alternative-data signals on AI-assistant commerce adoption for retailers and investors; supervising the repair of historical payment warehouses where the agent label was silently lost
Coverage spans US; Transaction & Payment Processing Services, Apparel, Accessories & Luxury Goods; alternative, sentiment, reference; equities.
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