Corgi Labs | AI-Powered Payment Optimization & Revenue Recovery

What

Corgi Labs provides AI-powered payment optimization and revenue recovery tools for businesses that want to approve more legitimate transactions while reducing fraud-related losses. Its offering appears to focus on payment decisioning and analytics for companies that process online payments, including ecommerce, subscription, SaaS, gaming, travel, ticketing, gift card, and cross-border businesses.

The product is positioned as an overlay to an existing payment processor rather than a replacement, with setup described as a simple plugin and no development work required. The workflow centers on two products: Corgi Intelligence, which surfaces where revenue is being lost in the payment flow, and Corgi Model, which applies custom machine learning on a company’s transaction data to improve approval decisions.

Features

• Custom payment decisioning model — Uses machine learning trained on a business’s own transactions to help distinguish legitimate buyers from fraud more accurately.
• Revenue loss analytics — Shows where customers drop off in the payment process so teams can identify avoidable declines and conversion leaks.
• Fraud trend detection — Helps teams spot fraud patterns faster, which can support quicker rule or policy adjustments.
• Approval optimization — Aims to increase acceptance of legitimate orders while reducing unnecessary rejections of good customers.
• Processor-side deployment via plugin — Connects to an existing payment processor through a simple plugin, which should reduce implementation effort according to the site.
• Support for recurring revenue recovery — Positioned for subscription and usage-based businesses that need to reduce payment-related revenue loss over time.

Helpful Tips

• Validate fit by payment profile — This type of product is most relevant for merchants with meaningful decline rates, fraud pressure, chargebacks, or recurring billing complexity.
• Review current decision logic first — Before adoption, document existing processor rules and manual review processes so any lift can be measured against a real baseline.
• Segment by business model — Ecommerce, digital goods, travel, and subscription businesses often have different fraud and approval patterns, so implementation should reflect those differences.
• Ask for clarity on model governance — The page states custom machine learning is used, but buyers should still confirm how performance is monitored, retrained, and explained internally.
• Plan around operational ownership — Payment optimization usually works best when fraud, finance, and growth teams share responsibility for reviewing insights and adjusting policy.