Instant payment systems have made it easier for businesses to get paid, but they also leave little room for error. When a transaction fails because of a data, routing, authorization, or timing issue, the sale can be lost before the business has a chance to intervene. SpurPay, an early-stage venture founded by Zhengxi Tan, is being built to close that gap by using artificial intelligence to identify payment failures in real time, diagnose the underlying cause, and guide the transaction toward a recovery path before the error reaches the merchant or customer.
SpurPay presented at “One-Person Unicorn: The Soloist’s Ascent | 2026 Pitch Day” in New York City on June 27, 2026. The company took second place in the Audience Favorite category and received a $300 cash prize. The organizers said the Audience Favorite results reflected both on-site audience votes and judging-panel scores.
The problem SpurPay targets is an increasingly critical bottleneck for merchants. Real-time payment rails move quickly and leave almost no window for manual correction, so a single failed charge can turn directly into lost revenue, an abandoned cart, or a gap in cash flow. SpurPay estimates that failed payments cost businesses around $118.5 billion globally each year, including roughly $44.4 billion in U.S. retail and hospitality. For companies processing large volumes of transactions, even a small share of preventable failures can add up quickly.
SpurPay is engineered to solve this friction through a three-layer system: a Smart Recovery System that manages real-time transaction error detection, telemetry orchestration, and failure classification; Smart Payment Recovery Models that apply advanced machine learning algorithms to evaluate failure vectors and prevent permanent transaction drops; and Autonomous AI Agents that coordinate retries, routing adjustments, and recovery decisions at transaction speed.
The company’s goal is to make payment recovery happen before a failed transaction becomes visible to the business or its customer. Over time, SpurPay intends for its AI agents to improve as they process more transactions, allowing the system to recognize recurring failure patterns and resolve more issues without manual intervention. For businesses, the intended result is fewer failed payments and dropped sales, lower payment processing friction, and predictable cash flow.
SpurPay is aimed at businesses where payment failure carries direct financial consequences, including retail and hospitality, logistics and manufacturing, and other payment-heavy enterprises. The company plans to begin with performance-based pilots, charging a small share of the revenue it recovers. Once the value is demonstrated, SpurPay expects to move customers onto recurring subscriptions. It describes its early market as high-volume U.S. businesses with visible payment leakage and a measurable return on recovered revenue.
Zhengxi Tan, the founder of SpurPay, brings a background in AI and machine learning model infrastructure, inference optimization, real-time payment recovery, and distributed financial systems. He studied computer science at the University of Michigan and has spent several years building large-scale, highly efficient and reliable financial infrastructure and payment recovery systems that have successfully reclaimed tens of millions of dollars in transaction volume. SpurPay’s product direction reflects that deep domain expertise, applying advanced AI infrastructure to reinforce the efficiency and reliability of modern financial rails.
SpurPay is still in its early stages, but its focus reflects a shift in payments infrastructure, where payment failures are transformed from lost revenue into automated, recoverable events.