Purpose-built AI for SNAP payment accuracy
SNAP payment error reduction to protect state budgets before FY 2028
The cost of inaction
Americans depend on SNAP
USDA FNS
of payments contain errors
FY 2024 QC Report
projected state liability by FY 2028
OBBBA provisions
Based on FY 2024 rates, projected since Jan 1, 2026
At the current error rate, states collectively accumulate ~$365 in new liability every second.
A system too important to get wrong
SNAP is the backbone of nutrition security in America. It reaches 42 million people across every state, every county, every zip code. But the program’s payment infrastructure hasn’t kept pace with its scale. Without modern SNAP error detection, EBT payment accuracy continues to decline.
Today, roughly one in nine SNAP dollars is issued incorrectly — overpayments that trigger clawbacks, underpayments that leave families short, and processing mistakes that cascade through state budgets. Under the One Big Beautiful Bill Act, new cost-sharing rules take effect in FY 2028. At the current ~11% national error rate (FY 2024), states collectively face billions in new annual costs with no scalable solution in place.
The people who lose aren’t abstractions. They’re parents choosing between groceries and rent. Caseworkers buried in corrections. State agencies facing budget shortfalls with no clear path forward.
Where we are today
Savor Snap is conducting cross-state research with SNAP directors, QC teams, and policy leaders across the country, studying how payment errors originate, persist, and compound differently in each state. We’re combining these findings with applied AI/ML research to evaluate where intelligent error detection can have the highest impact. Our 50-state analysis will be shared openly with every state we work with.
SNAP error rates by state
SNAP payment error rates vary widely across state SNAP agencies. Under the One Big Beautiful Bill Act (OBBBA), states face tiered cost-sharing obligations based on their error rates starting in FY 2028.
Data based on USDA FNS FY 2024 Quality Control error rate reports. Cost-share estimates based on OBBBA tiered provisions.
Intelligence at the point of impact
Savor Snap is building machine-learning infrastructure designed to integrate directly into the SNAP payment pipeline — a real-time error correction system designed to catch mistakes at the moment they happen, not months later during federal audits.
How it works
Savor Snap is currently in the development stage. The pipeline architecture shown reflects our target system design, not production metrics.
The result: a system that’s smarter, faster, and fundamentally more fair.
Built for the scale of the problem
Savor Snap builds machine-learning tools that detect payment errors, correct them in real time, and protect the integrity of America’s largest nutrition safety net. SNAP serves 42 million Americans across every state, but the payment infrastructure hasn’t kept pace with the program’s scale. With federal legislation shifting error liability to states by 2028, the need for intelligent, automated error detection has never been more urgent.
We combine deep domain expertise in food assistance policy with production-grade AI — building infrastructure designed to operate across all 50 states. Unlike legacy analytics platforms built for retrospective auditing, Savor Snap is designed to operate in real-time within the payment pipeline, catching errors at the moment of impact.