Machine learning for America’s largest nutrition safety net
Americans depend on SNAP every month
of payments contain errors
in costs shifting to states by 2028
A system too important to get wrong
SNAP is the backbone of food 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.
Today, roughly one in nine SNAP payments contains an error — overpayments that trigger clawbacks, underpayments that leave families short, and processing mistakes that cascade through state budgets. With new federal legislation shifting financial liability to states starting in 2028, that 11% error rate becomes a $15 billion problem that no one is equipped to solve manually.
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.
Intelligence at the point of impact
Savor SNAP deploys machine-learning models directly into the SNAP payment pipeline — catching errors at the moment they happen, not months later during federal audits.
How it works
Error rate comparison
The result: a system that’s smarter, faster, and fundamentally more fair.
Built for the scale of the problem
We’re not building a pilot. We’re building infrastructure — machine-learning tools designed to operate across all 50 states, processing millions of transactions with the precision that SNAP recipients and state agencies deserve.
Our approach combines deep domain expertise in food assistance policy with production-grade AI engineering. We understand both the regulatory landscape and the technical architecture required to move the needle at national scale.
The future of SNAP starts here
Whether you’re a state agency preparing for 2028, a policy leader working on food security, or a technologist who believes in using AI for public good — we’d like to hear from you.