Agents are only as trustworthy as the data and evals behind them.
Zero Proof Labs simulates your domain in milliseconds. Our optimization framework then tunes the simulation to maximize learning efficiency, cut GPU hours, and produce higher quality evals, before you spend a dollar on model training or harness optimization.
Safety judge for disputes and refunds
1B params
“There’s an $81.40 charge from Amazon I never made, I want it refunded.”
How it works
You bring the workflow. We build it as an environment and simulate it in milliseconds.
Our optimization framework tunes the simulation to maximize learning efficiency, cut GPU hours, and produce higher quality evals.
Only then do you spend a dollar on model training or harness optimization.
Proven on e-commerce. The safety judge below came out of this loop, with data, adapters, eval set, and serving cost public.
Worked example
We proved the framework on refunds and disputes, where a wrong decision costs a chargeback, and published every artifact. Rerun the evaluation against our numbers.
Accuracy against cost to serve
Near-frontier accuracy at about one hundredth of the cost.
Macro intent-type accuracy (%); zeroproof-ecommerce-1b on the full 1,977-row held-out set, frontier models on the balanced comparison subset. Frontier cost at published list prices; Zero Proof Labs cost from measured throughput on a single L4. The dashed line is one pass of post-training on the same base at the same serving cost.
The same method trains zeroproof-ecommerce-0.5b to within five points at half the size.