Proof of intent
Let agents transact on your customers’ behalf, with a verifiable record of every action. Runs in your transaction path. Works with the agents and tools you already use.
Customer
“There’s an $81.40 charge from Amazon I never made, I want it refunded.”
Intent
zeroproof-ecommerce-1b
Proof
VerifiedSigned by the agent and the tool server.
sha256:7f3a…1b2
Intent
Read what the customer actually asked for, before the agent acts.
Proof
Attest what the agent did, signed by the agent and the tool server.
The loop
Every action we catch trains the next judge. Models get replaced; the loop is what we keep.
Language models are already capable of transacting on a person’s behalf; however, financial errors are where tolerance for AI mistakes ends.
The barrier is not capability but verification.
Delegation becomes safe when three questions can be answered about any agent action:
Our research answers the first two before execution, by reading intent from the conversation, and the third after it, with a cryptographic proof. The result is a trust layer in which every answer is a signed, independently checkable claim.
We train small models that recover a customer’s true intent from conversation, before an agent acts on it.
One transaction through the trust layer: intent read before execution, a signed proof issued after, and a mismatched action stopped before it reaches the payment rails.
We build the cryptographic record of what an AI system did and produced. Each action carries a zkTLS proof signed by both the agent and the tool server.
The same primitive applied to content: each hop signs what it produced, the publisher verifies before publishing, and the disclosure the AI Act requires is carried as verifiable evidence.
Two models fine-tuned for e-commerce payment intent: rank-16 QLoRA adapters over gemma-3-1b-it and qwen2.5-0.5b-instruct, served from a single L4 behind an OpenAI-compatible vLLM endpoint. Weights, training data, and the evaluation set are on Hugging Face.
Accuracy vs cost to serve
Near-frontier accuracy at about 1/100th the cost.
Macro intent-type accuracy (%), identical balanced held-out rows for all models. Frontier cost at published list prices; ZeroProof cost from measured throughput on a single L4.
The same data trains zeroproof-ecommerce-0.5b to within five points at half the size.