ProofMesh turns sensitive AI runs into ZKF-backed certificates. A customer, auditor, or security team can verify what was allowed, what was blocked, and which private boundaries were respected without seeing the private data itself.
Built by Kadropic Labs for teams that need proof, not another dashboard screenshot.
The certificate is not a promise. It is a verification object generated from proof material, commitments, policy checks, and signed execution receipts.
A log says what your system claims happened. ProofMesh is designed to create verification material that another party can check.
Most AI products store prompts, model names, timestamps, and tool calls. That helps debugging, but it still asks the buyer to trust your database and your reporting.
ProofMesh commits private inputs and workflow facts into a proof chain, then issues a certificate that can be verified without exposing the raw data.
ProofMesh is not only a certificate issuer. It is a ZKF-native pipeline that turns private AI execution facts into independently checkable trust objects.
Private inputs, approved sources, model versions, tool boundaries, and policies are committed before the certificate is issued.
ZKF proof material shows that the workflow respected selected constraints without revealing the underlying sensitive data.
Execution receipts, timestamps, policy decisions, and proof roots are bound into a signed certificate.
The customer or auditor checks the certificate through a verifier instead of relying on a PDF or a screenshot.
The buyer wants proof that the AI assistant used approved data, avoided restricted tools, and kept private inputs inside the allowed boundary.
An AI SaaS company wants to sell to enterprise customers. The buyer asks a simple question: How do I know my customer records were not used outside the rules?
ProofMesh gives the vendor a proof-backed certificate for each sensitive workflow. The vendor can share a verification result, not raw prompts or private documents.
{
"workflow": "support_agent_private_case",
"model_approved": true,
"data_boundary": "customer_vault_only",
"restricted_tools_used": false,
"private_inputs": "hidden_by_commitment",
"policy_proof": "zkf_policy_0x4a91",
"certificate_status": "verified"
}
Start with one workflow. Upgrade when certificates become part of your security, sales, or compliance process.
For builders testing ProofMesh on one AI workflow.
For AI teams that need security review and buyer-facing proof.
For vendors selling AI products into enterprise accounts.
For regulated teams that need private deployment and deeper ZKF work.
Overage: $20 per additional 10,000 certificates on Developer and Team. Business and Enterprise usage is quoted by volume.
ProofMesh should start with a real workflow that already matters: a support agent, RAG assistant, document reviewer, or internal operations agent.
Pick one AI flow where trust blocks sales, security approval, or compliance review.
Decide what must be proven: approved model, approved sources, private boundary, blocked tools, or policy satisfaction.
Route the workflow through ProofMesh and start generating ZKF-backed certificates.
Give customers, auditors, or security teams a certificate they can verify without receiving sensitive data.
ProofMesh is for claims about execution boundaries, policy satisfaction, model approval, source approval, and tool restrictions.
The certificate is designed to verify claims while keeping private inputs hidden behind commitments and proof material.
The value is the verifier. A certificate should be checked, not just viewed.
If your AI product needs enterprise trust, ProofMesh gives you a path from private execution to verifiable evidence.