NexArt is verifiable execution infrastructure for AI systems. Every run produces a Certified Execution Record: a tamper-evident, cryptographically signed artifact that anyone can verify independently.
Free to create. Paid to certify. Public to verify.
One function call. No infrastructure change. Start certifying in under an hour.
Today, most AI executions leave no independently verifiable record. Logs can be edited, deleted, or fabricated after the fact. Inputs and outputs are rarely bound together. When something goes wrong, there is no verifiable link between what was requested and what was returned. Without execution-level evidence, trust depends entirely on the operator. Auditors, partners, and regulators have nothing to verify independently.
Most teams discover this gap during an audit or after a client dispute. By then, the executions they need to prove are already gone.
Verifiable execution evidence for every AI workflow
Every execution produces a Certified Execution Record (CER) that binds inputs, parameters, and outputs into a single tamper-evident artifact. Independent attestation nodes verify integrity and issue signed receipts. Recompute the hash. Check the signature. No API key, no account, no dependency on NexArt.
What NexArt is, and what it is not
NexArt proves what executed. It does not claim the output is correct. Verification is about integrity of the record, not the quality of the model. This is not observability. It is verifiable execution evidence.
NexArt is
An execution evidence layer for AI systems
A cryptographic proof system (SHA-256 + Ed25519)
Independent verification infrastructure
A record format anyone can verify without an account
NexArt is not
A logging or observability tool
A monitoring or analytics dashboard
A model provider or evaluation framework
A correctness or quality guarantee
Execution is certified. Context explains the decision.
Inputs, outputs, and parameters are sealed into the Certified Execution Record. Optional execution context, such as routing, retrieval, or tool signals, attaches to the same record as an explanation layer. When included, context is bound to the seal. The record proves not just what ran, but how the decision was reached.
Built for how you work
Builders: Integrate verifiable execution into your AI stack. Free tier, SDK, CLI, and full API access.
Compliance & Risk: Audit-grade execution evidence aligned with ISO 42001, SOC 2, NIST AI RMF, and the EU AI Act.
Enterprise & Regulated Workflows: Private attestation nodes, SLAs, retention policies, and architecture walkthroughs for regulated environments.
From execution to proof in four steps
Capture: Inputs, outputs, and execution context recorded at runtime.
Seal: SHA-256 hash binds all protected fields into a tamper-evident record.
Attest: Independent node signs the record with an Ed25519 receipt.
Verify: Verify a single CER or an entire Project Bundle. Anyone confirms independently.
What actually gets verified
In most systems, inputs and outputs are logged separately, if at all. A Certified Execution Record binds the parts of a run that matter for proof. Everything else stays available for review, without being part of the seal.
Sealed in the proof: Inputs, outputs, and parameters are bound together by a single cryptographic hash. Change any one of them and the record no longer verifies.
Recorded as context: Execution context like model, version, timestamp, and environment is captured alongside the seal so reviewers can see exactly what ran.
Available as evidence: Optional signals and supporting data attach to the record for review. They may not always be part of the sealed proof, but they remain inspectable.
One workflow. Many executions. A single verifiable artifact.
Real systems aren't a single call. Agents plan, retrieve, decide, and act across many steps. NexArt groups those steps into a Project Bundle.
Each step gets its own Certified Execution Record. The Project Bundle ties them into one independently verifiable artifact for the whole workflow, with a single Project Hash that verifies the entire run.
As AI systems move into production, the question shifts from "does it work?" to "can you prove what it did?" The teams that answer that question now set the standard. The rest explain gaps later.
AI auditability: Show exactly what ran, when, and with what inputs and outputs. The record can be validated independently, even outside your system.
Compliance evidence: Audit-grade records aligned with ISO 42001, SOC 2, and the EU AI Act.
Debugging with proof: Reproduce failed runs from a tamper-evident record, not a log file. No stitching together data from multiple systems.
Trustworthy agents: Multi-step agent workflows with verifiable decision trails end-to-end.
For systems where proof matters
AI agents & tool-calling workflows: Verifiable decision trails for autonomous tool calls, chain-of-thought workflows, and multi-step pipelines.
Approvals & policy enforcement: Prove that a decision followed the right inputs, parameters, and policy constraints with cryptographic evidence.
Compliance & audit readiness: Audit-grade execution records for regulated industries. Reproducible, independently verifiable, retention-ready.
See how NexArt fits your architecture, compliance requirements, and execution environment. No sales pitch. Just a technical walkthrough on your use case.