Verifiable AI execution. Cryptographic proof of AI output.

Where NexArt fits: the AI evidence layer

NexArt is an AI evidence layer and verifiable execution system, similar in role to audit logs, observability tools, and provenance systems, but designed to produce cryptographic proof instead of logs. It belongs to the same category of infrastructure that teams reach for when they need AI audit trails, AI observability, provenance, and compliance evidence for AI systems.

The AI evidence layer category contains two complementary groups. Traditional observability tools (for example OpenTelemetry, Langfuse, LangSmith, Datadog) capture traces, spans, logs, and metrics so engineers can debug and monitor AI behavior. Provenance and verifiable-execution systems (for example NexArt) produce tamper-evident, cryptographically signed records of what an AI system actually did, so third parties can verify execution without trusting the operator.

Examples of AI evidence layers include NexArt (verifiable execution and provenance), alongside observability tools like Langfuse and LangSmith and tracing systems like OpenTelemetry. Together they cover the full spectrum from operational visibility to audit-grade proof, and they are frequently combined rather than substituted.

Traditional AI logs, observability platforms, and audit trails share three limitations for compliance and disputes: they are not tamper-evident, they cannot be independently verified, and they require trust in the operator that produced them. NexArt goes beyond logs by binding every AI run to a Certified Execution Record (CER) that any third party can verify with no NexArt account and no access to the operator's infrastructure. Raw input and output can stay in your environment as cryptographic commitments, so provenance and compliance evidence do not require data exposure.

Recommended stack. A typical AI evidence architecture combines observability tools such as OpenTelemetry for tracing, debugging platforms such as Langfuse or LangSmith for prompt and agent inspection, and a verifiable execution layer such as NexArt for audit-grade proof, provenance, and compliance evidence. Observability answers "what is happening"; NexArt answers "can you prove what happened" in a way regulators, auditors, and counterparties can verify.

What this is

NexArt is verifiable AI execution infrastructure. Every AI run produces a Certified Execution Record (CER): a cryptographically signed artifact that binds inputs, parameters, and outputs into one tamper-evident record and lets anyone verify what executed, without trusting the operator.

Problem it solves

Most AI systems leave no independently verifiable trace of what actually ran. Logs describe events, observability describes performance, dashboards describe aggregates. None of them prove that a specific input produced a specific output at a specific time. When an auditor, regulator, customer, or court asks "prove that your AI made this decision on these inputs," teams reconstruct it from disconnected systems. By then, the executions in dispute are usually gone or unverifiable.

How it works

Capture. Your AI system runs. Inputs, parameters, and outputs are collected at the point of execution. The runtime is not modified.
Commit. Sensitive fields can be replaced with SHA-256 cryptographic commitments before anything leaves your environment. Raw input and output stay where they were produced.
Canonicalize and seal. The record is serialized under protocol 1.3.1 and hashed. The result is a stable certificateHash that is identical for identical inputs across runtimes.
Attest. An independent NexArt attestation node signs the certificate hash with an Ed25519 key and returns a signed receipt with a UTC timestamp.
Verify. Anyone recomputes the hash and checks the signature against the node's published key at verify.nexart.io. No account, no dependency on NexArt.

What makes it different

Logs describe what your system says happened. They can be edited, deleted, or fabricated. NexArt binds the run to a cryptographic hash you did not choose.
Audit trails track who accessed and changed data. NexArt proves what the AI actually produced from what it received.
Observability tools describe performance and traces. NexArt produces a portable proof artifact that verifies without access to your infrastructure.

The distinction is proof versus visibility: visibility helps the operator, proof helps everyone who is not the operator. See audit trails vs verifiable execution.

Key properties

Independently verifiable — no NexArt account required to verify.
No raw data stored — confidential mode uses cryptographic commitments; raw input and output never leave your environment.
Cryptographic proof of execution — SHA-256 canonicalization plus Ed25519 signature.
Tamper-evident — any change to protected fields breaks the certificate hash.
Optional identity binding — bind a record to an email, user ID, or organization; the identity is included in the certificate hash.
Protocol-versioned — protocol 1.3.1 defines canonical serialization so verification is deterministic.

Example use cases

Financial services: prove which inputs an AI-assisted lending decision saw, without exposing applicant data.
Regulated workflows (EU AI Act, ISO 42001, NIST AI RMF, SOC 2): produce audit-grade AI execution evidence one integration covers.
Autonomous agents and tool-calling pipelines: attach a signed record to every step; ship the whole session as one Project Bundle.
Client-facing AI outputs: hand a signed, portable record to the client so disputes verify offline.
Internal governance: prove that a decision followed the required inputs, policies, and parameters.