Inference produces an answer. Verification produces evidence.

InferenceVerification names a real and expanding technical problem: determining whether an AI system executed the intended model, configuration, computation, reasoning constraints, or policy—and whether the resulting output can be trusted. Verification can occur during inference, after inference, or through independent computational evidence.

Capture Inference

Capture model identity, inputs, configuration, execution metadata, computational traces, tool interactions, outputs, hashes, and other evidence required for verification.

Check the Result

Recompute claims, evaluate constraints, inspect outputs, replay operations, compare expected behavior, and determine whether inference satisfies defined verification criteria.

Establish Integrity

Use reproducible computation, trusted execution, cryptographic commitments, formal verifiers, proofs, signed traces, or independent checks to establish confidence in execution.

Create Evidence

Generate portable verification records connecting the model, inference request, execution environment, result, verifier, policy, timestamp, and integrity evidence.