The evidentiary machine for governed AI.
Blaze is building deterministic evidence infrastructure for governed AI evaluation. The current public lane covers independent observation and evaluator-defined workload intake while policy agreement, workload acceptance, run authorization, execution, and external effect remain closed.
AI capability is expanding faster than execution authority is being governed.
Policies and logs explain what should happen or what already happened. Blaze focuses on the missing layer between them: whether a live action path is reachable at all, what evidence justified it, and what remained blocked.
Observe approved evidence
Normalize selected signals into a bounded evidence envelope without importing credentials, secrets, or uncontrolled customer data.
Classify and explain
Produce readable outcome states, reason codes, and deterministic receipts instead of opaque model instructions.
Keep touch closed
Submission, activation, effectiveness, observation, and later execution boundaries remain explicit, separately addressable, and unexercised until authorized.
Look first. Prove restraint. Then earn authority.
Blaze is designed to keep sensitive capabilities unreachable by default and record the evidence state at each boundary instead of relying on a model promise.
Read the product thesisReceipts are not decoration. They are the product surface for restraint.
Within the public proof lane, governed boundaries are represented by canonical data, stable digests, explicit state, and a no-shortcut safety posture suitable for replay and independent inspection.
Canonical identities
Stable JSON, SHA-256 anchors, descriptors, and boundary digests bind what was evaluated.
Deterministic replay
Isolated runs are compared for identical outcomes, receipts, and state without environment drift.
Copy isolation
Mutation probes prove one in-memory evaluation cannot contaminate the second.
Independent evaluation
A sealed, zero-dependency evaluator package lets external reviewers inspect the evidence model outside the vendor environment.
You say it’s solid. Let’s find out.
Blaze independently examines consequential technical claims against a frozen scope, adversarial cases, and preserved evidence. We do not begin with the conclusion. We define what must be proved, exercise the boundary, and report only what the evidence supports.
Freeze the claim
Bind the exact object, version, environment, authority surface, acceptance conditions, and limits of conclusion before examination begins.
Exercise the boundary
Run agreed normal, negative, replay, revocation, rotation, continuity, and failure cases without quietly redesigning the system under test.
Let evidence decide
Supported, not supported, inconclusive, or outside scope. Findings stay attached to the exact evidence and object examined.
Start with evidence, not authority.
Blaze shadow pilots are designed to evaluate sanitized scenarios offline. They can classify evidence into allow, deny, hold, or escalate states while leaving production traffic, customer writes, dispatch, and live authority disconnected.
A control layer for teams evaluating AI near sensitive systems.
AI governance and security
Translate policy into explicit runtime boundaries, evidence states, and reviewable receipts.
Enterprise architecture
Evaluate integration paths before credentials, live traffic, or production write handles are introduced.
Independent examination
Qualify authority, identity, continuity, replay, and evidence claims against frozen requirements and bounded adversarial cases.
Built as infrastructure, incorporated for the road ahead.
Blaze Balance Engine Corp. was federally incorporated in Canada on July 17, 2026. The company is developing proof-first governance infrastructure for AI systems operating near sensitive workflows.
Bring the claim. Bring the boundary.
Tell us what system or integration you want examined, what claim matters, and what evidence can be made available. For shadow pilots, tell us the action that must remain blocked. Blaze will scope the boundary before any live access is considered.