No evidence. No defense.
Your AI can answer.Can you defend it?
Kenshiki Labs is the runtime control plane and proof layer for consequential AI decisions. It creates a per-decision evidence boundary around every answer, recording what the model saw, what it claimed, what evidence was retrieved, which checks ran, and what happened next.
Capability is here. What's missing is the execution boundary around it. Kenshiki binds AI to approved evidence, constrains how it acts, and produces a replayable record for every consequential decision.
Why governance is broken
Most AI governance shrinks the model. We don't.
Refuse more. Retrieve less. Hedge harder. That's the standard playbook — and it breaks the moment a model has to reason about a decision the enterprise must defend.
The model isn't the blocker. The execution boundary around it is. Regulators, lawyers, audit, and procurement don't ask whether the demo worked. They ask what the system used, what the model claimed, which checks ran, and where the record is.
Grounded in the MIT AI Incident Tracker, the AI Incident Database, SEC AI-washing enforcement, and Kenshiki Labs operational baselines for high-stakes systems.
Where the pressure lands
The same evidentiary burden shows up differently by industry.
Cross-border AI regulation doesn't stop at your sector boundary. Sector obligations don't stop at your AI deployment. Different regulators, different statutes — same demand: which evidence was authorized, and why was the answer released?
How Kenshiki Labs works
Prepare. Propose. Prove.
Every governed AI request runs through three phases. Bind evidence before, reason inside the boundary during, write a replayable record after. The model stays whole — execution is what we govern.
The cost of waiting
The longer you wait, the more expensive reconstruction gets.
The cost isn't the AI you delay. It's the record you cannot produce when someone else sets the timeline: buyer, auditor, customer, regulator, plaintiff.
This quarter
Next quarter
Six months out
Aug 2026
Year 2
This quarter · Procurement
The first deal you lose to a governance gap
A diligence questionnaire asks for runtime evidence of governed AI use. Sales writes 14 pages that boil down to 'we have a dashboard.' The deal slips a quarter.
Next quarter · Internal audit
The first audit finding the board sees
A SOC 2 walkthrough pulls a sample of AI-assisted decisions. There is no per-decision record. Audit issues a finding. The remediation plan goes on the next board pack.
Six months out · Customer counsel
The first renewal paused over a replay request
A customer's general counsel asks you to replay one contested decision and prove the AI stayed inside its authorized evidence. You can't produce the replay. The renewal pauses.
Aug 2026 · EU regulator
The first regulator's deadline lands
EU AI Act high-risk obligations cross from planning to enforcement. Fine math turns real, not hypothetical. The first published action becomes the comparable for every company in your sector.
Year 2 · Plaintiff
The first lawsuit you can't reconstruct
A model-generated decision harms a real person. Reconstruction takes weeks of outside-counsel hours. The verdict is 'we can't tell' — and that's now the public record.
Evidence is cheap when produced at runtime. It is expensive when reconstructed under pressure.
What this is — and isn't
What governance isn't.
These are the assumptions buyers most often arrive with. Each one fails the same test: can you replay the decision?
Not what you already built
Private deployment plus monitoring dashboard plus prompt template is packaging, not proof. If an auditor asks which evidence was authorized for a specific decision, none of those three answers the question.
Not a prompt layer
We don't rewrite your prompt and hope for the best. The system evaluates each claim in the response against governed evidence before it leaves.
Not a monitoring dashboard
Dashboards tell you what happened after the fact. The Boundary Gate decides what happens before the answer reaches anyone.
Not model-dependent
Kadai is the governed reasoning runtime, not a model replacement. It can orchestrate OpenAI-compatible backends, including your own model path — the evidence boundary still applies.
Not optional compliance
The SIRE gate checks obligation evidence before retrieval, not after. If the evidence doesn't exist, the system fails closed. That's the contract.
Control is what creates trust.
Start in Workshop to see a governed response in five minutes. Talk to Kenshiki when private deployment is on the table.