No evidence, no emission.
Fluency used to mean something. Now it does not. We built the control plane that makes fluency trustworthy again.
Operational Reality
When fluency breaks, everything looks right until it isn't.
AI systems don't fail loudly. They fail convincingly. The same models that produce useful answers can fabricate case law, hallucinate intelligence, and leak sensitive data—without any visible signal that something is wrong. The problem is not that they fail. It's that you can't tell when they do.
Curated from the AI Incident Database and Kenshiki operational baselines for high-stakes systems.
1,425+
Documented AI failures in the AI Incident Database
36%
Impacting vulnerable populations
Zero
Margin for error in the decisions that matter
Why this doesn't get better with bigger models
More parameters don't fix the problem.
If fluency is no longer a signal of truth, making a model more fluent doesn't restore that signal. It just makes the answers more convincing.
Scaling increases capability, but it does not introduce grounding. A larger model can still fabricate, still omit, still overgeneralize — only with greater confidence and fewer visible cracks.
This is why the problem doesn't go away with better models. It gets harder to detect. The system becomes more useful, but less interrogable.
Certainty does not come from making the model smarter. It comes from constraining what the model is allowed to say based on what can be proven.
Kenshiki does not treat the model as an authority. It treats it as a synthesizer. Every answer is bounded by governed evidence inside your trust boundary, and every claim must be supported before it is allowed to exist.
This is not a filter on top of a model. It replaces the assumption that the model can be trusted.
You don't have to take this on faith.
Prove an AnswerHow it runs
Where Kenshiki fits
However you use AI today, Kenshiki sits around it — making sure every answer holds up before you act on it.
Workshop /01
Refinery /02
Clean Room /03
Who this is for
For people who cannot afford to guess.
When a fluent answer can move money, shift care, expose intelligence, or trigger legal consequences, sounding right is not enough.
Defense & Intelligence
An intelligence brief gets pushed up the chain. Later, someone tries to trace the sourcing — and parts of it don't exist. Kenshiki ties every claim to verifiable sources before the brief ever leaves the system.
Government & Public Sector
A policy decision is justified with analysis that appears well-supported. During oversight, the underlying evidence can't be produced. Kenshiki makes every output traceable, so decisions can be defended when scrutinized.
Healthcare & Life Sciences
A clinical recommendation reads correctly. But when compared against actual studies, key assumptions don't hold. Kenshiki constrains recommendations to evidence that can be verified before they influence care.
Regulated Enterprise
A team uses AI to draft a position or risk assessment. When challenged by audit or regulator, the underlying basis can't be demonstrated. Kenshiki ensures every output can be explained and defended before it reaches operations.
How Kenshiki works
Two APIs. One contract.
Define what's real in Kura. Ask Kadai for answers that can be proven from it.
Kura Index
Kura is the evidence store. You POST source material into Kura, and the system preserves provenance, structure, and retrieval boundaries so every downstream answer can be traced back to something real.
Prompt Sanitizer
Input is the secure entry point where every request enters Kenshiki. It establishes who is asking, what evidence they can access, and binds identity through the entire pipeline via OpenFGA/ReBAC.
Prompt Compiler
Compiler turns a loose prompt into a disciplined query. Before the model sees anything, the system narrows the question to what can actually be answered from evidence instead of letting the model improvise.
Kadai Inference
Kadai is the reasoning API. You query Kadai and get back an answer grounded in the evidence available to the system. Kadai does not act as the authority. It synthesizes across what Kura contains and what the Claim Ledger can support.
Claim Ledger
The Claim Ledger breaks the answer into claims, checks those claims against the evidence, and records what is supported, what is unsupported, and what evidence is missing. Unsupported claims do not get through.
Boundary Gate
The Boundary Gate keeps model priors from slipping past the evidence layer unchecked. It is the final separation between fluent generation and claims the system is actually willing to let reach a user.
Verify it yourself
The systems, failures, and mechanics behind the platform.
What it's built on
System Documentation
The actual surface area of Kenshiki — APIs, operator workflows, and implementation details.
Platform Architecture
The full topology behind Kura, Kadai, and the control plane that enforces grounded answers.
AI Incident Archive
Real failures. Real consequences. The patterns Kenshiki is designed to detect and prevent.