Reasoning API
Kadai
The Kadai Inference Engine is our reasoning model, a domain-specific foundation model at the heart of our AI control plane.
Kadai is the Propose+Prove API — the bounded synthesis runtime that takes a governed question, runs it through the full execution pipeline, and emits a signed governed response. The Compiler rewrites the request into a CFPO-ordered (Content-Format-Policy-Output) prompt contract through a five-pass deterministic rewrite, matching known model attention behavior instead of appending retrieval chunks as an undifferentiated block. Authority weighting per evidence chunk uses Kura provenance metadata to decide which CFPO zone each piece of evidence belongs in. The model reasons inside the boundary, not around it — generation receives bounded context, not raw corpus access. The Ledger decomposes each response into atomic claims and evaluates them across multiple layers: L1 confidence signals from token logprobs, L2 source entailment via embedding similarity and NLI, L3 stability via multi-draw regeneration and semantic clustering, and L4 hidden-state probes for internal volatility (Refinery and Clean Room only). The differentiator is contrastive causal attribution — measuring whether evidence actually caused a claim, not just whether it appeared nearby. The Gate reads the Ledger evaluation record and assigns one of five output states deterministically: AUTHORIZED, PARTIAL, REQUIRES_SPEC, NARRATIVE_ONLY, or BLOCKED. ARBV (Adversarial Resilience and Boundary Verification) runs formal authorization invariants, adversarial tests for semantic and retrieval pressure, and emits signed Boundary Evidence Records that auditors and partners can independently replay without trusting the operator. Without Kadai, a user prompts a model directly and the model answers from training data, tool calls, context window, and improvisation — with no evidence boundary, no claim checking, no auditable provenance.
Without bounded synthesis: a user prompts a model directly. The model answers from training data, tool calls, its context window, and improvisation. There is no evidence boundary, no claim checking, no inspectable our auditable provenance. The burden of trust falls on the reader.
How Kadai turns bounded evidence into a governed response
Read this left to right from the Kura handoff. Bounded evidence enters, the runtime compiles a controlled prompt contract, the model drafts a proposal, the system verifies the claims, and only then is a governed response returned. Kadai orchestrates this runtime. It does not define evidence on its own, and it does not replace Ledger or Gate.
Who this is for
Application developers
integrate Kadai as the answer API. One API call replaces direct model access and returns governed, classified responses.
The end user
receives a response already evaluated and assigned an explicit state — not raw model text that still has to be defended by hand.
Kadai — the governed inference engine — is the reasoning API. It orchestrates Compiler — the prompt compiler —, bounded retrieval, generation, Ledger — the integrity-protected inference audit trail —, and Gate — the emission policy boundary — before anything reaches the caller. Every claim is checked against governed evidence before it is emitted. Kadai synthesizes — it does not act as authority.
Go deeper
API Developer Guide
Full API reference — POST /v2/chat, request/response schemas, streaming, error handling, attestation verification.
See Kadai in action
Ask a question and see what a governed response looks like.
Kura
The evidence Kadai draws from.
Ledger
The verification engine behind every Kadai response.
Pricing
Kadai usage-based pricing — per request and per token.