Not probably. Not on average. Formally. Decidably. Provably — or not at all. That is the question AlethiaGraph answers, and it is the question no other system in the market can answer today.
The FFIEC examiner does not care that your AI system is correct 94 percent of the time. The examiner cares whether this finding, in this examination, against this institution, is supported by this regulatory requirement. That question requires a proof engine, not a better prior.
The same is true for the General Counsel defending an AI-assisted contract decision. For the CTO attesting to the board that the AI deployment is governed. For the audit committee that needs to sign off on AI-driven findings. The question is always the same: can you prove it? Not simulate it. Prove it.
Existing AI governance approaches share a common structural failure: they operate after execution, on outputs, without formal guarantees about the system's epistemic state at the moment of decision. A system that produces a plausible output from an insufficient knowledge base is not governed. It is lucky.
AlethiaGraph is built on a formal mathematical framework — the Actionability Function A(R,Q) — that provides three guarantees unavailable in any existing AI governance approach.
AlethiaGraph does not replace the LLM. It governs it. The LLM produces the best possible completion given its domain knowledge. AlethiaGraph evaluates whether that completion is formally supportable by the authoritative knowledge base. One improves the prior. The other verifies the instance.
Every Decision Record is written to the Chandra Protocol chain — an append-only, cryptographically sealed audit substrate. The audit trail is not a log. It is the authorization mechanism. Every AI agent decision is formally attested, source-attributed, and unforgeable.
The framework has been formally specified and validated against the FFIEC bank examination domain — one of the most demanding regulated AI use cases in financial services. A formally governed examination system whose epistemic state is mathematically characterized, formally attested, and unforgeable in the Chandra audit chain.
The examination record produced by this system is not just auditable. It is provably complete within the bounds of the knowledge graph's current density — bounds that are stated, signed, and improving.
The technical framework is available for review by qualified organizations. Additional regulated domains — clinical, defense, FedRAMP — follow the same architecture with domain-specific knowledge graph construction.
AlethiaGraph is in final architecture and development. We are engaging with a small number of qualified organizations for early access and technical briefings. Framework documentation available upon request.
Artwork: "Eidyia" by Emily Balivet · emilybalivet.com