1. Auditability for the EU AI Act
High-risk AI systems must produce verifiable execution records. Logs and screenshots do not survive contact with a regulator.
Every time your policy layer overrides a model — or agrees with it — SONATE mints a cryptographically signed Trust Receipt. The decision is provable to any auditor, regulator, or court, without trusting a vendor dashboard.
AI systems already make consequential decisions — approving loans, drafting clinical notes, generating legal analysis. Frontier LLMs are unreliable judges of their own output. Enterprises have no defensible record of what was asked, what was returned, which policy ran, and whether the model and the policy agreed.
SONATE fixes that. One signed artifact, generated at the point of interaction, verifiable by anyone with a public key.
A Trust Receipt is verifiable evidence — not a log.
The receipt captures what was asked, what was returned, which policy decided, and whether the model and the policy disagreed — in a form anyone can verify independently.
What the model was asked
What it returned
Which policy decided (version + hash)
Which rules fired
Whether the model and the policy disagreed
Who authorized it and when
High-risk AI systems must produce verifiable execution records. Logs and screenshots do not survive contact with a regulator.
When AI causes harm, vendor dashboards are not evidence. A signed receipt that anyone can verify is.
When governance overrides a model — or fails to — someone has to be able to prove what was decided, by which policy, on which input.
Drift, bias, and manipulation rarely trigger alerts on their own. They become defensible findings when the receipt records the disagreement.
Everything else is a reference implementation around that primitive.
The receipt records what your policy layer decided and why. The default policy evaluator ships with SONATE so you can run end-to-end on day one — but it's designed to be replaced. Bring your own deterministic policy, and the receipt records its version, hash, and rule firings instead of ours.
A reference deterministic policy layer that ships with SONATE so you can run end-to-end on day one. Designed to be swapped out.
An example of how receipt-backed evidence can drive gated actions: observe, plan, gate authority, execute, record.
Behavioural and semantic signals that flow into the policy layer as evidence inputs — never as the verdict.
These reference implementations are useful, but they are not the product claim. The product claim is the receipt and the protocol behind it. Customers are expected to adopt the receipt as-is, and replace any of the reference layers above with their own.
We sent a manipulation prompt — wrapped in legitimate-sounding behavioural-science language — to two frontier LLM judges and to SONATE's deterministic policy layer. The judges and the policy disagreed. The signed receipt records the disagreement.
“The email allows for user override and does not manipulate the decision-making process… Overall, the email aligns well with ethical norms.”
Verbatim from the model judges. Both missed the manipulation framing.
Unified gateway captures the AI request and the model's response.
Your policy layer (or SONATE's default) produces a versioned, hashed result. The model's own judgment is recorded as advisory evidence, not as the verdict.
Ed25519 signature plus a hash-link to the prior receipt.
Immutable receipt stored as signed JSON, with support for DID / VC-style envelopes.
Anyone can verify independently using the open SDK. No vendor trust required.
AI is already making decisions that carry legal, financial, and ethical consequences. Operators need evidence before the claims process starts, not after.
TLS for networks. Code signing for software. Digital signatures for transactions. Nothing comparable existed for AI execution. SONATE is the primitive that fills that gap.
We're onboarding design partners now. Start with the open SDK or apply for full platform access.
Stephen Aitken, Founder & CEO. Twenty years in regulated fintech operations. Built SONATE solo using AI-assisted development — 200K+ lines of code in under twelve months. No traditional engineering background. The product is the proof of concept.
Raising pre-seed to onboard design partners and hire the team.