Three layers. One audit ledger.

The governance layer operates between enterprise applications and the underlying artificial intelligence systems they rely on. It does not modify model weights, alter training data, or require integration into model providers' infrastructure.

Instead, it imposes deterministic policy controls at the inference substrate — the moment between a system attempting to produce an output and that output reaching its consumer. Restricted content is suppressed by mechanism, not by request. Compliance is enforced architecturally, not behaviourally.

Designed for

• Institutions deploying third-party foundation models without supplier modification rights
• Environments subject to data-residency, classification, or sovereignty constraints
• Organisations requiring guarantees that exceed model providers' contractual representations

The trust measurement layer addresses a question that procurement, audit, and risk committees increasingly ask of AI systems: how confident should we be in any particular output, and how is that confidence determined?

Synnytra's measurement layer decomposes outputs into atomic claims and evaluates each independently. The result is a bounded, mathematically auditable score reflecting the trustworthiness of the output as a whole — produced without inspecting model internals and reproducible by independent third parties.

Designed for

• Regulated entities required to demonstrate model output reliability to supervisors
• Insurance carriers underwriting AI-related liability with quantifiable exposure
• Procurement bodies requiring evidence-based confidence assessments at acquisition

Both governance and trust measurement layers write to a single cryptographically anchored ledger. Every suppression decision, every confidence calculation, every policy invocation produces a tamper-evident record.

The ledger is hash-chained, locally verifiable, and deployable in air-gapped environments. It produces evidence suitable for regulatory submission, internal audit, and adversarial review — without dependency on external infrastructure or third-party verification services.

Designed for

• Federal and defence environments requiring data sovereignty by structural enforcement
• Financial institutions producing audit evidence under prudential supervision
• Pharmaceutical and life sciences contexts subject to regulatory submission integrity standards

Every layer is engineered to operate within the deployment constraints regulated institutions actually face — not the constraints consumer AI products are typically built for.

• Air-gap deployable. No external service dependencies required at runtime.
• Model-agnostic. Operates regardless of which foundation model the institution uses.
• Audit-ready. Outputs evidence in formats suitable for regulatory and judicial review.
• Sovereignty-aware. No data leaves the deployment perimeter without explicit policy permission.