Verifiable AI systems backed by continuous control probing.

Our work is structured across three tiers, each with defined problems and measurable outcomes. We deliver production-grade systems, conduct targeted research, and ensure operational resilience.

Engineering

Research

Operations

Systems built and operated in production. Problem: Undefined system behavior. Outcome: Source-bound assertions.

Applied investigation into unsolved problems. Problem: Unverified claims. Outcome: Bounded uncertainty.

Running what has been built with stated service levels. Problem: Unmeasured controls. Outcome: Continuous control probing.

Verifiable engineering method

Risk classification

Claim verification

Continuous assurance

Components are assigned criticality by consequence of failure. This determines review, testing, and deployment rigour. Only 15% of a system warrants the majority of assurance effort.

Generated assertions are decomposed, bound to evidence, and gated before release. Every claim traces to a source record with a stable identifier.

Controls are probed on a schedule, not validated once. This ensures real-time compliance without human intervention.

Operational bounds, not aspirations

p95 < 82ms

latency, measured across 340 artefacts

340+

evaluated artefacts per month

99.99%

uptime against stated service level

EU & US

jurisdiction, processing remains in region

How do we formally bound the set of states a large language model cannot verify, given a specific context window? This is difficult because the model's internal representations are opaque, making direct introspection challenging for novel inputs.

What is the minimal set of control probes required to assert continuous compliance for a system with N interacting components, where N is large? The challenge lies in avoiding combinatorial explosion while maintaining high assurance.