Digital engineering for deterministic AI governance.

SDL works on control placement, evidence production, and governance methods for high-stakes AI systems. The emphasis is on bounded claims, readable artefacts, and verification outside the serving path.

Deterministic control

Work centers on pre-inference control points where policy can be applied explicitly before a model is called.

External verification

Artefacts are intended to support checking outside the system that generated them, including signed records and supporting hashes.

Open technical surfaces

Core implementation and public proof surfaces are exposed through open-source tooling and published run artefacts.

What SDL works on

Current focus

Governance engineering

Pre-inference enforcement design

SDL develops rule-driven control paths that move governance closer to the request boundary rather than leaving it to provider-side probability.

Open-source tools

Visible implementation surfaces

SIR and related proof surfaces are exposed in a way that lets outside reviewers inspect how benchmark and audit evidence is being produced.

Grounded collaboration

Work with deployment and review teams

Collaboration is aimed at teams dealing with governance, assurance, and evidence review in settings where control placement matters.

Why this matters

Deterministic control is easier to inspect

The governance gap

Training-time alignment and post-hoc monitoring do not by themselves create a deterministic boundary in front of inference. SDL's work is about that boundary.

Evidence for assurance review

Deterministic controls and signed artefacts can be relevant to assurance and underwriting conversations because they create a clearer review surface than informal safety language alone.