The sectors we work in.

AI deployments in aerospace and engineering environments intersect with safety-critical systems, export control classifications, and qualification documentation requirements. A governance failure here isn't an audit finding — it's a safety event.

We design architectures that keep AI out of safety-critical decision paths, enforce data classification boundaries, and produce the documentation trail that DO-178C, AS9100, or ITAR compliance review requires.

Manufacturing operations are integrating AI into quality, scheduling, and supply chain functions — often faster than governance frameworks can follow. The result is undocumented decisions and unaudited data flows.

We establish the governance layer before tools go into production: data classification, access controls, decision logs, and traceability from AI output to source data.

Health sciences faces the strictest constraints of any sector we work in. Patient data sovereignty, clinical validity requirements, and regulatory frameworks like HIPAA, MDR, and GxP compliance mean that 'move fast' is not an option.

We design private-cloud or on-premise deployments that never expose patient data to external models, with governance frameworks that satisfy clinical and regulatory review.

Government and public sector AI deployments operate under public accountability requirements that have no private-sector equivalent. Every decision made with AI assistance must be explainable, auditable, and defensible in a public inquiry.

We build governance frameworks that treat explainability and audit trail as first-class requirements — not features added after deployment.