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.

CPA Ontario's own guidance is plain: accountability rests with the CPA regardless of the technology used. A companion publication goes further — the CPA continues to hold the sole responsibility for the quality of the work performed, no matter which tool produced it.

That means every AI tool a firm adopts, from client-facing chatbots to bookkeeping automation, needs the same vetting a new hire would get: where does the data go, and does the vendor train on it. We verdict the stack you already run and prescribe additions that fit, with your first fix installed live on the review call.

RCDSO expects dentists using AI in their practices to remain responsible and accountable, and to override AI-generated outputs as necessary. Ontario's privacy regulator published its own due-diligence guidance for AI scribes in January 2026 — a checklist for adoption, not a case against it.

AI scribes, front-desk intake tools, and scheduling assistants all touch patient data. We verdict each one on where the data goes and whether the vendor trains on it, and hand you the due-diligence checklist your regulator now expects before you sign up for anything new.

Ontario courts have already sanctioned lawyers over unverified AI citations. In Ko v. Li, 2025 ONSC 2965, fabricated case law in a factum led to a show-cause order. In R v. Chand, 2025 ONCJ 282, mismatched citations got the tool barred from refiled submissions. Neither case banned AI — both turned on the missing verification step.

We verdict the research and drafting tools your firm already runs, and put a verification workflow around every AI-assisted filing, so the check happens before a judge finds the gap.