Review, design, and govern AI.
One practitioner across all three. Each mode is presented as a clear line of work: what we focus on, what you leave with, and the matching engagement.
An independent read before you commit.
A candid second opinion on one AI initiative, architecture, or platform already in flight, with no commercial interest in the answer.
What we focus on
- The intended outcome, decision, and affected users
- Data, access, accountability, and controls
- Security, integration, validation, and the existing design
- The evidence required before build or production
What you leave withA decision memo, prioritized findings, the conditions to meet, and a recommended next step, not a maturity score.
View the matching review engagementThe architecture underneath the decision.
Reference patterns your engineers can build, your auditors can read, and your users can trust, designed for real enterprise constraints.
What we design
- Target-state architecture, reference models, and key decisions
- RAG retrieval, indexing, embeddings, evaluation, and guardrails
- Data residency, access, personal information, and integration
- Portability, lifecycle, cost, and delivery sequencing
What you leave withA reference architecture, documented decisions, and a sequenced roadmap that survives procurement, security, and budget.
View the AI Architecture DiagnosticThe operating model that makes it repeatable.
A policy states the rules. An operating model makes them survive intake, procurement, release, and monitoring.
What we establish
- Inventory, intake, classification, and decision rights
- Controls, ownership, testing, and evidence requirements
- Applicable requirements: Quebec, Law 25, ISO 42001, NIST AI RMF, and other relevant frameworks
- Portfolio management, steering rhythm, and vendor orchestration
What you leave withAn evidence-backed position, a prioritized control and gap register, a remediation roadmap, and an executive briefing.
View Governance & Compliance Readiness