Proprietary AI. Senior leadership. Governance built in.

Everything a full-time CAIO would, at a fraction of the seat cost: owns the AI strategy and roadmap, governs vendor selection and contracts, makes architecture decisions, sets acceptable-use and data-handling policy, supports AI-related hiring, and reports progress in business terms leadership can act on. For most professional firms — a 10-partner law firm, a 6-physician concierge practice — the AI agenda needs senior ownership a few days a month, not a $300K+ full-time executive.

Three triggers. First, when your obligations outgrow vendor terms of service — client-confidential legal documents or PHI shouldn't flow through consumer AI tools. Second, when the value is in your proprietary knowledge: a copilot trained on your firm's precedents, protocols, or playbooks compounds in a way a generic subscription never will. Third, when workflow fit matters — off-the-shelf tools automate generic tasks, custom AI automates yours. If none of the three apply yet, we'll tell you to keep the subscriptions.

Governance is designed in, not bolted on. For healthcare: PHI is processed under a signed BAA, transmitted via encrypted channels, and never routed through non-compliant LLMs. For law firms: systems respect confidentiality and UPL guardrails, with advertising-adjacent outputs reviewed against Florida Bar Rules 4-7.11 through 4-7.22 and ABA Model Rules where applicable. For wealth managers: SEC marketing-rule and books-and-records constraints shape what the AI may generate and what gets logged. Every system ships with audit trails.

Custom engagements are scoped individually — we do not publish a public menu because no two firms' data, risk posture, and workflow are alike. Most work begins one of two ways: a defined-scope project (a copilot, a document-analysis pipeline, a governance framework) or a monthly fractional Chief AI Officer retainer that sequences the roadmap over time. Either path starts with the free AI Executive Readiness Assessment, which shows where your firm stands across six pillars before anyone writes a proposal. See pricing.

A private AI assistant trained on your firm's own knowledge base — precedents, SOPs, intake histories, research memos — that your team queries in plain language. A law-firm copilot drafts first-pass documents from your templates and flags conflicts; a medical-practice copilot answers protocol questions from your own clinical standards; a wealth-management copilot summarizes client files within your compliance perimeter. Your data stays in your environment, and the copilot's knowledge compounds as the firm's does.

We're vendor-neutral by design — that's half the point of the governance work. Depending on the security posture and workload, builds draw on frontier models (Anthropic Claude, OpenAI, Google Gemini) via their enterprise APIs, open-weight models run in your own environment when data cannot leave it, and retrieval infrastructure (vector stores, document pipelines) sized to the firm. Model choice is an architecture decision we justify in writing, not a default.

Take the free AI Executive Readiness Assessment — a self-scored diagnostic across six pillars (strategy, governance, data, security, team, and adoption) that produces a 0–100 score, a readiness band, and your lowest-scoring pillar. It takes about ten minutes, and you can attach the results directly to a strategy-session request so the first conversation starts from evidence instead of generalities.

Yes — enablement is a standard governance deliverable. That includes leadership briefings, role-specific training on approved tools, acceptable-use policy rollout, and change management. If you're planning a rollout, the free AI-Ready Change Management Playbook covers the sequencing we use: readiness, pilot selection, policy, training, and measurement.