Proprietary AI. Senior leadership. Governance built in.

Everything a full-time CAIO would, at a fraction of the seat cost. A fractional CAIO owns the AI strategy and roadmap. They govern vendor selection and contracts, make architecture decisions, and set acceptable-use and data-handling policy. They support AI-related hiring and report 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 We do not publish a public menu because no two firms' data, risk posture, and workflow are alike.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. One is a defined-scope project: a copilot, a document-analysis pipeline, or a governance framework. The other is a monthly fractional Chief AI Officer retainer that sequences the roadmap over time. Either path starts with the free AI Executive Readiness Assessment. It 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, and research memos. Your team queries it 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. The mix depends on your security posture and workload. Builds draw on frontier models (Anthropic Claude, OpenAI, Google Gemini) via their enterprise APIs. They use open-weight models run in your own environment when data cannot leave it. And they use 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. It is a self-scored diagnostic across six pillars: strategy, governance, data, security, team, and adoption. It produces a 0 to 100 score, a readiness band, and your lowest-scoring pillar. It takes about ten minutes. 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 $4.97 AI-Ready Change Management Playbook covers the sequencing we use: readiness, pilot selection, policy, training, and measurement. It is the first paid rung of the CAIO ladder and comes with a 40-prompt companion pack.

Two retainer tiers. The Signature retainer starts at $4,000 per month and covers AI strategy and quarterly roadmap ownership, vendor governance, architecture decisions, and board-level reporting in business terms. The Estate retainer starts at $8,000 per month and adds active build management, team training, and custom-project execution within the retainer. Custom AI project engagements scope from $15,000 depending on scope, data complexity, and compliance requirements. Every path starts with the free AI Executive Readiness Assessment so the first conversation is grounded in evidence.

Three-layer architecture for regulated firms. First, data-path isolation: PHI is processed under a signed Business Associate Agreement, transmitted through encrypted channels only, and never routed through a non-compliant LLM. This applies to all healthcare clients and aligns with HIPAA 45 CFR Part 164 requirements. Second, model selection: open-weight models (such as Llama 3 or Mistral variants) can be deployed entirely within the firm's own environment when data cannot leave it. Third, access control: role-based permissions and full audit trails on every AI interaction, so there is always a record of what was queried and what was returned.

Phase 1 is Exposed: tools are in use but there is no sanctioned stack, no policy, and leadership has limited visibility into what the team is doing with AI. Phase 2 is Uneven: some structure exists but adoption is inconsistent across the firm. Phase 3 is Governed: documented acceptable-use policy, sanctioned tools, training complete, and a named governance owner. Phase 4 is Native: AI is a core part of operations with metrics, continuous improvement, and board-level accountability. The free AI Executive Readiness Assessment diagnoses which phase your firm is in. The $14.97 AI Governance Snapshot translates that into the three most critical action items.

Most copilot and document-analysis projects ship in 6 to 12 weeks from kickoff. That timeline typically breaks down as: 2 weeks for discovery and architecture (understanding the firm's data, workflow, and security posture), 3 to 5 weeks for build and iteration (model selection, retrieval pipeline, interface, integration testing), and 1 to 2 weeks for compliance review and a monitored soft launch. Larger projects involving multiple pipeline stages, BAA review, or regulatory sign-off take longer. We scope each project individually and put the timeline in writing before work begins.

An AI use policy is a firm-branded document that defines which tools are sanctioned, what data may flow through them, what outputs require human review before use, and what happens when the policy is violated. Without one, firms face three concrete risks: shadow AI (staff using consumer tools like ChatGPT with client-confidential data), documentation gaps during regulatory examinations (bar audits, HIPAA audits, SEC examinations expect evidence of governance), and liability when AI-generated content carries the firm's name but nobody reviewed it. The $497 AI Use Policy Kit delivers a ratifiable draft in 5 to 10 days, written for the firm's specific regulatory posture.

Yes, with the right architecture. Custom document-analysis AI performs first-pass privilege review, extracts key terms, flags inconsistencies across contract versions, and drafts summaries from the firm's own templates. All of this operates as an attorney productivity tool, not a legal advice system. The AI does not communicate with clients, does not give advice, and every output is reviewed by an attorney before use. Systems are scoped against Florida Bar Rules 4-5.3 (responsibilities regarding non-attorney assistants) and relevant ABA Model Rules so the attorney-supervision requirement is embedded in the workflow, not left to chance.

Retrieval-augmented generation (RAG) is the architecture that lets an AI model answer questions using the firm's own documents rather than general training data. When a staff member queries the copilot, the system retrieves the most relevant chunks from a vector store of the firm's precedents, SOPs, intake histories, and memos, then feeds them to the language model as grounded context. The result is a copilot that cites the firm's own protocols with attribution rather than hallucinating from general knowledge. For a 10-partner law firm this means the copilot answers from that firm's matter history. For a 6-physician concierge practice it means the copilot references that practice's own clinical protocols.

No. But most firms that skip it spend the first consultation describing symptoms rather than gaps. The free AI Executive Readiness Assessment scores six pillars in ten minutes: strategy, governance, data readiness, security posture, team capability, and adoption readiness. It produces a 0 to 100 score, a readiness band, and your single lowest-scoring pillar. Most firms discover their biggest gap is not the one they expected. Starting from that data makes the first scoping conversation specific rather than exploratory.