Services

Most AI pilots never reach production, and the cause is usually organizational, not technical. My engagements are staged so each step de-risks the next: you see measurable value before you commit further, and you're never paying for a strategy deck that sits on a shelf.

A staged approach

Start small, prove value, and scale what works. Each stage stands on its own and feeds directly into the next.

1

AI Readiness Assessment

2–4 weeks

An honest audit of your data, infrastructure, skills, and governance, delivered as a gap analysis and a use-case roadmap prioritized by impact and ease. You'll know exactly where AI can move the needle and what's standing in the way.

2

Strategy & Roadmap

Sequenced for action

A pragmatic plan that sequences one or two pilots with clear build/buy/partner decisions and success metrics defined up front, so "success" is a number, not a feeling.

3

Pilot / Proof of Concept

4–12 weeks

Prove ROI on one high-value workflow against a measured baseline, with a clear production-readiness gate. Disciplined scoping is the value-add: a pilot should answer a business question, not demo a technology.

4

Production Implementation

From pilot to P&L

Deploy the proven workflow to production with monitoring, system integration, and human-in-the-loop controls, engineered for the compliance realities of financial services and healthcare.

5

Ongoing Advisory / Fractional AI Leadership

Retainer-based

Continued governance, new use-case identification, vendor evaluation, and capability transfer to your team: senior AI leadership without the full-time hire.

Standalone services

Not ready for a full engagement? Each of these is available on its own: technical builds where engineering depth is decisive, and organizational work where adoption is won or lost.

Technical

Data Infrastructure Readiness & Pipelines

The #1 reason pilots fail is poor or siloed data. I diagnose and fix the data foundation most consultants only talk about.

RAG Systems & Document Intelligence

Retrieval-grounded copilots that cite their sources, the dominant pattern for trustworthy AI in document-heavy, regulated businesses.

AI Agents & Workflow Automation

Agentic workflows scoped with discipline, automating the busywork while keeping humans on the decisions that carry regulatory weight.

Custom Copilots & Chatbots

Purpose-built assistants for your teams' actual workflows, integrated with the systems they already use.

CRM / ERP / Core-System Integration

AI that writes back to the systems of record, because a workflow isn't automated until the output lands where work actually happens.

Model Selection & Evaluation

Independent, vendor-neutral assessment of which models and platforms fit your use case, budget, and risk profile, with measurable evaluation criteria.

Organizational

Use-Case Identification & Prioritization

A structured scan of your operations to find the workflows where AI pays back fastest, scored on impact versus ease.

Executive Workshops & Team Upskilling

Hands-on sessions that take leadership and staff from AI-curious to AI-capable, grounded in your industry and your data.

Change Management & Adoption

70% of AI value is people and process. I design rollouts that frontline teams actually embrace, with adoption metrics tracked from day one.

AI Governance, Risk & Compliance

Policies and controls fit for regulated industries (HIPAA, model-risk guidance, emerging rules like the EU AI Act), turned into an enabler, not a blocker.

Vendor Selection & Procurement Support

Independent help cutting through "agent washing" and vendor hype to choose tools that will still make sense in two years.

Not sure where to start?

A short conversation is usually enough to identify whether there's a workflow worth piloting. No pitch, no obligation.

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