AI advisory & consulting
Where AI actually earns its place in your business: the strategy, the first production use cases, the guardrails and governance, and the build-versus-buy calls — from someone who has moved generative AI from pilot to production inside a hyperscaler and built it into products since.
This is for you if
- The board is asking for an AI strategy and the answer so far is a list of pilots
- A copilot or chatbot pilot worked in the demo and stalled on the way to production
- You need guardrails, data handling and governance before customers or auditors ask
- Vendors are pitching platforms and you want an independent view of what to build, buy or wait on
What the engagement includes
AI strategy tied to outcomes
A short list of use cases ranked by value and feasibility, the data and platform prerequisites for each, and a roadmap the business can fund one step at a time.
Pilot to production
The unglamorous work that turns a promising demo into a running system: evaluation, guardrails, cost control, monitoring, and the operating rhythm around it.
Governance and guardrails
Responsible-AI policy, data handling and privacy, model and vendor risk, and the review path that keeps regulated customers and auditors comfortable.
Build, buy or wait
Independent model and vendor selection, honest cost-of-ownership, and the build lab on hand when a proof of concept is the fastest way to know.
How it usually runs
Typically two to four days a month, agreed up front, with a standing weekly slot and availability between. Engagements run in three-month terms so both sides can reassess honestly.
What it is not
Not a prompt-engineering workshop or a slide deck about the future. This is executive-level counsel with working software behind it when it is needed.
Bring the brief, or the empty seat.
Two paragraphs is enough. You’ll get an honest read on fit, timeline, and cost within two business days.
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