Cohort 1 — Limited to 10 seats
6-week live cohort for the leaders responsible for AI that can't afford to fail. 10 seats. Behavioral evals. A board-ready governance framework.
Leaders responsible for AI deserve a system that proves it works. Not a hope. Not a demo. A system.
FROM
The leader who championed AI and is nervously hoping it doesn't blow up
TO
The leader who built the operating model that made AI trustworthy
FROM
The bottleneck who slows everything down with manual reviews
TO
The architect who designed the system that scales trust
FROM
The person who can't answer "how do we know this works?"
TO
The person who defined exactly what "works" means — and built the system to prove it
You walk into the board meeting and someone asks "How do we know our AI is working?" — and you have the answer. Not a hopeful answer. Not a demo. A system that measures, governs, and proves it. Continuously.
I spent over 20 years as an enterprise architect in the kinds of organizations where getting AI wrong isn't a UX problem. Healthcare. Financial services. The places where a bad output is a compliance incident, a patient safety event, or a regulator asking questions you're not prepared to answer.
I've been the leader in the room when AI initiatives that looked perfect in the demo fell apart in production. I've done the postmortems. The pattern never changes: nobody defined what "good" looked like before they shipped. Not because the teams were reckless. Because nobody had ever built a systematic way to do it.
That's what this course exists to change.
The framework in this course wasn't built from research papers. It was assembled from pattern recognition across production failures — the same preventable failure, at scale, in regulated industries, by teams that had no playbook for AI that can be confidently wrong.
Step 1
In Week 1, you'll build your AI Ambition Statement — the behavioral specification that tells your team AND your AI what success actually means. No more vibes.
Step 2
Over Weeks 2–5, you'll construct the evaluation pipeline — Golden Datasets, automated scoring, governance gates, CI/CD integration. Hands-on. In your codebase. For your use case.
Step 3
In Week 6, you walk out with a completed Eval Strategy Charter — a governance document your board can read, your regulators can audit, and your team can operate against. Not a certificate. A system.
Each week is 3–4 hours of live instruction, a hands-on code lab, and one deliverable you keep. By Week 6 you have a complete Eval Strategy Charter — the governance document that answers every question your board will ask.
Why deterministic software intuitions are actively dangerous in agentic systems. The shift from "did it run?" to "did it behave?" You discover exactly how exposed your current systems are — and name the villain.
Deliverable → AI Ambition Statement
Building a Behavioral Scorecard before writing a prompt. The Day One Eval Worksheet. How to construct a Golden Dataset from real production logs — not hypotheticals.
Deliverable → Behavioral Scorecard + Golden Dataset (50 examples)
Evaluating multi-step agents, RAG systems, and tool-calling trajectories. The Testing Pyramid. LLM-as-a-Judge — calibration, pitfalls, and when to trust it. The machine checks the machine.
Deliverable → Agentic Reference Architecture diagram
Wiring evals into CI/CD. Online evals: sampling 1-2% of live traffic. From manual review to automated intelligence. Circuit Breaker Protocol — what stops the system before it harms.
Deliverable → Continuous Intelligence Pipeline spec
Structuring the Strategy Realization Office. EU AI Act compliance by design. AI FinOps: managing your Intelligence Budget. Compliance isn't optional — build it in or retrofit it at 10x the cost.
Deliverable → Governance Guardrails Charter
Scaling eval culture across teams. The SRO as organizational design. The Evaluation Paradox — cognitive conditions for high-quality human review. Transformation complete: you are the architect now.
Deliverable → Eval Strategy Charter (board-ready)
Start Here — Free
Chapter 1 of the book — "You're Shipping AI Blind" — plus a 5-email course on why proving your AI works is different from testing it.
The Book
The complete 100-page practitioner guide. Hero's Journey structure. Every framework, template, and checklist — no course required.
The 6-Week Cohort
The full 6-week live cohort. You build the complete Eval Strategy Charter. 10 seats per cohort. Week 1 money-back guarantee.
→ Founders pricing may still be available — book a call and ask.
Limited to 10 seats per cohort
If you can't complete the Day One Eval Worksheet after Week 1 — or if the framework doesn't apply to your specific AI context — tell us within 7 days of the first session. Full refund. No questions. No hoops.
We're confident enough in the framework to put money on it.
Chapter 1 of the book is free. It covers the problem you're already living — shipping AI without any way to prove it works — and gives you the framework preview and a worksheet you can use this week.
You'll get the $25M case study, the argument for why "testing" and "proving" are different things, and a preview of the Day One Eval Worksheet that forces the question your team hasn't answered yet.
This course was designed for the space between product and engineering — not for pure developers. If you can read a CI/CD pipeline diagram and write a user story, you're technical enough. The code labs are optional but designed for engineers who want to go deeper. PMs and architects get everything they need without writing a line.
That's exactly why you need this now. The teams who are ahead of this problem built the framework before they deployed, not after. You have a six-month window before your organization ships something it can't govern. Use it.
Unit tests verify deterministic behavior: does the function return the right value? Eval engineering measures probabilistic behavior: does the AI act the way it was specified to act, across thousands of real-world inputs, continuously? Testing is a snapshot. Evals are a signal. You need both.
One agentic AI incident costs orders of magnitude more. The $25M case study in this course is real. At $1,999 (or $999 for founders), you're buying the operating model that prevents it. If you're already running AI in production, you're already exposed. The question is whether you're prepared.
The commitment is 3–4 hours per week. One live session plus one code lab and one deliverable. Everything is recorded. If you can't find 3 hours a week to build the governance framework for your most strategically important technology category, that's a prioritization problem worth examining.
Both — but the frame is leadership. We cover the code because leaders need to understand what they're governing. But the deliverables are designed for VP presentations, board decks, and architecture reviews. The Eval Strategy Charter is a document you take to a business case, not a GitHub repo.
This is not a course for people who want to learn Python. It's for the three roles that determine whether your organization's AI investments succeed or fail — and who are currently operating without a shared framework.
Enterprise Architect
You're designing the systems. You need a governance model that doesn't collapse under regulatory scrutiny or production load.
Product Owner
You're defining requirements for AI products with no prior playbook. You need to write specs that the model can actually be held to.
Engineering VP
You're accountable for delivery. You need a CI/CD framework that catches model failure before it becomes an incident report.
This is NOT for you if:
Every lesson, every template, every worksheet in this course was produced by a seven-agent AI team — a Product Manager, Writer, Editor, Marketer, SEO Agent, Sales Agent, and Operations Agent — all coordinated through Claude.
The entire build process is documented on YouTube. You can watch the evals fail in real time. You can watch the iterations. The course isn't just about the operating model for AI trust — it's a live demonstration of it.
Watch the Build Series →Cohort 1 — 10 seats total
Every week you ship AI without proving it works is a week you're accumulating invisible risk. Cohort 1 is limited to 10 people. When those seats are gone, the founders rate goes with them.