The AI produces useful answers, but nobody has defined what evidence is good enough to trust or release it.
FDE Production Reviews
Put your AI system under production review.
A working demo is not the same thing as a production-ready system.
Bring one real piece of your system and one consequential decision. We challenge the assumptions, evidence, how the system can fail, and what that means in production before a weak decision becomes expensive.
Enrollment is open. $3,500 per quarter · 14-day fit guarantee.
What production review catches
Most production failures are not obvious in the demo.
The interface, behind-the-scenes logic, AI model, and workflow each look reasonable. The complete process still fails under real conditions.
The system is live, but what happens when it fails, how it is monitored, where people step in, and who owns it are still unclear.
The production question is not only, “Does this component work?” It is, “Which consequential system decision is still unsupported by evidence?”
How a review works
One piece of real work. One decision. One next action.
The review is designed to improve what you do next, not produce a pile of commentary.
- 01Bring the real workSystem design, requirements, workflow, AI evaluation, test evidence, release plan, handoff material, or another focused piece of the system.
- 02Name the decisionMake the question specific enough to change the build. “Review my project” is not a production decision.
- 03Challenge the systemFollow the dependency wherever it leads across product, user experience, the interface, behind-the-scenes logic, AI, testing, release, and ownership.
- 04Leave with an actionBuild, test, redesign, narrow, fix, release, defer, transfer, or stop.
Seven Production Decisions
Enter wherever your system is now.
These are review entry points, not a waterfall. A review can move backward or forward when the evidence exposes a dependency elsewhere.
Who this is for
Use external scrutiny where the decision is expensive to get wrong.
The strongest fit is a senior builder working on a real AI system and an approving manager who wants better decisions, not generic coursework.
Challenge system design decisions about how several parts of the system work together.
Best when the pieces work but the whole system still carries uncertainty around readiness, failure recovery, or ownership.
Manager value: less rework and stronger whole-system engineering judgment.Challenge evaluation and release decisions around a working AI capability.
Best when model quality looks promising but confidence, failure behavior, human control, speed, or cost still determine production risk.
Manager value: independent scrutiny of an AI investment already underway.Challenge the path from customer problem through release and ownership.
Best when delivery crosses product, engineering, AI, customer workflow, and ownership handoff.
Manager value: more repeatable technical delivery across customer-facing engineers.For approving managers
Fund one quarter of applied production scrutiny.
This is not generic AI training. The engineer brings current work, applies the FDE Lab production method to it, and receives written records of substantive reviews and next actions.
Quarterly access
Put one quarter of production decisions under review.
Bring your current build. Use the same production standard you see in the FDE Lab. Leave substantive reviews with a written decision record and an explicit next action.
Production Review Record
Reviewed does not mean passed.
The record captures the production question, evidence, finding, decision, and next action. It documents scrutiny, not certification theater.
Enrollment open
$3,500 / quarter
- Two live review sessions each week
- At least three substantive reviews of your real work
- Written Production Review Records
- 14-day fit guarantee
30 active membershipsEnrollment pauses at capacity across the two weekly sessions.
Fair review priorityMembers with less recent review time receive priority when a session is oversubscribed.
Non-confidential work onlyDo not submit credentials, regulated data, customer records, proprietary source code, private forecasts, or trade secrets.
Not ready to enroll?
Ask one question.
You do not need to apply or explain the whole project. Tell me what you need to know before deciding.
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