YakData
FDE Lab is free · Production Reviews open

Your AI works. Is the system ready for production?

The model is only one component. YakData helps senior engineers turn working AI into production systems teams can trust, operate, recover, and own.

Built around 77 Rules: a real production AI system, not a hypothetical course project.

Does this look familiar?

The prototype is working. The production decision is not.

  • The demo works, but nobody can define production-ready.
  • The model performs well, but failure behavior is barely tested.
  • The screen, behind-the-scenes logic, AI, and product work each move separately.
  • Nobody is certain where human judgment belongs.
  • Release is approaching, but nobody is sure what happens when it fails or who owns it.

What 77 Rules made obvious

The model was not the hard part.

A production AI system has to turn perception into evidence, controlled logic, a reviewable decision, and an operating result.

01Dashboard + contextOperating input
02Image + text AIInterpret what is visible
03Structured evidenceObservation before inference
04Explicit rulesInspectable domain logic
05Fixed scoring rulesThe AI does not invent the score
06Human reviewVerify, correct, override
07Operating outputDecision record you can inspect
77 Rules audit workspace showing findings, evidence, scores, and review controls
This is the finished surface. The FDE Lab follows what had to happen underneath it.
3 levels of evidenceConfidence changes system behavior.

Three evidence levels keep an AI guess from being presented as a business fact.

Fixed scoring logicThe final score stays explicit.

AI supplies evidence. Business-critical scoring follows visible rules that produce the same result from the same inputs.

Ownership proofThe client could extend the system.

Client-specific rules fit the core architecture, and the system was used to change reporting practice after handoff.

The FDE Production Map

Seven decisions. Five responsibility lanes. One production system.

These seven decisions show where a production AI system stands and what has to happen next.

01Decide
02Requirements
03Architecture
04Complete Path
05Evaluate
06Production
07Ownership
PMUXFront EndBack EndTest / QA

The FDE keeps the lanes connected. They overlap from the beginning. This is not a neat PM → UX → backend → frontend → QA sequence.

The success condition is simple.Every consequential production decision has an owner, enough evidence, a clear success standard, and an explicit next action.

See the method applied

Follow 77 Rules through the full production build.

FDE Lab is free. Follow 77 Rules through all seven production decisions, from definition through ownership.

Episodes 1 to 3

Define it correctly.

Decide what deserves to be built. Turn expert judgment into requirements. Design the production system and the work.

Decision · Requirements · Architecture
Episodes 4 to 5

Prove it works.

Build the smallest complete path through the system. Test the AI on real examples, deliberately test how it fails, and make human review explicit.

Vertical Slice · Evaluation
Episodes 6 to 7

Make it production.

Make the system reliable, release it, prove it can recover from failures, transfer ownership, and decide what should happen next.

Production · Ownership

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Apply it to your own work

Now put your system under the same scrutiny.

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 when real users depend on it. You retain ownership of the build and leave with the next action.

$1,500 / quarter
  • Weekly live Production Reviews
  • Bring work from any of the seven production decisions
  • Written Production Review Record for substantive reviewed work
  • Reviewed does not mean passed

Why this standard exists

Built from production work, not certification theory.

Netflix

Built the subscriber lifetime-value framework and worked on business problems spanning Finance, Marketing, and Operations.

Tableau

Director of Product Management. Conceived the native forecasting engine and worked on advanced data-ingest and management flows.

35 years

Across clinical-trial data, enterprise BI, product leadership, analytics, software systems, and production AI.

Stephen looks at what the business needs and works back to the technology. This insight is invaluable for improving ROI and driving quick wins from the analytical investment.
John LodmellCFO, Nordstrom Credit Services

What would fail if your system went into production tomorrow?