Learn to enter an unfamiliar organization, understand its operation, build the right intervention, deploy it under real constraints, and remain accountable for adoption and measurable impact.
Start with the complete deployment lifecycle, build technical depth, specialize in production AI, or practice through standalone deployment missions.
Understand the complete deployment lifecycle from discovery and workflow mapping through pilot design, adoption, and value measurement.
Build a working operational system across data, APIs, interface, permissions, observability, rollout, and measurable impact.
Move RAG, agent, and model-driven workflows from prototype to governed operations with evaluation, human review, and production controls.
Practice against messy evidence, conflicting stakeholders, hidden complications, and constraints across seven industries.
The work spans technical implementation, field judgment, organizational change, and evidence of value. The curriculum evaluates all four.
Identify the recurring decision, the operational bottleneck, and the cost of delay or error.
Build a narrow but deployable workflow across data, logic, interface, access, and support.
Make explicit tradeoffs around legacy systems, security, imperfect data, and deadlines.
Treat trust, training, ownership, incentives, and exceptions as system requirements.
Instrument whether the solution changed behavior and improved the intended outcome.
Move cleanly between operator detail, architecture reasoning, and executive decisions.
Every program uses the same operating sequence so artifacts, decisions, and evidence connect across the entire deployment record.
Understand the decision, workflow, stakeholders, baseline, evidence, and constraints.
Define the minimum deployable workflow, architecture, controls, and acceptance criteria.
Create the complete vertical slice with data, logic, interface, access, and observability.
Release through a bounded pilot with monitoring, support, rollback, and ownership.
Change the real workflow through training, trust, feedback, and accountable operations.
Compare outcomes with the baseline and recommend scale, revision, or shutdown.
Each mission introduces incomplete information, conflicting incentives, and a complication after the first design decision.
Reconcile unstable vessel, port, and cost data into a delay decision system before the decision window closes.
Unify sensor, material, maintenance, and quality evidence while avoiding unsupported claims of causation.
Deploy a governed knowledge assistant where outdated documents, unsafe shortcuts, and weak connectivity create real operational risk.
Work is evaluated on whether it could survive inside a real organization and improve a real operational decision.
| Dimension | What strong work demonstrates | Weight |
|---|---|---|
| Problem framing | The solution addresses a specific decision and documents assumptions, constraints, and the baseline. | 15% |
| Technical design | Architecture is proportionate, secure, maintainable, and complete enough for the workflow. | 20% |
| Implementation | The system works with imperfect inputs and includes observability and failure handling. | 20% |
| Field judgment | Tradeoffs, stakeholder conflict, and newly discovered evidence are handled explicitly. | 15% |
| Adoption | Pilot, training, ownership, exception handling, and feedback are designed into the release. | 15% |
| Value proof | Results are compared with the baseline without overstating causality. | 15% |
Submit a complete deployment, defend your decisions in a live review, and earn a publicly verifiable independent credential if your work meets the published standard.
FDE Foundations teaches discovery, workflow mapping, solution design, pilot planning, adoption, and value measurement through a complete portfolio project.