Data and permissions
Confirm what data can be used, who can access it, and what must not enter the model context.
- Identity mapping
- Sensitive data handling
- Source and version tracking
Delivery method
Design AI systems outward from real operating workflows.
We do not start with a model demo. We start with how work gets done: what people read, what they decide, which systems they use, which actions need approval, and how failure is recovered.
Project stages
Every stage must produce reviewable evidence.
- 01
Diagnose
Interview business, technical, and front-line users to define workflow boundaries, data sources, permissions, exceptions, and success metrics.
- 02
Validate
Build a prototype with representative samples to test value, cost, latency, quality, and human collaboration.
- 03
Integrate
Connect enterprise knowledge, business systems, tools, approvals, logs, and identity permissions into a working workflow.
- 04
Govern
Add eval datasets, regression tests, prompt-injection checks, human review, monitoring metrics, and release gates.
- 05
Enable
Turn the project into internal playbooks, courses, templates, and operating rhythm so the team can keep improving it.
Quality gates
Before launch, know when the system should not be trusted.
Answer quality
Build evaluation samples covering common, edge, and high-risk questions.
- Golden set
- Recall and citation checks
- Human review rubric
Action safety
Define agent actions, approval conditions, recovery behavior, and audit requirements.
- Tool catalog
- Approval policy
- Rollback and alerts
Operating metrics
Track quality, cost, latency, usage, and human intervention after launch.
- Metrics board
- Release record
- Review cadence
Project start
A good AI project kickoff should define workflow, evidence, and launch gates.
If your pilot has a demo but no evals, permissions, or integration plan, start with delivery diagnosis.