Delivery Playbook.
The path we use to turn governed AI ideas into secure production systems with clear evidence, controls, and handoff.
Discovery & Metrics
We map your operational bottlenecks to clear, testable AI opportunities. We define success criteria and establish how value will be measured at the end of the engagement.
Data Access & Governance
Before writing code, we map data ingestion pipes. We evaluate RBAC models, establish PII handling rules, and formally agree on the infrastructure constraints.
Prototype & Evaluate
Rapid build of the core workflow. We develop evaluation harnesses to test model behavior, evidence quality, and failure modes against your specific tasks.
Control Hardening
The prototype is wrapped in defensive architecture. Policy engines, rate limits, token budgets, and human-in-the-loop approval workflows are added where the workflow needs them.
Deploy to Target
We package the system for the target environment: managed cloud, client VPC, on-prem, or offline/local-first deployment where appropriate.
Operate & Audit
Day-2 operations. We verify trace logging, hand over incident and support runbooks, and monitor performance against the initial metrics.
What we need from you
- Subject matter experts for workflow validation
- Clear direction on RBAC and data boundaries
- A champion internal to the affected team
- Compute environment availability
Typical Timelines
- Pilot Engagement1-2 Weeks
- Production Build4-8 Weeks
- Architecture Review1 Week
Playbook Simulator
Select a phase to simulate our delivery pipeline execution, logs, and verifiable deliverables in real-time.