Intake & framing
- Goal + constraints + data stack mapping
- Use-case risk tiering and approvals
- Success signals defined up front
Applied AI Lab
From intake to production, same pod.
Clear stages, criteria, and signals. We ship something usable every sprint, with governance and QA from day one.
Pilot & MVP
- POC with acceptance criteria
- MVP with QA, evals, and guardrails
- User training and prompt playbooks
Production rollout
- Telemetry dashboards and alerts
- Regression, red team, and drift checks
- Change management and communication
Continuous improvement
- New experiments on a safe runway
- Quality, adoption, and safety signals to steer
- Same pod stays on to iterate
Sprint rhythm
Ship every sprint—POC, MVP, production.
Each sprint ends with something demoable, eval’d, and governed. No “we’ll get back to you in six weeks.”
Sprint 0
Frame
Decide what to prove
- Use-case + risk tiering
- Success signals + eval rubrics
- Approval path aligned
Sprint 1–2
POC/MVP
Build & eval
- RAG/agentic patterns implemented
- Red/green tests + HITL reviews
- Telemetry wired for adoption
Sprint 3+
Production
Launch & iterate
- Approvals, audit logs, rollback paths
- Drift monitoring + red team checks
- New experiments queued
Guarded delivery
Governance is built in, not bolted on.
Approvals, observability, and rollbacks are part of the workflow, whether it’s QA, personalization, drafting, or agentic tooling.
Approvals
Audit logs
Rollback ready
Approvals
HITL by design
Owner + approver captured, with steps gated for high-risk flows.
Observability
Every step visible
Telemetry, traces, and evals exposed for QA, compliance, and product.
Recovery
Rollback paths
Versioned prompts/configs with safe rollbacks and incident playbooks.
Ready to run
Bring one use case. We’ll ship a governed pilot.
QA, personalization, drafting, or agentic tooling—pick one. We’ll propose the fastest, safest path to evidence.