Ship What Works
Hands-on delivery. Speed over perfection, but never recklessness.
What Happens in This Phase
- Feasibility assessments validating technical viability before committing engineering resources or budget
- Two-week sprint cadence with working demos at each checkpoint — stakeholders see progress, not presentations
- MLOps pipeline setup: model evaluation, deployment automation, monitoring, and rollback procedures
- Weekly accountability checkpoints with measurable progress against the roadmap milestones set in Step 02
- Rapid iteration based on real user feedback from production environments, not theoretical requirements documents
What Gets Delivered
Working Systems in Production
AI capabilities deployed to real users, not proofs of concept gathering dust on a staging server. Measurable results from day one.
Deployment Strategy
Clear deployment pipelines with rollback plans, monitoring dashboards, and alerting. No “ship and pray” launches.
Team Confidence
Engineering and product teams gain hands-on experience through paired implementation, not passive observation of external consultants.
Sprint-Cycle Results
Measurable outcomes within the first sprint cycle. If the approach is not working, it surfaces fast enough to course-correct.
AI Feasibility Assessment Template
A structured framework for evaluating whether an AI initiative is technically viable, organizationally ready, and worth the investment.
Ready to Ship?
Strategy without execution is a slide deck. Bring a challenge and get hands-on help turning it into a working system.