Your team has the tools.
Now build the system that makes them work.
Custom AI capability sprints for product and engineering teams. Not training. Your team ships real features with structured governance from day one.
Tools are not capability.
Every team has access to the same AI tools. Most adoption is uneven: a few early adopters experiment, the rest wait.
Giving everyone licenses doesn't build capability. Unstructured adoption creates inconsistent output, no governance, and tools that get abandoned after the initial excitement fades.
The competitive moat is not the AI tools themselves. It's the harness around them: the context, constraints, governance, and feedback loops that make AI-assisted development reliable and repeatable in your specific environment. That harness is earned through structured practice, not installed in a day.
How It Works
The Sprint SeriesFoundation
- Async video content covers principles, no calendar disruption
- Technical setup verified, champions identified and prepared
- Every participant arrives ready to build on day one
Sprint Sessions
- Four half-day working sessions over 2 weeks
- Cross-functional pods progress from greenfield prototypes to production-ready features on real backlog items
- Governance architecture established: deterministic rules, observer model patterns, human escalation workflows
Embedding
- 30 days of champion-led standups and async expert review
- Day 14 pulse check, Day 30 full assessment
- Habits become operating rhythm, not a one-time event
The Harness Engineering Framework
A structured system for measuring and improving how your team builds with AI tools. Five dimensions, scored 1-5, tracked quarterly.
Does the team provide AI tools with the right project context, constraints, and domain knowledge?
Do systems mechanically prevent bad output through branch protection, validation, and governance rules?
Does the system self-correct over time through hooks, drift detection, and monitoring?
Do quality feedback loops compound through testing, review checklists, and performance tracking?
Is the codebase structured for AI tools to succeed with clear file organization, composable modules, and explicit failure paths?
Each dimension is scored across three maturity levels (Individual, Team-Ready, Systematic). The score gives leadership a concrete, trackable metric to report quarterly and improve over time.
Measurable Outcomes
AI Readiness Score
Baseline before engagement, re-scored at completion and Day 30. A number leadership can track.
Working Software
Pods ship real features through a full development workflow. Not exercises.
Reusable Infrastructure
Architectural guardrails that persist after the engagement and scale to future teams.
Internal Champions
Early adopters trained to sustain momentum after the engagement ends.
Governance Architecture
Deterministic rules, observer model patterns, and the feedback loop that makes the system improve over time.
Who This Is For
- →Product and engineering teams adopting AI development tools
- →Organizations where adoption is uneven (some power users, most haven't started)
- →Teams that need structured governance alongside speed
- →Leaders who want a trackable metric for AI readiness, not just anecdotes
20 years building products and leading teams. Head of Product at Seen by Indeed (120-person incubator). Director of Product at Bazaarvoice. Built the ML matching foundation at Indeed contributing to $500M+ in attributable revenue. Multiple 0-to-1 products launched and scaled.
Now works with product and engineering teams to build reliable AI-assisted development through the harness engineering framework. Has coached 200+ product managers through the shift to AI-native work.
Let's talk about your team.
Every engagement starts with a discovery call. No pitch, no pressure. Just a conversation about where your team is today and what's possible.