AI-Native Strategy
AI-Native Engineering Strategy
Consulting that treats AI coding adoption as a redesign of the engineering operating system, not an individual-productivity question. Workflow, review, testing, education, measurement, and governance are redesigned as one bundle.
Designed on top of multi-organization experience deploying Claude, Copilot, and other agentic tools, with code review, testing, certification, and CI/CD all wired in. The unit of change is the organization's delivery, not a tool demo.
Outcomes
- Operating playbook covering AI-native development, review, and testing standards
- Performance measurement system defined and operated (review lead time, PR rework rate, defect escape rate, AI-graded pass rate, and more)
- Governance policy across model and tool selection, security, license, and data
- Engineer skill diagnosis and a phased in-house training and certification roadmap
Scope
- Diagnose the engineering organization, codebase, and workflows (recurring patterns, bottlenecks, review and testing habits)
- Design AI-native standards: CLAUDE.md hierarchy, team commands and Hooks and MCP governance, code review criteria
- Design and run pilot programs (target KPIs, enablement, measurement, retrospective)
- Wire in-house training, certification, and onboarding into a company-wide adoption roadmap
Best fit
- Engineering leaders who want to standardize AI tool usage and redesign how development itself happens
- Platform and R&D organizations that need to scale AI adoption without compromising review, testing, or release quality
- Enterprises that need AI tool governance under security, license, and compliance constraints
- Executives who require measurable adoption results, not satisfaction scores
Frequently Asked Questions
How is this different from typical AI consulting?
Most AI consulting stops at tool selection and demos. We integrate real features like CLAUDE.md, Skills, Hooks, and MCP into your organization's review, testing, and CI/CD process, and we design the performance measurement system alongside.
What does the strategy work cover?
Workflow diagnosis, AI-native standards (code review, testing, release, education, certification), performance measurement system, governance (model, security, license, data), and a company-wide adoption roadmap.
How does this connect to the training programs?
The standards and KPIs defined in the strategy become the content baseline for in-house training and certification. When useful, the Claude Code · GitHub Copilot training and AI-graded certification run alongside, so strategy and practical capability move in one direction.
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