AI-Native Engineering
I help engineering organizations adopt AI the way it actually ships value: not as a chatbot on the side, but as a core engineering tool woven into how teams design, build, test, and deploy software. This is grounded in real experience rolling out Claude Code to an entire engineering organization across Software Engineering, QA, and DevSecOps. The outcome is smaller, higher-leverage teams delivering more with the same headcount.
Claude Code Adoption & Rollout
Phased enterprise rollout modeled on a successful deployment across a full engineering organization. Covers Phase 1 Software Engineers establishing CLAUDE.md standards, repo conventions, and productivity baselines, Phase 2 QA Engineers with test case generation and Playwright and SQL Server MCP automation, and Phase 3 DevSecOps with infrastructure-as-code assistance and CI/CD optimization. Includes introduction strategies that land with senior engineers.
Agentic Development Enablement
Going past autocomplete to teams that ship full features with AI. Services include CLAUDE.md authoring and templates tuned to your stack and architecture, custom agents and slash commands for repeated workflows like reviews, releases, commit discipline, and spec drafting, Claude Skills packages for your engineering patterns, and MCP server design and implementation with integrations for Azure DevOps, SQL Server, Playwright, and your internal APIs.
Spec-Driven Engineering
Operating at Level 4 on the 5 Levels of AI Coding Adoption. Covers specification workflows where engineers author implementation-ready specs and the agent executes, agent-friendly codebase conventions with clear architectural patterns and generous CLAUDE.md context, review practices that preserve craftsmanship while capturing the velocity gain, and a practical model for assessing where each team sits on Levels 1 through 5.
Team Topology & Scaling
The real lift from AI is organizational, not just per-developer. Focus areas include restructuring larger teams into smaller pods that deliver more with the same headcount, shifting the ratio of architects, senior engineers, and AI-augmented ICs, re-scoping roadmaps to what is actually achievable at the new velocity, and change management for engineers who were skeptical at first and became the strongest adopters.
The Human Side of AI Adoption
The hardest part of AI adoption is not the tooling, it is the people. Work covers honest conversations about what changes for engineers and what the new expectation is for a senior IC, rebuilding professional identity around architecture, review, and spec authorship, trust calibration on when to accept, push back on, or throw out agent output, addressing the quiet skeptics, managing the fear cycle, and career conversations for engineers asking what their next five years look like.
AI Governance, Security & Metrics
Enterprise readiness, honestly discussed. Covers data handling and .claudeignore strategy with acceptable-use policies for regulated code, audit and session logging approaches that satisfy security and compliance partners, an honest read on what Claude Code measures today (seats, sessions, lines) and how to build a credible productivity story, and a mix of qualitative and quantitative metrics including adoption rate, developer satisfaction, cycle time, and code review quality.