Results from agentic workflows designed and deployed as an engineering leader at an enterprise organization.
Your last AI consultant delivered a strategy deck. Nothing shipped.
I open a terminal on the first call.
The agentic workflows I designed compressed five sprints into a single sprint. Claude Code skills, hooks, and MCP integrations wired into Jira, Confluence, Slack, and the codebase. The team ships with it every day.
16x development acceleration, measured across PI-level initiatives.
You bought Claude licenses. Adoption stalled after the first week.
Training that sticks because it matches real work.
I've taken two engineering teams from scattered Claude experiments to daily, team-wide usage. The adoption system I build is not a workshop that fades. It is interactive guides, internal documentation, and workflow-specific playbooks your team keeps using.
Dozens trained across engineering, QA, product, and leadership. Adoption sustained.
AI adoption depends on one champion. That's a single point of failure.
A self-sustaining practice, not a dependency.
Consultant dependency is a failure mode. I build training materials and adoption standards that make your team self-sufficient. The goal is always that your team owns the outcome independently.
Built AI practice standards, training programs, and adoption tooling at an enterprise organization.
I built this practice around Claude because it is the strongest model for code generation, agentic workflows, and enterprise integration. If a different model is the better choice for a specific job, I will say so.