I teach developers to work with AI systematically. It’s not about writing better prompts. It’s about a methodology that organizes team workflow, and an entire tool ecosystem I provide: from MCP servers, through agent orchestration, to production workflows.
Running Claude Code isn’t enough to do serious AI-powered development. Real projects require a systematic approach: code review, security, documentation, and multi-tool coordination. I deliver a battle-tested working environment. The BMad methodology, configured tools, and standards that get the team moving from day one.
During the training, developers work on real projects and see how AI accelerates their daily work. They don’t wait weeks for results.
AI doesn’t work on autopilot. I teach teams how to maintain full control over code, how to do code review with AI, and how to avoid common pitfalls.
I don’t train individuals, I transform the way the entire team works. Shared standards, shared tools, shared workflow.
I deliver a complete working environment: BMad methodology, configured MCP servers, agent orchestration tools, and standards that get the team going immediately.
The training is delivered progressively. We start with the basics and add tools and techniques with each stage, building up to the target workflow.
We explore Claude Code, Codex, and Gemini. Basic commands, environment setup, first tasks delegated to AI agents. The team sees what daily AI-powered work looks like from the very first hour.
CLAUDE.md as the project constitution. The agent knows the rules, standards, and architecture. Todo files for tracking progress. Project context that makes AI work smarter with each task.
A structured workflow with specialized AI agents: analyst, architect, developer, PM. From requirements analysis, through planning and implementation, to documentation. Each stage has its own agent and clear rules.
Multiple agents work simultaneously on separate branches without interfering with each other. You work on one task while the agent handles another in the background. True parallelism, not queuing.
Playwright for browser testing, Context7 for up-to-date library docs, database and API servers. We learn to install, configure, and build custom MCP servers tailored to the team’s needs.
How to verify AI-generated code. Hooks, guardrails, quality standards. Prompt injection, hallucinations, common pitfalls. Full control over what gets into the repository.
Coordinating multiple AI tools in one workflow. Automating repetitive tasks, CI/CD integration, working with multiple agents simultaneously. From a single agent to a production pipeline.
We apply everything above to your team’s code. Real projects, real challenges, real results. Developers finish the training with a working workflow, not notes.
After the training, I don’t leave the team on their own. I monitor progress, answer questions, help optimize the workflow, and react to new tools and opportunities.
Let’s talk about how your developers can start working effectively with AI.
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