AI Augmented Development
Speed matters more than ever. I combine AI powered development tools with senior engineering judgment to build production ready features in a fraction of the traditional time, without sacrificing quality or accumulating technical debt.
How It Works
AI handles boilerplate, common patterns, and repetitive code. I handle architecture decisions, edge cases, performance optimization, security considerations, and integration strategy. This division of labor results in dramatically faster delivery while maintaining the code quality you'd expect from a senior engineer.
The Process
Architecture Design: I design the system architecture, data models, and API contracts
Rapid Scaffolding: AI generates initial components, routes, database schemas, and test templates
Expert Refinement: I review, refactor, and enhance AI output with patterns learned from 15 years of production systems
Edge Case Handling: I add error handling, loading states, validation, and security measures
Integration & Testing: I ensure everything works together seamlessly and passes comprehensive testing
Documentation: AI helps document, I ensure accuracy and completeness
Real Results
Feature development: 3 to 5x faster than traditional development
Code review time: Reduced by 50% (AI helps catch basic issues)
Iteration speed: Same day updates instead of multi day sprints
Code quality: Maintained or improved (due to consistent patterns)
What Makes This Different
Most developers using AI create fast but brittle code. I provide the architectural thinking that makes AI accelerated development sustainable long term. You get speed AND quality.
Ideal For
Companies needing rapid MVP development (weeks instead of months)
Teams with large feature backlogs wanting to accelerate velocity
Startups racing to market with limited engineering resources
Projects with tight deadlines requiring compressed timelines
Organizations wanting to adopt AI augmented workflows themselves
Knowledge Transfer
I can train your team on effective AI augmented development practices, including prompt engineering for code, when to trust AI vs. when to code manually, and how to review AI generated code effectively.
Technologies
AI Tools: Claude Code, GitHub Copilot, Gemini, Cursor AI, GPT-4 for code generation
Stack: React/Next.js, TypeScript, Node.js, PostgreSQL
Testing: Automated testing strategies that work well with AI generated code
Velocity Paradigm
Traditional engineering methods usually take several weeks or even months to reach MVP. Through my workflows assisted by AI and 15+ years of experience with advanced visual building tools, I leverage acceleration methods that condense these usual timelines significantly without sacrificing architectural integrity or security.