Reality Check
AI coding tools are remarkable. Your team can build impressive demos in days instead of months. But here’s what I’ve learned shipping production software:
- Demos don’t need to handle errors gracefully
- Prototypes don’t need security audits
- MVPs shown to investors don’t need to scale
- Code that “works on my machine” isn’t production-ready
That gap from working demo to production system is where experience matters.
Software Architecture & Technical Leadership
Decades of building software: web apps, mobile (iOS/macOS), desktop software, distributed backend systems, network monitoring tools, API services.
I help you:
- Design architectures that scale beyond the demo phase
- Make informed technology decisions based on your actual constraints
- Structure codebases that handle real users, real data, real edge cases
- Navigate the Apple ecosystem (iOS, macOS, Swift, App Store requirements)
- Build distributed systems that are reliable, not just fast
- Balance moving quickly with building things that last
For startups: Your competitors are also using AI. Your advantage is having someone who knows how to architect properly, handle security, and build for scale not just for demos.
Product Acceleration with AI Coding Agents
I’m not anti-AI. I use these tools daily. But after years of building software, I know what AI can and can’t do:
What AI does well:
- Generate boilerplate quickly
- Implement well-known patterns
- Speed up routine tasks
- Create initial prototypes
What AI can’t do:
- Understand your specific business constraints
- Make architectural trade-off decisions
- Catch subtle relationships
- Know when code is “good enough” vs needs refinement
- Maintain consistent concept, style, and structure beyond the file level
I help teams:
- Integrate AI coding assistants into professional workflows
- Establish quality gates for AI-generated code
- Train engineers to prompt effectively and review critically
- Accelerate development while maintaining production standards
- Build systems that work under load, not just in demos
System Design Review & Scalability Planning
Your demo works great. Now let’s make sure it works at scale.
Architecture reviews:
- Web application architecture (frontend, backend, API design)
- Mobile app architecture (iOS/macOS patterns, data sync, offline support)
- Distributed systems design (microservices, message queues, caching)
- Database design and query optimization
- API design and versioning strategies
Performance & scale:
- Identifying bottlenecks before they become outages
- Capacity planning based on realistic growth projections
- Infrastructure cost optimization
- Monitoring and observability strategies
- Incident response planning
Security & reliability:
- Security reviews (especially critical for AI-generated code)
- Error handling and edge case coverage
- Deployment strategies and CI/CD pipelines
- Testing strategies for distributed systems
Real-world focus: Not academic exercises. Practical recommendations from someone who’s debugged production issues at 2 AM and knows what actually matters.
Mentorship for In-House Engineering Teams
Your engineers are talented. They’re using AI tools effectively. But do they have the experience to know when the AI is leading them astray?
I provide:
- Technical mentorship for senior engineers and team leads
- Code review training (especially for AI-generated code)
- Architectural decision-making frameworks
- Testing strategies that actually catch bugs
- Deployment automation and DevOps practices
- Security awareness for modern web and mobile apps
- Concurrency patterns and performance optimization
- Cross-platform development best practices
Specific expertise:
- Apple ecosystem: Swift, iOS, macOS, App Store requirements
- Backend systems: Go, distributed systems, network programming
- Web development: Modern stacks, API design, frontend/backend integration
- DevOps: CI/CD, containers, cloud infrastructure
Who This Is For
Startups building with AI coding tools who realize “impressive demo” and “production-ready product” are different things
Growing companies discovering their prototype architecture doesn’t scale
Engineering teams who want to move fast with AI without accumulating crushing technical debt
Not Sure Where to Start?
Schedule a discovery call and let’s discuss:
- What you’ve built so far
- Where you’re feeling pain
- Whether AI tools are creating hidden problems
- What “production-ready” means for your specific product
Or email me with a description of your situation. I’ll let you know if I can help.
Bottom line: AI coding tools are powerful accelerators. But production software needs architecture, security, performance optimization, and the judgment that comes from long experience. That’s the difference between a good demo and a successful product.