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 skip security audits, and 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
I've spent decades building software across web apps, mobile (iOS/macOS), desktop software, distributed backend systems, network monitoring tools, and API services. That breadth means I can help you design architectures that actually scale, make technology decisions based on your real constraints, and structure codebases that hold up against real users and real edge cases.
Whether you need someone who understands the Apple ecosystem deeply, can build distributed systems that are reliable and not just fast, or can help you balance speed with durability, I've been there and shipped it.
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. AI is excellent at generating boilerplate, implementing well-known patterns, and creating initial prototypes quickly. But it can't understand your specific business constraints, make architectural trade-off decisions, or maintain consistent design beyond the file level. It doesn't know when code is "good enough" versus when it needs refinement.
I help teams integrate AI coding assistants into professional workflows with proper quality gates, train engineers to prompt effectively and review critically, and accelerate development without sacrificing production standards.
System Design Review & Scalability Planning
Your demo works great. Now let's make sure it works at scale.
I review architectures across the full stack: web frontends and backends, mobile apps with data sync and offline support, distributed systems with microservices and message queues, database design, and API versioning strategies. On the operational side, I help identify bottlenecks before they become outages, plan capacity around realistic growth, optimize infrastructure costs, and build monitoring and incident response into the foundation.
Security gets special attention, especially for AI-generated code. I review error handling and edge case coverage, deployment pipelines, and 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, and security awareness for modern web and mobile apps.
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 we can talk through what you've built so far, where you're feeling pain, and 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.