Applied AI Systems

The Challenge

The market is flooded with AI hype, but most initiatives fail to move from a promising demo to a robust production system. The reason is simple: frontier models excel at generating code that is 85% correct, but this creates the “85% Trap.” Teams get stuck in a perpetual “almost-done” loop, applying old development methods to a new class of problems, which leads to brittle, unreliable systems that never truly ship. You need a partner who can help you escape this trap by applying a disciplined, modern engineering approach to the world of AI.

My Approach

I help you build production-ready AI systems by implementing a lightweight version of the AI-Native Operating Model. My approach is the antidote to the hype cycle. We will:

• Define Testable Specs: We’ll begin by turning your goals into a clear, testable contract that can be used to validate the final output, ensuring the AI builds what you actually need.
• Architect for Validation: We’ll design a system with “Frozen Zones” and clear interfaces, allowing us to isolate AI-generated code and prevent it from breaking critical components.
• Implement with Guardrails: We’ll use a spec-driven, test-first workflow to build the system, catching errors and hallucinations early and ensuring the final product is robust and reliable.
• Enable Your Team: I’ll train your team on these modern, AI-native practices, empowering them to build better systems long after our engagement ends.

Key Outcomes

My approach to AI implementation is designed to help you achieve:

• Production-ready AI systems that escape the “85% Trap” and deliver measurable business value.
• A clear, spec-driven development process that ensures predictability and quality.
• Robust system architecture with “Frozen Zones” that protect your critical assets.
• An empowered internal team capable of maintaining and extending AI systems using modern best practices.
• Confidence in your AI strategy and a repeatable framework for future innovation.

Ideal For

• Looking to implement their first production AI systems
• Struggling to move from AI proof-of-concept to production
• Seeking guidance on responsible AI implementation at scale
• Building internal AI capabilities and need expert mentorship