My Approach
How I Build Production AI Systems
I don't build demos. I build AI systems that ship to production, handle real traffic, and deliver measurable business value. Here's my approach — from problem framing to deployment.
1 Core Principles
Production-first thinking
Every architecture decision starts with: "How does this behave at scale, under failure, with real data?" Demos are easy. Reliable systems are hard.
Problem framing over tool selection
I spend more time understanding the problem than picking frameworks. The right problem framing often eliminates half the complexity.
Observability from day one
You can't fix what you can't see. Every AI system I build has logging, tracing, and evaluation baked in — not bolted on later.
Trade-off reasoning
Latency vs accuracy. Cost vs capability. Autonomy vs control. I make these trade-offs explicit and document them, so teams can revisit decisions as requirements evolve.
2 My Process
Discovery & Problem Framing
Understand the business problem, not just the technical ask. Map data flows, identify failure modes, and define what "success" looks like in measurable terms. Often the most important phase.
LLM Evaluation & Model Selection
Not every problem needs GPT-4. I run structured evaluations across models (OpenAI, Claude, Gemini, open-source) for the specific use case — testing accuracy, latency, cost, and edge case behavior before committing.
Architecture Design
Design the system: RAG pipelines, agent workflows, tool orchestration, fallback strategies. I use the simplest architecture that solves the problem — complexity is added only when proven necessary.
Build with Guardrails
Implement with hallucination detection, input validation, output formatting, and structured error handling. AI systems fail differently than traditional software — the guardrails must account for probabilistic behavior.
Observability & Evaluation
Deploy with comprehensive logging, tracing, and evaluation pipelines. Monitor token usage, latency, accuracy, and user satisfaction. Set up alerts for drift and degradation.
Ship & Iterate
Deploy to production with confidence. Use real-world data to continuously improve prompts, retrieval quality, and agent behavior. The system gets better over time, not just at launch.
3 My AI Stack
LLM Providers
Agent & Orchestration
RAG & Vector Stores
Infrastructure & Observability
Ready to build something that actually ships?
I help teams move from AI experiments to production systems. Let's talk about your project.