Building AI systems that actually ship
Applied AI engineering is the work of turning an AI idea into a system that runs in production and holds up under real users — not a demo, not a slide deck. I’m Christopher Queen, a Forward Deployed / Applied AI Engineer with 15+ years shipping production software in Python, TypeScript/Node, and AWS, on a foundation that includes real-time and financial systems. This is where I write about how that work actually gets done.
Most “AI” content stops at the prototype. The hard part starts after: retrieval that returns the right context, agents that fail gracefully, evaluations that catch regressions before your users do, and cost and latency that make the feature viable. I work the way a forward-deployed engineer does — understand the problem, build the thing, deploy it, and prove it works.
What you’ll find here
- RAG & Retrieval — building retrieval pipelines that ground LLMs in real data.
- AI Agents & MCP — reliable agents, tool use, and the Model Context Protocol.
- LLM in Production — evaluation, cost and latency, and security.
- AI for E-commerce — applying these techniques to search, personalization, and analytics.
- How We Ship — how Christopher Queen Consulting scopes and delivers AI engagements.
Everything here is grounded in hands-on building. If you’re an engineering leader, founder, or developer trying to ship AI that survives contact with production, this is written for you.