What I Actually Build — AI Engineering, RAG Systems, Agents & Full-Stack

What I Actually Build — AI Engineering, RAG Systems, Agents & Full-Stack

20 years in software. Started in 2005 with banking systems. Survived humanitarian tech under fire. Built eCommerce platforms that moved real money. Now I build AI that works in production — not demos, not Twitter threads.

Here's what I do.

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AI Engineering (Not Hype Engineering)

I design and ship production AI systems. RAG pipelines that ground LLMs in actual data. Tool-using agents that make decisions, not just chatbots with API access. Workflow automation that replaces manual processes without replacing the humans running them.

The difference between a demo agent and a production agent is architectural, not technical. Prompt engineering gets you to the proof of concept. Guardrails, fallback chains, observability, and permission boundaries get you to production.

I build the second kind.

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RAG Systems That Don't Suck

Retrieval-Augmented Generation sounds simple on a whiteboard. Chunk documents, embed them, retrieve relevant chunks, feed to LLM. Done, right?

No. The hard parts are chunking strategy, metadata enrichment, hybrid search (vector + keyword), reranking, context window management, and knowing when to say "I don't know" instead of hallucinating a confident answer.

I've built RAG for banking, humanitarian operations, and content systems. Each domain has different failure modes. Each solution needs different grounding strategies.

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Full-Stack Development (Laravel + Next.js)

AI doesn't replace the stack. It sits on top of it. I build backends in Laravel, frontends in Next.js, and the glue layer that connects AI capabilities to real user workflows.

Mobile apps with React Native. REST and GraphQL APIs. Database design that scales. The boring stuff that makes AI features actually usable.

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Agent Architecture

Not chatbots. Not copilots. Agents that decompose tasks, call tools, make decisions, and recover from failures autonomously.

The key insight: agents need protocols, not prompts. You define what they can do, what they can't do, and what happens when things go wrong. The prompt is the easy part. The system around it is the hard part.

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What Makes This Different

20 years means I've seen the hype cycles. I've watched frameworks come and go. I know which patterns survive contact with production users and which ones fall apart at scale.

AI is the biggest shift since the internet. But it's still engineering. Still needs testing, monitoring, versioning, rollback strategies. Still needs someone who's been through a production incident at 3 AM and knows what to build so you're never in that position.

That's what I bring.

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Building something that needs to actually work? Let's talk.