AI Development
Applied AI systems built on real infrastructure: local and hosted LLM inference, retrieval, and structured knowledge. Not prompt-only prototypes that fall apart outside a demo.
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I build applied AI systems that go past a single prompt. That means retrieval, memory, and inference infrastructure designed to hold up under actual usage, not just look good in a demo.
How I Build This
Local LLM inference comes into play when cost, latency, or data control rule out a hosted-only API. Ollama-served open-weight models like Qwen2.5 are a common fit here. On the retrieval side, I combine vector similarity search with structured, graph-based knowledge for cases where relationships matter as much as similarity does.
The inference layer, the knowledge/memory layer, and the application layer stay clearly separated so each one can evolve on its own timeline. And production concerns show up on day one rather than after launch: containerized deployment, observability, evaluation against known scenarios before anything runs unsupervised.
Who This Is For
Teams that need AI capability embedded in a real product or workflow, not a proof-of-concept that quietly dies the first time it touches production data.
FAQ
Do you only work with hosted APIs like OpenAI/Anthropic?
No. I also build with locally-hosted open-weight models through Ollama when cost, latency or data-residency requirements call for it.
What makes this different from a prompt-engineering consultancy?
The focus sits on systems architecture: memory, retrieval, orchestration. Prompt wording is a small part of that, not the whole job.