AI Systems
& Data Infrastructure

Most organizations sit on large amounts of messy data.

Contracts, registries, marketplace listings, and internal documents contain valuable information that often lives in PDFs, spreadsheets, and manual workflows maintained by someone, somewhere in the organization.

Often there is just enough structure to suggest automation should be easy – until you actually try.

I build systems that turn this kind of data into structured intelligence.

That usually involves automated data pipelines, AI-assisted extraction, and applications that allow people to explore information that was previously scattered, inconsistent, or simply too time-consuming to analyze.

The models themselves are evolving quickly and are increasingly capable. Much of the interesting work today comes from experimenting with these capabilities and understanding where they actually add value.

But turning those capabilities into reliable systems requires more than the model alone. It requires pipelines, validation layers, and infrastructure that allow AI to behave predictably once it leaves the demo environment.

I spend a fair amount of time exploring hybrid AI architectures – combining local models, cloud inference, and structured data pipelines to see what actually works once real-world constraints like cost, latency, and data sensitivity enter the picture.