The honest version of what most AI projects look like: Someone runs a ChatGPT demo in a meeting. Everyone gets excited. Six months later, nothing's in production, the proof-of-concept is rotting in a GitHub repo, and the data team is back to doing things manually.

The gap between "this works in a notebook" and "this is running reliably in your stack" is where most AI initiatives quietly die.

What I actually do: I come in, audit what you've got — data quality, existing pipelines, where the actual bottlenecks are — and build the thing that ships. Not the demo. The production system.

That's meant LLM redaction pipelines with compliance requirements, Foundry-based forecasting tools cutting inventory overspend by millions, data monitoring systems preventing bad decisions before they happen. Across healthcare, food supply chains, retail, and sports.

The through-line: Every engagement I've done has been about turning messy, untrustworthy data into something a business can actually act on. Fast.