Applied AI, built by someone who has run the operation.

I talked to operators and subject-matter experts before automating inter-DC transfers, to understand the problem and its impact.

I run full customer discoveries before proposing a pilot, to understand what it costs the customer when they can't trust their AI agents' answers.

I defined exactly what the field needed to see in yard reporting (status, contents, and age) before anything was built.

I built templates for defining pilot use cases, the security review, what we need from the customer and from our own team, and internal specs. I use them on every pilot, and so does my team.

I mapped transportation and warehouse system tables in SQL, wrote the EDI feed in Python, and built transfer flows in Power Automate.

I engineer the semantic layer, then connect it to the customer's agents through MCP, whatever agent tools they plan to use.

I ran a two-week user acceptance test with field users and transportation analysts, then revised the reports with decision science until sign-off.

I build the models, then run structured evaluations and benchmark rounds, adding question coverage until the models can be certified at 94–100% accuracy.

I recorded training videos, wrote job aids, and held live training sessions on national DC network calls, then ran bi-weekly office hours after go-live.

I ran training sessions, built a public documentation library, and supported the customer hands-on through their production push.

After testing, I handed ownership of the yard report to the field, so field leaders run it and are accountable for the number themselves.

I turn what I see in customer work into feedback and specs for Product and R&D, covering model-building usability, benchmarking workflows, and observability.