Run the models on your terms.

What this feels like from the inside

A demo goes well. Then the system meets a real customer, a real record, a real regulation. Nobody can draw the line between “the machine composed this” and “we stand behind this.” Agents multiply. The vendor becomes the memory of the firm.

We sit with the people who have to live with that. Not to add another tool. To decide what the institution will still own after this year’s model is gone.

How we help

• Decide where the model runs: someone else’s cloud, the edge, or the building. Our own filings on secure local and edge serving mean we know what each choice costs and what it protects.
• Make a clean split: the model can write and look. It should not be the vault.
• Say where an “agent” is useful, and where it is an expensive way to guess.
• Share AI across institutions, employees, and customers without sharing what must stay private.
• Give the board a way to ask “how do we know?” that survives a bad week.

Where this comes from

Three granted patents and five published applications in Kevin’s name are enterprise language systems that were built and run inside JPMorgan Chase: automated classification of natural language data, sentiment analysis, automated support services, content lifecycle management. Two 2025 filings cover secure local LLM serving and privacy-preserving AI across institutions. That operating experience is what the advice is made of. The record is on the Scholar page.

What you leave with

A clearer sentence about the decision. If we go further: a map of how AI is actually used in your organization, where it should run, and a short plan for what to keep in-house. Our public writing on agents and on cloud versus edge is on the research page.