Building AI products and the teams behind them.

Pat Mullee

From industrial analytics to AI platforms.

  1. GE

    Solving operational problems.

    At GE, I built analytics products for industrial equipment. The work focused on reliability, service decisions, and costs.

  2. FourKites

    Growing the product portfolio.

    I joined FourKites as its first data science product hire. I helped grow one arrival prediction service into an AI and ML portfolio spanning truck, ocean, air, and rail.

  3. Storable

    Leading AI and automation.

    At Storable, I lead AI and automation products. My scope includes what we build, how we price it, and whether customers use it.

How I lead.

  1. Start with the problem.

    I start with the person doing the work. What is slowing them down? What would help them make a better decision? Then I decide whether AI can help.

  2. Make it work in practice.

    I plan for messy data, existing systems, and the people who need to review the output. Those details belong in the design from day one.

  3. Stay accountable after launch.

    After launch, I look at usage, reliability, cost, and revenue. Those results guide what we improve, expand, or stop.

What I want to build next.

I want to lead teams building AI products around a business’s own data and the work its customers do every day. I’m interested in the full job: deciding where to invest, building the team, and growing the business.

Experience covers the products I’ve shipped. The Workshop contains independent prototypes I use to test new ideas.