Cassandra
Cassandra leads AI adoption, training and change management, helping your people actually use what we build. She's completing the same Purdue MS in AI Management and Policy.
Radman AI is a small, senior practice. The person you talk to is the person who does the work, backed by people who know how to make it stick.
I lead every engagement myself. The lesson I keep relearning is simple: AI pays off only when it's built into the work people already do.
I've spent 15 years building and running software businesses, and the last several putting AI to work inside them.
I started out as a partner in a web design and development agency in San Diego. In 2014 I founded a B2B e-commerce and operations software company, grew it, and sold it, then stayed on to lead it for five more years under its new owner. Along the way I built a venture that used natural-language processing and predictive modeling to read company press releases and forecast market moves. That's where applied machine learning hooked me.
My focus now is applied AI: taking it from experiment to something your people use every day. I lead PRFlow, an open-source harness that makes AI coding agents dependable on real codebases, in daily use by dozens of developers. I'm also completing an MS in AI Management and Policy at Purdue, because the hard part of AI adoption is rarely the model. It's the people, the process and the governance around it.
Cassandra leads AI adoption, training and change management, helping your people actually use what we build. She's completing the same Purdue MS in AI Management and Policy.
A vetted bench of senior developers who work with us on every build.
We're based in Utah and work with companies across the US. Leadership interviews, readouts and kickoffs go better in the room, so we show up for those. The rest happens remotely, on your schedule.
Your processes, data and systems are the starting point, not an obstacle. AI that ignores them doesn't last.
Every idea gets sized and ranked against the others. The most valuable work goes first, not the loudest.
People approve what matters. Outputs carry their evidence, and nothing runs unattended where it shouldn't.
We measure how the work happens before we change it, so the improvement is a fact, not a feeling.
The people doing the work shape the tool from the first week. That's how AI gets used instead of tolerated.
Thirty minutes with Daniel. No pitch deck: you talk about your business, I tell you honestly where AI fits and where it doesn't.