I spent 25 years at Cummins learning how big companies get complex work done. I started as an applications engineer and finished directing global technology strategy for the electronics business: a $190M annual engineering budget, research programs on three continents, and the frameworks that decided what we built and when. I hold four patents from those years. If I got good at one thing, it was making important work run on a system instead of on heroics.
None of it came with me. No planning staff. No analyst pulling the numbers together before a decision. No program office keeping twenty commitments from quietly becoming forty. I had spent a career inside some of the best operating machinery in industry, and I walked out the door with a laptop and a calendar. I knew exactly what good operations looked like. I just couldn't be all of it at once.
For the next two years I ran a consulting practice: more than twenty engagements on technology roadmaps, AI integration, and organizational transformation, for clients from Fortune 500 manufacturers to venture-backed startups. Different companies, same lesson every time. The expensive problems almost never traced back to talent. They traced back to work built on assumptions nobody had checked against a source, and to mistakes that slipped through because catching them depended on busy people. The fix was rarely heroics. It was rigor upstream, and systems that catch what people miss.
Those years were also where I first put AI to real work. Not generating anything — checking. We used it to read requirements documents for faults in logic and discontinuities, the things that just seemed off, and every flag became a target for real investigation. The checks held up, and defects got designed out instead of caught late. One venture-backed startup had technology their industry genuinely needed and no viable path to the customer commitment that would scale it. What closed that gap wasn't better science. It was operating discipline a serious buyer could trust.
What separates companies that run well from companies that run on adrenaline isn't knowledge. It's having someone whose whole job is keeping the system running.
So I built the staff. Excelsius Compounder runs the same methods I spent my career on — objectives tied to real work, decisions framed with evidence, a record of what was decided and whether it proved right — operated by a team of AI agents working on your business, in your own workspace. And it's built the way I learned to trust AI in the first place: every piece of work passes a check before it reaches you, and nothing moves without your decision. It clears the chaos. You scale your business.
One more thing, because it matters more than any credential: I run Excelsius on it. The Strategic Review I read every Saturday, the decision log, the calibration record that tracks whether the system's recommendations turned out to be right — the same mechanisms you get. I am customer zero. This company operates on the product it sells.
If you're where I was — certain you could run something real, and buried by everything that running it requires — this was built from inside that exact spot.