“For every dollar spent on software, six are spent on services.”
We build, invest in and acquire AI businesses that do the work.
How we work
We identify work businesses already pay for, where jobs repeat, customers are alike and AI can deliver the finished result.
We start from money already being spent on the work, not from what the technology can do. Repeat jobs and similar customers mean every lesson from one job carries over to the next.
We build, fund or acquire the company to deliver that work, and rebuild its operations around AI from day one.
The route depends on what already exists: a new company where nobody does the work well, backing for a team already doing it, or a services business whose delivery we can rebuild. Whichever way it starts, the company sells finished work, priced by the result.
AI executes the routine. Specialists own the judgement calls.
Each job is split into the steps a system can run and the decisions that need a person. Specialists handle the hard cases, and every judgement is captured and built back in, so the same mistake is never paid for twice.
Every judgement becomes shared intelligence. What works in one company moves across the lab. Each new company starts ahead of the last.
The judgements, playbooks and systems built in one company are open to every other, so a fix proven once is used everywhere. Anyone can rent the same models. The lessons from real work belong to the lab.
Every job lowers the cost of the next. We expand where that learning transfers: first to new customers for the same work, then to new work for existing customers.
Because we price by the result, not by the hour, the gain stays in the business and pays for each step outward.
The larger market is not software. It is the work.
Our companies capture the six dollars spent on services for every dollar spent on software. AI executes the routine. Specialists own the judgement.
We create AI businesses and take them from first idea to paying customers. Each one starts on work that repeats, so every job teaches the next.
We back AI businesses that deliver outcomes, not software, and equip them with the lab's methods and systems. They start with what the lab has already learned.
We acquire AI and services businesses and rebuild how they deliver around AI. The customers and the work are already there. We make each job cheaper to deliver than the last.
Adding AI to the old way of working makes today's jobs cheaper once. Then the cost goes flat. In our companies, the human time behind the same job keeps falling, job after job and customer after customer. We look for that before we build, back or acquire a company, and we track it after.
This is the problem we exist to solve. Every judgement a specialist makes is captured with its reason and built back in, so the same mistake is never paid for twice and the next job needs less of a person's time. We price by the result, not the hour, so the gain stays in the business and compounds.