Many of the things that kill AI projects are classic agency problems. Private Equity was famously invented to fix exactly that.
In normal companies, managers run on stories — plausible explanations that are cheap to tell and easy to believe. AI runs on models — statistical recommendations that ask leaders to act before they fully understand why.
Models tend to be opaque, slow to set up, expensive, imperfect, and unpopular because the actions they demand feel harsh or abrupt. When leaders don't act on what the model says, the AI project fails — even when the model was right.
A senior health-insurance phone sales business was losing $1–2M a year on $79M in revenue. We built a detailed AI model to predict P&L per lead.
Outcome: the company did not implement. The model couldn't explain why those three states were loss-making, so leadership kept buying leads. Same losses, year after year. That is exactly the agency problem the PE model was invented to solve.
We help PE teams adapt their existing toolkit — rigorous metrics, patient investment, and controlling interest — to AI projects across the portfolio.
Walk the portfolio and identify real opportunities — and the traps — company by company.
Stand up a tight 5–6 person AI team at the management company — practical operators, not theorists.
Deploy a shared quant stack — data plumbing, orchestration, lineage — across portfolio companies.
Get inside each company to find the low-hanging fruit. Most of it is hiding in plain sight.
Common platforms and strategies that work across the fund — not bespoke science experiments.
Build the rigorous metrics that let the PE model do what the PE model already does best.
Replicate operational tasks on shared platforms, then optimize headcount where the data earns it.
The goal is to leave your fund with a self-sufficient team — not to build dependency on us.
