Production-grade AI value creation across your portfolio.
We help operating partners and value-creation teams turn AI from a board-deck talking point into measurable portfolio EBITDA: faster diligence, a repeatable playbook, and rollouts that standardize across every company you own.
AI that compounds across the portfolio.
The approach starts in diligence, lands a first win at one portfolio company, hardens into a reusable playbook, and is rolled out across the rest of the book, instrumented for value the whole way.
Diligence & AI thesis
Before close, we read each target’s data assets, tech debt and AI-upside the way an operating partner reads a P&L, so the value-creation plan has an AI line item from day one, not a year in.
The state of AI value creation in private equity.
Every firm now has AI in the value-creation deck. Almost none have a repeatable way to actually deliver it across a portfolio, which puts PE in a higher-execution-bar quadrant than the operating companies it owns, for structural reasons.
Every company runs a different stack, data maturity and team. A win at one doesn’t transfer for free. You need a playbook engineered to adapt, not a one-off.
Value has to land inside the hold period and survive to exit. A slide-deck strategy that takes two years to operationalize is value you never capture.
LPs and the IC expect AI value creation to show up in the numbers, not the narrative: instrumented, attributable EBITDA impact, deal by deal.
The opportunity is real. The execution bar is higher.
What goes wrong with AI value creation in PE.
Four failure patterns we see again and again across portfolios, and what they require to fix at the execution layer.
Slide-deck AI that never ships
AI sits in the value-creation plan but never reaches production at a single company. The thesis is sound; the firm has no engineering function to operationalize it, so the line item stays a promise.
We act as the portfolio’s AI engineering team: one production win first, then a repeatable playbook.
A win that doesn’t transfer
A pilot that worked at one company dies at the next because nothing was abstracted. Different stack, different data, different team, and the firm starts from zero each time.
We build the win as a reusable playbook (reference architecture, adaptation layer, governance) engineered to transfer.
No standardization across the book
Each company picks its own vendors, models and approach. The firm gets no shared leverage, no consistent reporting, and no way to compare AI ROI deal-to-deal.
We standardize the stack and reporting across the portfolio: shared reference architecture, one ROI view, common governance.
Value that decays before exit
An AI system that shipped early in the hold quietly degrades: data shifts, the champion leaves, nobody owns it. The EBITDA bump you underwrote isn’t there at exit.
Eval + regression in CI, portfolio value dashboards, automated retraining triggers, so the value holds to exit.
What makes AI value creation in PE different.
Three constraints that portfolio-wide AI has to be designed around, not retrofitted into.
Two engagement paths, depending on where the portfolio is.
We work differently with firms whose portfolio hasn’t put AI into production versus firms ready to standardize and scale what’s already working.
For firms whose portfolio hasn’t yet shipped production AI.
We map where AI delivers the most EBITDA upside across the portfolio, land a first production win at one company, and build the infrastructure foundations and ROI instrumentation that make it repeatable.
First portfolio AI win shipped, with EBITDA measurement baked in.
For firms ready to standardize AI across the portfolio.
We audit what’s running across your companies, turn the wins into a standardized playbook, and roll it out with quantified before/after value: cost, reliability, attributable EBITDA, one reporting view.
One AI playbook, scaled across the book, value measured deal-to-deal.
We don’t just plan it. We ship it.
A measured outcome from a production-AI engagement, the same operational discipline we bring to portfolio value creation.
The execution bar is higher here. So is ours.
Before AI was the conversation, we were building performance-critical, real-time systems for Disney, Coca-Cola, Apple, LEGO, Warner Bros, and SXSW: systems that had to run reliably under unpredictable load, integrate with existing infrastructure, and hold up at brand scale.
That same engineering discipline (observability, integration rigor, real SLAs, measured ROI) is what AI value creation requires. Especially across a PE portfolio, where heterogeneous companies, a short hold clock, and LP accountability raise the bar higher than in most other software contexts.
