Bring AI into your portfolio, without the failed pilots that burn a value-creation thesis.
We help private-equity firms move from “we should be using AI across the portfolio” to “our companies are shipping AI that moves EBITDA”, with a repeatable value-creation playbook, portfolio-grade infrastructure, and ROI instrumentation built in from day one.
Where Pre-AI portfolios stall.
Most firms standing up an AI value-creation thesis hit the same wall in the same place. The pattern repeats across portfolio company after portfolio company.
The thesis looks promising.
An operating partner runs an AI pilot at one portfolio company, a model that looks impressive in a board deck. The deal team greenlights an AI mandate across the fund. Budget gets allocated.
The infrastructure question gets deferred.
The pilot was built fast, in a vacuum, by a vendor who left. Production needs each company’s own data architecture, integration with legacy operating systems, and evaluation pipelines, none of which were in scope, and none of which transfer to the next company.
The use case wasn’t tied to value creation.
The pilot was chosen because it was technically tractable, not because it was the highest-EBITDA-impact use case for that company’s model. The opportunities that actually move enterprise value stay un-built.
There’s no ROI instrumentation.
The feature ships, but nobody can tie it to a value-creation metric the IC or LPs recognize. The win can’t be templated, and the next company starts the argument from scratch.
The thesis stalls.
The AI mandate loses sponsorship at the fund level. The investment isn’t paid back in enterprise value. Every portfolio company keeps reinventing the same wheel.
We exist to break that cascade. →
How we work.
A structured 60–90 day engagement that sequences value-creation strategy first, then portfolio infrastructure, then deployment, with measurement and governance baked into every stage, and built to be repeated across the portfolio.
Value-Creation Mapping
We start by mapping where AI actually pays off across a portfolio company: every candidate use case scored on EBITDA impact, technical tractability, and how well the win replicates to other holdings. You get a ranked thesis, not a wishlist.
Every candidate use case, scored before a line of model code.
We map and prioritize opportunities against EBITDA impact, technical tractability, and how cleanly the win replicates across the portfolio, specific to the company. You walk away knowing exactly what to build first, and why it templates.
The panel that proves what the deployment is actually delivering.
Measurement systems and runbooks quantify the value to the operating team and to the investment committee, so the case for rolling it across the portfolio makes itself. Evidenced, not asserted.
What you walk away with.
Concrete artifacts, portfolio-grade infrastructure, and a shipped deployment, not slides.
A first portfolio deployment, shipped, not piloted.
What a Pre-AI engagement looks like when it lands: a scoped feature live in production at a portfolio company, instrumented from day one and built to template.
An operating partner had a promising AI pilot at one holding but no production path: vendor-built, no integration with the company’s operating systems, and no way to tie it to a value-creation metric the IC would recognize.
We scored the highest-EBITDA-impact use case, built the data and integration foundations as a reusable pattern, and shipped the first production feature, instrumented to measure operating outcomes from day one.
Engineering under real-world constraints, before it was an AI problem.
Cryenx’s engineering legacy is in performance-critical, real-time systems for brands where production reliability wasn’t optional: Disney, Coca-Cola, Apple, LEGO, Warner Bros, SXSW. Real-time interactive systems, AR/XR experiences, integration with complex existing infrastructure.
That experience, engineering under real-world constraints, integration with legacy systems, observability discipline, is exactly the discipline portfolio AI requires, where a deployment has to land in a real operating business and pay back in enterprise value. Most AI teams don’t have it. We do.
