AI is live in your portfolio. Now make it compound across every company.
We help value-creation teams already rolling AI across portfolio companies turn scattered wins into a repeatable playbook: standardizing what works, killing the pilots that don’t, and proving value creation in terms an LP and an IC will accept at exit.
What a stalling AI program actually looks like.
The signs that AI across the portfolio has crossed from “promising pilots” to “value leaking out the side” are usually clear once you know what to look for; and across a dozen companies, every one of them multiplies.
Five lights. One pattern. We exist to turn them green. →
How we work.
A structured audit-then-standardize cycle that doesn’t end at one company, because a portfolio AI program never does. As you add platforms and roll the playbook into new companies, the loop runs again.
Portfolio AI audit
We inventory every AI initiative across the book, its spend, who owns it, whether it reached production, the EBITDA impact claimed, and the wins worth standardizing, so the value-creation work starts from evidence, not anecdote.
What you walk away with.
Concrete audit reports, a reusable value-creation playbook, and the cross-portfolio reporting to keep what we standardize compounding, and defensible at the IC.
Production AI at scale: the engineering pattern that ships.
A measured outcome from the same operational discipline we bring to a portfolio AI program.
An enterprise SaaS provider needed a production-grade RAG and vector pipeline serving thousands of in-app AI features, with strict latency SLAs and predictable cloud spend.
An MCP-based retrieval pipeline with a tuned vector store, caching and batching for inference-cost control, and observability for query latency and recall.
Production reliability was our discipline long before AI made it urgent.
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, and integration with complex existing infrastructure.
That discipline (observability, integration rigor, real SLAs, and operating under unpredictable load) is exactly what a portfolio AI program demands once it’s live across a dozen companies. Most teams learn it the hard way, one stalled rollout at a time. We brought it with us, and apply it to every company in the book.
