CCA runs its technology engagements on eight frameworks.

Five dimensions, each scored 0–100. Every finding is priced as a CapEx requirement, an EBITDA drag, or an exit multiple implication. The output is built to inform price and deal terms.

CLEAR™ has five phases that run from close to exit, when the fund realizes its return. It starts from the findings of a PRISM™ diligence review and organizes the technology work during the hold.

A framework for turning AI from a cost and a distraction into a measurable margin driver. It covers use case prioritization, governance, and a board-ready AI narrative, each tied to EBITDA.

A six-layer model of AI spend (API, infrastructure, tooling, people, waste, risk) that uses governance as the detection layer. Three of the six layers account for 55 percent of spend and are the least visible. Working all six recovers 20 to 34 percent of AI spend in the first year.

A five-level measurement hierarchy (Cost, Adoption, Productivity, Business Outcomes, Enterprise Value) for verifying what AI investment produced. Most companies can evidence the first two levels and report as though they had evidenced the last two.

The engagement that runs between the diagnostic and the target architecture: five phases that take a portfolio company from unlisted AI systems to a board-ready, regulator-ready governance posture.

A unified governance architecture (5 pillars × 4 lifecycle stages = 20 control domains). It treats AI systems as enterprise operational infrastructure that needs identity, access, observability, and resilience controls.