Separate AI signal from the noise.

Corefour gives private equity sponsors the evidence to price AI risk and upside before a deal and back the few opportunities most likely to create value after close.

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Before a deal

Diligence

For deals where AI could move revenue, margin, or execution risk. We test the commercial assumptions, technology and data readiness, and management capability, then size the capital and time to value.

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IC decision package
  • Revenue-at-risk view
  • Readiness assessment
  • Scenario EBITDA model
  • Investment committee verdict and conditions
Before broad access

Enablement

We stand up a governed AI environment in the company’s cloud, set what data each tool may touch, and train each function on the work it does.

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Safe operating foundation
  • Governed cloud environment
  • Access and data controls
  • Role-based training
  • Adoption and usage measures
Before allocating capital

Discovery

We trace workflows, systems, transactions, data, and exceptions to rank the few AI use cases that merit capital.

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Investment priorities
  • Workflow and data map
  • Use-case economics
  • Feasibility and dependency view
  • Sequenced value roadmap
After discovery

Transformation

We build the data, infrastructure, baseline, and working proof for the leading opportunity, and continue only when the measured result supports the investment.

Explore transformation
Evidence to scale
  • Technical assessment
  • Baseline and success metrics
  • Working proofs of concept
  • Full production deployments
How we work

Most AI ideas should never become projects.

  1. The people who advise can build.The team that sizes the opportunity builds the software that tests it.
  2. Evidence earns the investment.We set the baseline, measure the result, and expand only when the numbers hold.
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Discuss a target or portfolio company.

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