Data Layer and AI Strategy for PE Portfolio Companies

The governed foundation that lets agentic AI deliver

We build the data foundation AI needs: connected sources, consistent definitions, and clear rules on who and what may use which data.

From work we have done

We have built a PE-backed accounting firm's business-ready data layer, unified client view and data organization, and rebuilt a SaaS company's leadership KPI review.

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Five layers, from scattered sources to AI that can act

An agent asked about margin by customer should use the governed definition and only the data it may see.

1 · Integration

Bring the sources together

Business units and acquired companies, joined reliably

2 · Semantic layer

One definition of every metric

The same for every report and every agent

3 · Ontology and knowledge map

How the business fits together

Customers, products, sites and contracts, related explicitly

4 · Governance and operating model

Who owns it, and who may use it

Ownership, access and routines that keep it accurate after we leave

5 · Agentic access

AI that works from the governed layer

Agents query through controlled interfaces such as MCP, under the same rules as people

Figure 1. Each layer depends on the one beneath it. Skipping to the top is why so many AI pilots stall.

Worked out in practice, written down in our white paper

Our white paper, The Governed Data Layer, sets out how semantic layers and MCP fit together for agentic data work. The reference implementations behind it show what governance adds, and what goes wrong without it.

Make the method part of how your AI works

How we define metrics, trace them to source and govern who can change them can be packaged as a skill or agent your own AI tools use, so an assistant asked about revenue or margin applies the agreed definitions instead of improvising them.

Build the foundation AI can use

Most engagements start from a Data and AI Readiness assessment, so the build is sequenced and costed before it begins.

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