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The right scope depends on where you are in the deal or portfolio lifecycle. Each engagement below starts with the situation you are actually in, what question needs answering, and what the work produces.
Four situations drive most engagements. Each has a different scope and timeline, with the same seven-dimension methodology running underneath all of them.
The seller attributes a meaningful portion of revenue, margin, or competitive advantage to AI. Your IT diligence team can confirm the technology exists. They cannot evaluate whether the AI system is production-grade, whether the financial attribution holds under scrutiny, or whether there are failure modes that will surface after close: hallucination, model drift, vendor dependency, data quality gaps.
Typical timeline: 2–3 weeks from access to data room materials.
The AI initiative is in the VCP. The company reports it is on track. The operating partner wants an assessment that does not come from the same team that built the program. Can it scale? Is the financial case realistic? What has to be true for the projected EBITDA impact to materialize, and is it true today?
Typical timeline: 2–3 weeks proactive. 1–2 weeks if the initiative is already underperforming and the board wants answers quickly.
You do not need a full evaluation every quarter. You need an ongoing independent view: monthly reporting that feeds the VCP tracker, early warning when an AI initiative is drifting off course, and evaluation of new proposals and vendor claims as they surface. The operating partner receives information that has not passed through the management team first.
Commitment: 2–4 days per month per portfolio company.
AI capabilities have been part of the value creation narrative. A buyer's diligence team will test that narrative. The question is whether the documentation of what the AI does, how reliably it performs, and what financial impact it has produced can withstand that scrutiny. Finding problems in the data room costs time, leverage, and multiples.
Typical timeline: 6–12 weeks before the anticipated process launch. Earlier is better.
Every engagement uses the same seven-dimension framework, adapted to scope. Dimensions are scored independently and combined into a maturity assessment that supports financial modeling and board reporting.
Model design, infrastructure, and the gap between a working pilot and a production-grade deployment.
Training data representativeness, pipeline reliability, and whether the data infrastructure supports the claimed use case at production scale.
Hallucination rate assessment, accuracy benchmarking, monitoring architecture, and whether guardrails exist and work.
Monte Carlo modeling of realistic value ranges. Cost and revenue impact attribution. Sensitivity analysis and tornado diagrams.
Vendor dependency concentration, IP ownership, contract terms, and portability of the AI system on a change of control.
Change management assessment, workflow integration, and whether the organization can absorb the AI program at the scale projected in the value creation plan.
AI governance framework assessment mapped to NIST AI RMF and ISO/IEC 42001. Regulatory exposure in relevant jurisdictions.
AI-specific attack surface: prompt injection, model extraction, data exfiltration exposure. CISSP-grounded review of security architecture and controls.
Deliverables are structured as inputs to whatever VCP platform the PE sponsor uses. We produce the evaluation data. Your VCP tracker displays it. Your board deck presents it. The evaluation layer and the tracking layer work together.
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The first conversation is 30 minutes. You describe what you are facing and we determine together whether and how an evaluation would be useful. No pitch, no obligation.
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