The gap we fill
PE portfolio monitoring is a well-established software category. Maestro, Chronograph, Allvue, Planr track KPIs, flag variances, and automate board reporting. They are the dashboard.
We evaluate the engine. A VCP tracker can show a green status on an AI initiative. We determine whether that green status reflects an AI system that can scale to produce projected EBITDA impact, or whether it is tracking a pilot that will never reach production.
A company's AI forecasting model "drives 15% of revenue." Standard IT diligence confirms the model exists and runs on production infrastructure. It does not evaluate whether the model is retrained frequently enough to remain accurate, whether the training data reflects current market conditions, or what happens to revenue when the model drifts. These are AI-specific failure modes that require AI-specific evaluation methods.
A customer-facing AI system "reduced support costs 40%." The metric is real. What the data room does not contain: how often the system produces confident, wrong answers, and whether those answers are reviewed before they reach customers. Hallucination is not corrected by a software patch. It requires architectural guardrails. Evaluating whether those guardrails exist requires a different discipline than confirming the system is deployed.
How we compare
| Alternative | What you get instead |
|---|---|
| Big 4 firms | The senior practitioner scopes the engagement and a junior team delivers it. At OnRamp Growth, the person who scopes the work delivers the findings to the board. There is no handoff. |
| VCP tracking platforms | VCP platforms report status. We evaluate whether the AI initiatives your tracker reports on can scale to produce the outcomes they are tracking toward. The two are complementary, not interchangeable. |
| Generalist fractional CAIOs | Quantitative methodology with Monte Carlo financial modeling and seven-dimension technical evaluation. The deliverable is a scored report with specific findings and financial projections, not a presentation of strategic recommendations. |
| AI platform vendors | No vendor relationships. No software commissions. The evaluation is entirely independent of the technology being assessed. |
| Internal CTO evaluation | A CTO evaluating their own AI program will find what they built and why it was a reasonable decision. That is not the same as an independent assessment of whether it works at scale. Every other domain of due diligence uses outside evaluators for this reason. |
Where we fit in the PE lifecycle
Evaluate AI maturity, data readiness, and technical debt of the target. Quantify realistic AI upside and identify the costs that do not appear in standard diligence. Findings feed directly into deal models.
Evaluate which AI initiatives in the value creation plan can scale to produce projected EBITDA impact. Build financial models with realistic assumptions. Define measurable milestones that give the VCP tracker something worth tracking.
Ongoing independent evaluation of whether AI systems are producing reliable output. Identify hallucination, model drift, and integration failures before they appear in financial results. Evaluate vendor claims and new AI proposals as they arise.
Validate that AI capabilities attributed to value creation are working. Prepare technical documentation for buyer diligence teams before the process begins, so the data room answers the buyer's questions before the process starts.
25 years building and evaluating enterprise software and AI systems across financial services, clean energy, SaaS, and regulated industries. CISSP certified. Architect of production AI systems with hands-on work in scalability assessment, data pipeline evaluation, hallucination testing, and governance framework design.
Every engagement is conducted personally. The person who scopes the work delivers the findings to the board.