Independent AI Value & Risk Advisory
OnRamp Growth provides independent AI intelligence for PE operating partners, portfolio company leaders, and deal advisors. We pair AI risk diligence with strategic value creation to uncover the liabilities standard IT audits miss, and to quantify and monitor realistic AI upside.
Every deal deck carries an AI value creation story. Stress-testing that story requires more than the seller's projections. Which initiatives have the data infrastructure to scale? What does the financial model look like when you test the assumptions? Where is the genuine operational leverage, and what conditions have to hold for it to show up in EBITDA?
We model the upside on our own terms, so the number in your deal model reflects what the system can deliver under realistic conditions.
Standard IT diligence confirms whether a company uses Python or has a data warehouse. It does not evaluate whether the AI forecasting model that "drives 15% of revenue" is production-grade or a demo running on a curated dataset. It does not evaluate whether the chatbot that "reduced support costs 40%" is producing reliable answers or hallucinating responses that create liability.
We evaluate what the system actually does, tested against production conditions, not what the documentation describes.
VCP trackers record whether an AI initiative hit its milestone. They do not tell you whether the initiative can scale to produce the EBITDA impact the value creation plan projects, or whether it is tracking a pilot that will never reach production.
Most portfolio companies lack the internal expertise to evaluate AI objectively, challenge vendor claims, or build the measurement framework that produces auditable results. Asking a CTO to evaluate their own AI program produces the same result as asking any professional to assess their own work: optimistic findings and unexamined assumptions.
OnRamp Growth provides that outside evaluation. Independent. Quantitative. No software to sell.
Pre-close evaluation of a target's AI assets, capabilities, and claims. Technical architecture, data quality, output reliability, IP ownership, vendor dependency. Findings feed directly into deal models and provide M&A counsel with specific language for representations and warranties.
Independent evaluation of whether a portfolio company's AI initiatives can scale to produce the projected EBITDA impact. Monte Carlo financial modeling of realistic value ranges. Prioritized roadmap of what to build, accelerate, or stop. The operating partner gets a documented, independent answer before the next board review.
When an AI initiative is underperforming and the board wants answers. We determine what went wrong: technical failure, data problems, organizational resistance, vendor underdelivery. Compressed engagement with clear findings and a specific recommendation on whether to fix, restructure, or shut down.
Translating AI performance into financial terms the board can act on. Monte Carlo probability distributions with sensitivity analysis. Attribution that distinguishes AI-driven value from correlated trends. Documentation a buyer's diligence team will accept, not challenge.
Validating that AI capabilities attributed to value creation are working at exit. Technical documentation prepared before the buyer's team asks for it. Identification of gaps the buyer will find, addressed before the process starts. Surprises in the data room cost multiples.
Recurring independent signal on AI initiative health across one or more portfolio companies. Monthly reporting designed for VCP dashboards. Quarterly re-assessment. Evaluation of new proposals and vendor claims as they surface. The operating partner receives information that has not been filtered through management.
No vendor relationships. No software to sell. Every evaluation stands on what it finds. There is no pre-existing relationship with the vendor being assessed and no platform license that shapes the conclusion.
25 years building and evaluating enterprise AI systems, data architectures, and security-sensitive environments. CISSP certified. Evaluations are built on direct system experience, not on frameworks applied from the outside.
Every evaluation produces outputs an operating partner can use: EBITDA attribution, realistic value ranges, risk quantification, and documentation that answers the questions a board or buyer will ask. The work does not stop at the technical layer.
Familiar with how AI representations appear in deal documents and what M&A counsel needs to address them. Findings are written for deal professionals, not engineering teams. They can go into a board package or a data room as produced.
Whether you are evaluating an acquisition target's AI claims, assessing a portfolio company's program before a board review, or preparing documentation for an exit, the right starting point is understanding what you are actually dealing with. That conversation takes 30 minutes and carries no obligation.
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