Investors know
how to say no.
Do you know
when to say yes?

AI due diligence is an independent assessment of an AI company's technology, infrastructure, pricing logic, product-market fit, and business durability before an investment decision is made. It covers what a standard financial due diligence process cannot — the technical and commercial claims that determine whether an AI business can sustain its growth.

Technical due diligence focuses on code quality, architecture, scalability, and engineering risk. AI due diligence includes all of that but goes further — examining AI-specific infrastructure, model dependency, data pipeline integrity, pricing architecture, product-market fit signals, and whether the commercial story holds up under scrutiny.

A standard AI due diligence engagement is delivered in 10 business days from kick-off to final report. The deliverable is a written assessment structured for investment committee presentation, with an executive briefing session included. Expedited timelines are available for time-sensitive deals.

A technical due diligence report covers AI infrastructure integrity, model architecture, vendor dependencies, deployment readiness, technical debt assessment, pricing architecture review, product-market fit analysis, competitive moat durability, and an executive summary with investment implications. It is written for a GP or investment committee, not an engineering team.

The most common red flags include: AI infrastructure built entirely on a single third-party model with no proprietary layer, pricing that cannot survive commoditisation, retention metrics that do not support the PMF narrative, technical debt that would require a rebuild within 18 months of acquisition, and founders who cannot explain their infrastructure costs.

Yes. I advise venture capital firms, private equity firms, family offices, and portfolio leadership teams evaluating AI-native and AI-enabled businesses. Engagements are available for pre-investment due diligence, post-investment portfolio advisory, and investor education.

Independent expertise removes conflicts of interest and brings pattern recognition from 20+ years across 76 enterprise organisations. Internal teams are often close to the deal and optimistic by nature. An independent advisor delivers a view that is objective, operator-grade, and not attached to the outcome — which is exactly what IC decisions require.

Every engagement begins with a 30-minute discovery call. Due diligence projects are scoped and delivered in 10 business days as a written report with an executive briefing. Portfolio advisory is structured as a monthly retainer scoped to the portfolio's specific needs. There is no retainer required to start — each engagement is independent.

For pre-investment due diligence, an independent advisor is almost always more appropriate than internal hiring — the scope is bounded, the timeline is short, and the independence is the point. For ongoing portfolio support, a retainer advisor can be more cost-effective than a full-time hire until the portfolio has enough AI complexity to justify dedicated headcount.

AI pricing assessment covers packaging architecture, usage economics, gross margin logic, infrastructure cost exposure, cost-to-serve at scale, and whether the current pricing model can survive commoditisation of the underlying AI layer. The goal is to determine if the business has real pricing power or is relying on temporary AI novelty.

An AI infrastructure audit covers model architecture and dependencies, orchestration layer design, data pipeline integrity, vendor lock-in risk, deployment readiness, security posture, governance frameworks, and hidden operational costs that are not visible in the P&L.

I evaluate AI-native SaaS companies, AI-enabled software businesses, technology platforms, and portfolio companies where AI infrastructure, pricing strategy, and product-market fit matter to investor outcomes. I have evaluated companies across fintech, healthcare, logistics, real estate, education, aviation, and enterprise software.