Data & AI Due Diligence
From claims to evidence in
data & AI
Independent, evidence-based diligence that tests what is real, what works, and what will create value—across both buy-side and exit scenarios
Data and AI are now central to most investment theses—but are rarely tested with the same rigour as financial or commercial assumptions.
We move from diagnosis to proof, using real data to validate claims, quantify feasibility, and expose hidden risks.
What we do
Diligence that goes beyond the data room
We complement commercial, financial, and technology diligence by testing the data behind the narrative.
For investors, this means de-risking price and validating value creation.
For management teams, it means strengthening the equity story and preparing for exit.
Buy-side
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Validate what is real vs claimed
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Identify hidden risks and data debt
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Test whether value levers are achievable
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Inform pricing and 100-day plan
Sell-side / VDD
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Evidence AI and data capability
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Strengthen buyer confidence
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Articulate scalable value drivers
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Support premium valuation
Our two-phase approach
From reality to evidence
01
Reality & readiness
DURATION: ~1-2 weeks
We assess the true state of the data ecosystem—separating substance from story across systems, data, people, and processes.
Key outcomes
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Clear view of what exists vs what is missing
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Identification of data debt and structural gaps
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Assessment of AI and analytics feasibility
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Validation of roadmap credibility
02
Analytical evidence
DURATION: ~2 weeks
We assess the true state of the data ecosystem—separating substance from story across systems, data, people, and processes.
Key outcomes
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Validation of data completeness and connectivity
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Testing of value levers using real data
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Review of models, code, and live systems
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Clear view of what is production-ready vs conceptual
Typical scope
What we assess
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Data estate (ERP, CRM, product, third-party dependencies)
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Data quality, structure, and governance
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AI readiness and model validity
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Infrastructure scalability and flexibility
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Real data testing of key value levers
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Gap analysis vs investment thesis
Deliverables
What you get
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Two-phase diligence report (readiness + evidence)
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Data & AI maturity assessment
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Validation of claims, risks, and gaps
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Quantified feasibility of value creation
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Clear recommendations (what / when / how much)
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Direct input into 100-day plan or exit narrative
Value delivered
Why it matters
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Evidence, not assumptions
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De-risked investment decisions
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Clear link between data, AI, and EBITDA
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Faster post-deal execution
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Stronger, defensible equity story
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Increased buyer confidence at exit
As investors increasingly evaluate data and AI as core value drivers, the ability to validate data quality, architecture, and delivery feasibility is critical to investment decisions.
Data and AI due diligence provides a structured, evidence-based view of a company’s data assets, technical foundations, and AI readiness — moving beyond management narrative to assess what is truly scalable and value-accretive.
By examining both structured and unstructured data, underlying data models, and the feasibility of priority use cases, this approach identifies gaps, risks, and unrealised opportunities early in the investment process.
Conducted ahead of or alongside commercial and technical diligence, it enables investors to quantify upside, de-risk execution, and define a clear roadmap from current state to measurable value creation.