AI Technical Due Diligence
AI Due Diligence for Investment and Underwriting Decisions
FrontierScale AI gives private equity, venture capital and insurance teams an independent view of an AI company's technology, performance, defensibility and risk before capital is committed or exposure is underwritten.
Service frameworks
FrontierScale AI Assessment
Investment view
Proceed with targeted diligence
Findings are framed for valuation, deal conditions and post-investment priorities.
Next: confirm model evaluation evidence, data rights and remediation priorities.
Illustrative framework only. Not based on a real company.
Two primary use cases
One body of technical evidence. Two decisions it informs.
Private Equity & Venture Capital
Assess whether an AI company's technology can support its investment thesis, valuation and value-creation plan. We validate technical claims, examine product and model performance, assess scalability and economics, and determine whether the company's competitive advantage is genuinely defensible.
Insurance Underwriting
Assess the technical and operational risks presented by an AI company, product or system before underwriting the exposure. We examine how the AI operates, where failures could arise, what controls are in place and how security, privacy, data, model and regulatory risks could affect the insured risk.
The problem
AI creates risks that conventional due diligence may not reveal.
A strong demo, experienced technical team or growing customer base does not always reveal how an AI system will perform in production. Material risks may sit within model dependencies, data rights, failure modes, infrastructure costs, governance gaps and operational weaknesses. These issues can affect both investment value and underwriting risk.
Investment committee summary
Confidential draftAI Due Diligence Scorecard
- Commercial AI potential
- Technical AI maturity
- Data & model risk
- Governance & controls
- Regulatory exposure
- Value creation upside
The evidence
What investors and insurers both need to see.
Ten evidence areas, scoped to the decision in front of you — whether that decision is an investment or an underwriting exposure.
01
AI product and model performance
Capability, evaluation methods, measured performance and reliability in production rather than in demonstration.
02
Technical architecture and scalability
Architecture, infrastructure, integrations and whether the system scales without unsustainable cost or fragility.
03
Data provenance, rights and quality
Where data comes from, what rights exist, how it is maintained and whether it holds up under scrutiny.
04
Third-party model and infrastructure dependency
Reliance on external foundation models, vendors and cloud infrastructure, and the concentration risk this creates.
05
AI safety and operational resilience
Model behaviour, failure modes, monitoring and the controls that keep AI systems reliable in operation.
06
AI governance and human oversight
Accountability, risk management, documentation and where humans meaningfully oversee AI decisions.
07
Technical differentiation and defensibility
What is genuinely proprietary, hard to reproduce and capable of sustaining advantage over time.
08
Failure scenarios and potential impact
How the system could fail or cause harm, how likely those scenarios are and what the consequences would be.
09
Economic model and unit economics
Inference, evaluation and human-in-the-loop costs and how they scale with revenue, margin and usage.
10
Remediation priorities and risk controls
The practical controls and improvements that would most reduce technical, operational and commercial risk.
Investors and insurers
Different decisions. The same need for technical evidence.
Investment decisions
- 01Is the technology genuinely differentiated?
- 02Can it support the growth and margin assumptions?
- 03Are management's technical claims supported by evidence?
- 04What risks could affect valuation or post-investment performance?
- 05Can the technology deliver the investment thesis?
Underwriting decisions
- 01What could cause the AI system to fail or create harm?
- 02How likely and material are the identified exposures?
- 03Are appropriate governance and technical controls operating?
- 04Could model behaviour, data quality or operational weaknesses increase claims risk?
- 05What conditions, exclusions or improvements may require consideration?
FrontierScale AI provides technical evidence and independent risk analysis to support the insurer's own underwriting process. We do not set premiums, determine coverage or make underwriting decisions.
Outcomes
Technical evidence translated into decision-ready findings.
Every finding is framed in terms of what it means for the decision in front of you — valuation, deal terms and the value-creation plan for investors; exposure, controls and risk conditions for underwriting teams.
What clients receive
- Independent validation of material AI claims
- Assessment of technical strengths and weaknesses
- Prioritised investment or underwriting risks
- Analysis of model, data and infrastructure dependencies
- Evaluation of governance and risk controls
- Clear red flags and areas requiring further investigation
- Practical remediation and risk-reduction recommendations
- Decision-ready findings for investment committees or underwriting teams
How it works
A four-stage process, scoped to the decision
Stage 01
Scope
Agree the investment thesis or underwriting question and the technical evidence needed to answer it.
Stage 02
Review
Review technical evidence and management claims: documentation, evaluations, architecture, data agreements and metrics.
Stage 03
Test
Test capabilities, risks and dependencies through management sessions and targeted verification. Where relevant to the agreed scope, deeper activities such as code review, security testing or independent model benchmarking can be included.
Stage 04
Deliver
Deliver decision-ready findings, with risks prioritised and implications framed for the investment committee or underwriting team.
Beyond the assessment
Due diligence informs what happens next.
FrontierScale AI can help management teams and portfolio companies translate diligence findings into strategic priorities, remediation plans and practical transformation initiatives.
Related
Explore related engagements
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Pre-sign, confirmatory and post-close diligence for corporate acquirers.
AI Due Diligence for Private Equity
Diligence, portfolio uplift and exit readiness for PE firms.
AI Value Creation
Turn AI into measurable operating leverage while managing governance risk.
Before capital is committed or exposure underwritten, examine the evidence.
Get an independent view of the technology, risks and defensibility behind the business.