AI Technical Due Diligence Checklist
A practical claim and evidence framework for inspecting AI capability, data rights, vendor exposure, reliability, cost behavior, team maturity, and post-transaction priorities.
Representative
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At a glance
The engagement in practical terms.
| Field | Answer |
|---|---|
| What it is | AI Technical Due Diligence |
| Role | Evaluate |
| Who it is for | Investors, acquirers, boards, and executives evaluating an AI company, product, team, or claim. |
| When it is useful | The demonstration is strong but the evidence behind capability, data rights, evaluation, reliability, cost, or team maturity is unclear. |
| Expected outcome | A practical view of what is real, what remains unverified, what is risky, and what should happen next. |
| Typical duration | Five to ten business days, depending on scope and access |
| Working model | Independent evaluation with access defined before work begins. |
| Primary CTA | Request Technical Diligence |
Recognition
This is the situation it is built for.
- The demonstration is stronger than the technical documentation.
- AI claims are difficult to verify.
- Evaluation methods are informal.
- Data rights or vendor dependencies are unclear.
- The roadmap assumes capability the current team may not possess.
- Reliability and cost behavior are not visible.
- The deal team needs a practical technical view rather than a generic checklist.
Named evidence
Evidence is used with boundaries.
AI evaluation scorecard
Scorecard for testing task success, groundedness, tool use, policy compliance, escalation, and regression behavior.
Representative
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Christopher's enterprise leadership chronology
First-party chronology separates current Bato Labs work from prior-employer data, AI, product, and technology leadership experience.
CurrentHistorical
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Who leads the work
Christopher Petrino leads every Bato Labs engagement directly.
I lead every Bato Labs engagement directly. When specialist expertise is required, it is identified and scoped explicitly rather than hidden behind a generic delivery team.
- Founder of Bato Labs, the product lab and executive practice.
- 15+ years across data, AI, product, cloud, SaaS, healthcare, and enterprise technology contexts.
- AI Technical Due Diligence work is led by Christopher rather than assigned to a generic delivery team.
- Specialist support is scoped explicitly when an engagement requires it.
Cost of continued delay
The cost of underestimating technical risk
- Mispriced technical risk.
- Unexpected post-transaction investment.
- Data-rights or vendor surprises.
- Product claims failing under production conditions.
- Key-person dependence.
Engagement outcome
What the work produces.
Claim and capability assessment
Clarifies what is real, demonstrated, unverified, and dependent on vendors, manual processes, or restricted data.
Technical and operating risk view
Covers product, architecture, data, models, vendors, team, evaluation, security and privacy posture at the agreed level, release maturity, cost, and scalability.
Decision and post-transaction priorities
Provides critical questions, material risks, investment or deal considerations, immediate remediation, and optional first-100-day priorities.
Process
How the engagement moves.
- Confirm scope, access, confidentiality, and conflict boundaries.
- Review product, architecture, data, model, vendor, and team evidence.
- Inspect claims against available demonstrations and artifacts.
- Assess operating risks, release maturity, cost behavior, and scalability.
- Deliver decision view and priority questions.
Fit
Ideal fit and not a fit.
Ideal fit
- The sponsor needs independent technical judgment.
- The target can provide meaningful artifacts, demos, and stakeholder access.
- The decision depends on practical technical and operating risk, not only code review.
Not a fit
- You need legal, financial, tax, audit, or formal security opinions.
- You need a penetration test or formal certification.
- Access is too limited to support the requested conclusion.
Scope and boundaries
Boundaries are part of the offer.
- Not legal advice.
- Not financial or tax advice.
- Not a penetration test.
- Not formal security certification.
- Not an audit opinion.
Logistics
Client participation and next step.
Client participation
The client provides current artifacts, stakeholder access, and a named sponsor who can make or escalate decisions.
Timing
Five to ten business days, depending on scope and access. Timing may change when scope, access, or contracting constraints change.
Next step
Use the routed contact form so the first response has the correct opportunity context.
Request Technical DiligenceFAQ
Common decision questions.
Is a release audit required first?
No. It is a strong entry point when the blockage is unclear, but some situations move directly to delivery, leadership, diligence, advisory, or an executive conversation.
Does Bato Labs provide a large delivery team?
No. Bato Labs is founder-led. Christopher leads the work directly and scopes specialist support explicitly when needed.
Is this legal, financial, or security diligence?
No. AI Technical Due Diligence is a practical technical and operating assessment. It is not legal advice, financial advice, tax advice, a penetration test, formal security certification, or an audit opinion.
What if access is limited?
The output will distinguish what is demonstrated, documented, inferred, unverified, vendor-dependent, manually assisted, or roadmap-dependent. Limited access narrows the confidence of the conclusion.
Start the right conversation
Know what is real before you invest, acquire, or scale.
Choose the closest route. The contact form will preserve the context so the first reply can focus on the right decision.