AI Technical Due Diligence

Know what is real before you invest, acquire, or scale.

AI Technical Due Diligence is a five- to ten-business-day engagement, depending on scope and access, that clarifies what is real, what remains unverified, what is risky, and what should happen next.

Email ChristopherWays to Work

Who it is for

When technical diligence is useful

Useful when

The demonstration is strong but the evidence behind capability, data rights, evaluation, reliability, cost, or team maturity is unclear.

  • 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.

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

Independent evaluation with access defined before work begins.

What changes

What the assessment must answer

Before

The demonstration is strong but the evidence behind capability, data rights, evaluation, reliability, cost, or team maturity is unclear.

After

A practical view of what is real, what remains unverified, what is risky, and what should happen next.

Christopher leads the engagement directly

AI Technical Due Diligence work is led by Christopher, with his judgment present in the decisions, deliverables, and readout.

Outputs

What you receive

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 review works

  1. Confirm scope, access, confidentiality, and conflict boundaries.
  2. Review product, architecture, data, model, vendor, and team evidence.
  3. Inspect claims against available demonstrations and artifacts.
  4. Assess operating risks, release maturity, cost behavior, and scalability.
  5. Deliver decision view and priority questions.

Scope

What the diligence includes—and excludes

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

Situations where this is the wrong engagement.

  • 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 exclusions

Services not included unless separately scoped.

  • Not legal advice.
  • Not financial or tax advice.
  • Not a penetration test.
  • Not formal security certification.
  • Not an audit opinion.

Relevant experience and evidence

Relevant experience and frameworks

The following operating experience and evaluation frameworks are relevant to testing technical claims, dependencies, risks, and post-transaction priorities.

Assessment context

Technical breadth

Experience spans cloud data platforms, AI/ML systems, evaluation, regulated workflows, architecture, vendor management, and organizational capability.

Review Technical breadth

Reliability and release discipline

Sailthru

Reduced customer-impacting incidents by 80% by building engineering-process controls, release discipline, and quality infrastructure.

Review Sailthru

Next

Related resources

Email Christopher

Email Christopher

Tell Christopher what you are trying to decide, own, build, evaluate, or unblock.

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