Resource
AI due diligence checklist
The evidence request list behind our engagements. Use it to structure your own data room request, or to see what a buyer will ask for before they ask.
The frame
Eight questions the evidence has to answer
The requests below exist to resolve these eight questions. A request that cannot be tied back to one of them is noise.
Does it work?
Measured performance against the task the buyer is actually paying for.
Is it real?
Claims traced to evidence: evals, logs, code, not demo footage.
Is it proprietary?
What is owned versus rented from a foundation model provider.
Is it scalable?
Behaviour under load, latency budgets, failure modes.
Is it economical?
Inference economics and gross margin at realistic usage.
Is it valuable?
Whether AI features drive adoption, retention and price.
Is it durable?
Data moat, switching costs, model-provider commoditisation.
Can the org execute?
Team depth, evaluation discipline, AI governance maturity.
Evidence requests
What to ask for
The full request list, grouped by area. Tell us where to send it and it opens below.
Get the checklist
Unlock the full evidence request list
Six groups of requests covering model and evaluation, data rights, economics, reliability, governance, product and team. It opens on this page immediately.
Scope
The sixteen dimensions the checklist maps to
Each request above resolves one or more of these assessed dimensions.
- AI
Model architecture
What produces the output, and how much of it the company controls.
- AI
Evaluation discipline
Held-out sets, regression suites, and whether results are reproducible.
- AI
Training & fine-tuning
What was trained, on what, and whether it measurably improved the task.
- Technical
Data pipeline
Ingestion, chunking, freshness, tenancy isolation and lineage.
- AI
Data rights
Licensing, customer terms and training-use permissions behind the corpus.
- Technical
Retrieval quality
Relevance and degradation as tenant corpora grow.
- AI
Agent reliability
Tool-call errors, escalation rates and observed autonomy.
- AI
Inference economics
Cost per unit of value delivered, and margin at realistic usage.
- Technical
Latency & scalability
Behaviour under load, latency budgets and failure modes.
- AI
Model dependency
Supplier concentration, exit paths and tested fallbacks.
- Technical
Security posture
Customer data in prompts, logs, embeddings and traces.
- AI
AI governance
Policy, model change management, human review and audit trail.
- Technical
Observability
Whether quality regressions are detected before customers find them.
- Product
Product adoption
Marketed AI features versus features actually used.
- Product
Workflow fit
Where the AI sits in the customer's process and what it displaces.
- Technical
Team & execution
Depth of people who can actually change model behaviour.
Want the annotated version?
Email ask@probatio.tech and we will send the version with the follow-up questions, the red flags each request tends to surface, and how to read a non-answer.
Considering an AI investment?
Bring the thesis, the data room and the timeline. We will tell you what evidence exists, what is missing, and what it means for the deal.