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Before investing in an AI startup, verify five things in parallel: the company is legally and financially what it claims, the product works for a defined customer problem, its data and model supply chain is lawful and resilient, its risks are governed across the system lifecycle, and the deal terms fit the evidence. A polished demo or one impressive accuracy number is not enough.
Use the checklist below to request documents, test claims, and record uncertainty. It is a general framework, not a finding that any company is safe, compliant, technically sound, or investable. The right legal and financial review depends on the company, transaction, offering, and jurisdiction.
How to run an AI-startup diligence process
Ask for evidence in a shared data room, assign an owner to each question, and distinguish what is verified from what is merely asserted. Reconcile statements across contracts, system records, bank data, source code or technical documentation, and conversations with customers and staff. Keep a dated list of open issues, the person responsible for resolving each one, and the decision impact if it remains unresolved.
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1. Verify the company, team, market, and customers
Establish the legal and ownership record
- Obtain the legal name, jurisdiction, registration records, subsidiaries, beneficial ownership information, and authority of the people signing the investment documents.
- Reconcile the capitalization table with issued shares, options, warrants, SAFEs, convertible notes, debt, promised grants, and any side letters. Ask for board and shareholder approvals that the transaction requires.
- Review prior financing documents, use of proceeds, outstanding obligations, and any disputes, liens, or change-of-control provisions.
Test whether the team can execute
- Map each founder and executive’s responsibilities, relevant technical or commercial experience, references, employment status, and planned hires.
- Identify dependence on a founder, a single engineer, or another key person. Ask what documentation, succession plan, and retention arrangements would let the company continue if that person leaves.
- Confirm that employees, contractors, advisors, and research collaborators assigned their code, inventions, models, datasets, and other work product to the company.
Define the customer problem and competitive substitute
Write down the target customer, the costly or important job to be done, the current alternative, and the specific reason the product is better. Map direct competitors, internal customer-built tools, conventional software, services, and foundation-model providers that could make the product easier to replicate.
Verify traction rather than repeating the pitch
- For every claimed customer, request the contract, order form, invoice, usage record, renewal status, and payment evidence where appropriate.
- Separate paid production use from pilots, unpaid trials, bookings, signed-but-not-started contracts, and collected cash.
- Reconcile revenue, retention, churn, expansion, and customer concentration to the general ledger and customer-level records. Speak with customers using appropriate confidentiality controls.
2. Examine what the product actually does
Trace the complete system
Ask the team to diagram a real production request from input to outcome: data collection and processing, prompts or other orchestration, retrieval, tools, models, post-processing, human review, storage, and the action taken by the user or an integrated system. State which decisions or tasks the AI affects, who relies on the output, and what happens when the system is wrong, unavailable, manipulated, or delayed.
Separate owned components from dependencies
For each component, record whether it is built by the startup or supplied by a model provider, software package, dataset, cloud service, labeling vendor, or other contractor. Note the version, service-level terms, data-retention settings, geographic processing location, and replacement difficulty. A product can appear proprietary while its most important behavior depends on a third party that may change pricing, limits, policies, or model behavior.
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Demand context-specific performance evidence
- Request the evaluation protocol, representative test data, baseline or comparator, sample sizes, date, and the metric definition behind every material performance claim.
- Inspect false positives, false negatives, hallucinations, abstentions, latency, cost per task, and failure cases—not only an average score.
- Where relevant, require subgroup, language, geography, edge-case, adversarial, and out-of-distribution testing. Ask how uncertainty and limitations are shown to users.
- Check whether test data overlaps with training or tuning data and who controlled the evaluation.
NIST’s AI RMF Core identifies explanation, validation, and documentation as outcomes to consider. Ask for model cards, evaluation reports, change logs, and documentation that explains how outputs should be interpreted in the intended context.
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Check production controls
- Review monitoring for quality, drift, latency, cost, abuse, security, and availability.
- Inspect release approval, model and prompt versioning, rollback, access control, incident reporting, and human-escalation procedures.
- Ask for examples showing that these controls operated during a real change or incident, rather than accepting policy documents alone.
AI risk management is continuous across the system lifecycle, as described in the NIST AI RMF Core.
3. Audit data, model supply chain, and intellectual property
Build a data inventory and provenance trail
- List training, fine-tuning, evaluation, and production data, including sources, collection methods, custodians, retention periods, and deletion processes.
- For each dataset, identify the rights that permit the intended use, whether personal or confidential information is included, how consent or another legal basis was obtained where applicable, and how provenance is recorded.
- Check segregation between customer data and training or evaluation data. Confirm whether customers can opt out, request deletion, or restrict secondary use.
Read every material third-party term
Obtain agreements and current terms for models, datasets, software, cloud infrastructure, labeling, and other vendors. Look specifically for restrictions on commercial use, outputs, retention, training, disclosure, audit, geographic processing, termination, rate limits, and price changes. Record renewal dates and notice periods.
Plan for dependency failure
Identify suppliers whose loss, degradation, policy change, outage, or price increase could impair the product. Ask whether the company can switch models, run a smaller fallback, export required data, or operate in a degraded mode. Request recovery objectives, tested contingencies, and the capital and engineering time needed to execute them.
Assess rights and infringement exposure
Review invention and copyright assignments, open-source notices, dataset licenses, model terms, employee and contractor agreements, and any research collaboration restrictions. Ask qualified counsel to assess infringement, privacy, publicity, trade-secret, and contractual risks based on the actual licenses, contracts, and product behavior. NIST specifically identifies third-party data, software, and intellectual-property risks in its AI risk-management materials.
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4. Evaluate governance, safety, and legal exposure
Govern: assign accountability
Identify who owns AI risk decisions, who can pause or withdraw a system, how legal and compliance advice reaches product teams, and what board or investor reporting exists. Request policies, approval records, training, escalation paths, and a register of material risks.
Map: define context and foreseeable misuse
Document intended users, affected people, deployment environments, high-impact decisions, sensitive data, and foreseeable misuse. Map risks arising from the model, data, vendors, interfaces, automation, and downstream customer use. Consider consequences for people who never directly interact with the product.
Measure: test performance and harm
Connect each important claim to a test and an owner. Examine limitations, uncertainty, bias, security, privacy leakage, prompt injection, data poisoning, harmful content, and other failure modes relevant to the use case. Ask how complaints and external reports are converted into new tests.
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Determine who accepts residual risk, how incidents are triaged and disclosed, how mitigations are tracked, and how lessons feed back into development. Request incident records, material customer complaints, security assessments, remediation evidence, and withdrawal or shutdown criteria, subject to suitable confidentiality controls.
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Map the applicable law with counsel
Have qualified counsel identify relevant privacy, consumer-protection, product-liability, employment, sector-specific, intellectual-property, and AI-specific requirements in each operating and customer jurisdiction. A voluntary framework does not establish legal compliance. The OECD Due Diligence Guidance for Responsible AI (2026) provides another reference for scoping risks across operations, products, services, and business relationships; confirm obligations against the original guidance and applicable law.
5. Reconcile finances and model the investment
Request primary financial records
- Obtain historical financial statements, management accounts, bank and cash information, accounts receivable and payable, tax filings where relevant, the operating plan, and the assumptions behind projections.
- Reconcile reported revenue to contracts, invoices, payment records, usage, refunds, credits, renewals, and churn.
- Examine gross margin, cloud and inference costs, support and labeling costs, customer concentration, cash burn, runway, hiring needs, and capital required to reach the next milestone.
Stress-test AI-specific economics
Model how margins and cash needs change with usage, model-provider pricing, token or compute consumption, latency requirements, data-labeling volume, customer support, and a fallback provider. Ask what happens if the largest customer expands slowly, a supplier raises prices, or the company must run redundant infrastructure.
Read the financing terms line by line
Review the capitalization table alongside option-pool increases, outstanding convertibles or SAFEs, debt, liquidation preferences, conversion mechanics, pro rata and information rights, board and consent rights, protective provisions, use of proceeds, and milestone conditions. Build dilution and liquidation scenarios for plausible downside cases. There is no authoritative AI-startup-specific financial threshold that can substitute for company-level analysis.
6. Verify the offering and investment claims
Challenge AI-powered claims
The SEC, NASAA, and FINRA investor alert on AI and investment fraud warns that AI claims can be used in fraud and that AI-generated information can be false or inaccurate. Verify underlying evidence, consult multiple independent sources, investigate registration status where relevant, and consider advice from a registered investment professional.
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Confirm the exemption and investor eligibility
For a US Regulation D offering, identify the specific exemption and review the eligibility and verification process with counsel. The SEC’s March 21, 2025 guidance on assessing accredited investors describes verification methods and explains that merely checking a self-certification box, without other relevant knowledge or steps, is insufficient in the circumstances described. Do not extend that narrow guidance to every offering or jurisdiction.
7. Compare multiple AI startups on the same evidence
Use a written comparison matrix rather than a single unsupported score. For every company, record the evidence, date, source, confidence, and unresolved questions.
| Dimension | Questions to answer |
|---|---|
| Customer value and traction | Who pays, what problem is solved, and are revenue and retention verified? |
| Product performance | Does it work in the intended context, including edge cases and human handoffs? |
| Defensibility | What prevents replication by a customer, competitor, or model provider? |
| Data rights | Is provenance documented and is the intended use permitted? |
| Third-party dependence | How exposed is the company to model, cloud, data, or policy changes? |
| Evaluation and monitoring | Are tests representative, repeatable, and connected to production controls? |
| Governance and incidents | Who owns risk, and is there evidence that escalation and remediation work? |
| Regulatory and liability exposure | Which jurisdictions and sector rules apply, and what counsel has been engaged? |
| Unit economics and capital intensity | How do margins, burn, and runway change as usage and provider prices move? |
| Team execution | Are responsibilities clear, key-person risks controlled, and hiring realistic? |
| Investment terms | What dilution, preference, governance, information, and downside rights attach to the investment? |
8. Turn findings into an investment decision
- Create an evidence register with one row for each material claim, the document or test supporting it, the date, and the person who verified it.
- Classify issues as resolved, acceptable with a covenant or milestone, requiring a price or term adjustment, or a reason to stop.
- Ask the founders for written responses to contradictions and missing documents; preserve both the question and the answer.
- Model downside cases for technical failure, supplier loss, regulatory restriction, slower sales, higher inference cost, and additional financing.
- Have securities, tax, intellectual-property, privacy, and other specialist counsel review the transaction and the risks relevant to the company’s jurisdictions.
- Write the decision memo: what is proven, what remains uncertain, which assumptions drive value, what protections are required, and what evidence would change the decision.
A startup can pass a demo and still fail diligence if its customer claims, rights chain, dependency plan, monitoring, or capitalization cannot be verified. Conversely, an early company may have limited history but make uncertainty explicit and show disciplined controls. The quality of evidence and the team’s response to adverse findings are themselves investment signals.
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