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An AI startup is investable only when its claims survive primary-document checks and reproducible tests. Verify eight connected areas before committing capital: the legal entity and ownership, customer value and revenue, technical performance and unit economics, data and intellectual-property rights, security and resilience, responsible-AI governance, team execution, and financing terms. Treat a slide deck as a starting index—not evidence.
The practical standard is simple: every material claim should have an owner, a source record, a test method, and a downside implication if it proves wrong. A polished demo cannot compensate for missing IP assignments, unverified revenue, unlawful data use, or economics that collapse when an API price changes.
Set the evidence standard before reviewing the pitch
Send the founders a written request for primary records and define how demonstrations will be tested. Ask for read-only access to a secure investor data room, a document index, version dates, and a named contact for each workstream. Keep an evidence log that records the claim, source, reviewer, date, assumptions, unresolved questions, and materiality.
- Primary evidence: signed contracts, invoices, bank and ledger records, source-control history, model-evaluation files, security reports, policies, and board minutes.
- Reproducibility: a second reviewer should be able to rerun a test with the same model version, data slice, prompts, thresholds, and environment.
- Traceability: reconcile totals between systems—for example, reported recurring revenue to contracts, invoices, usage, renewals, and cash receipts.
- Adversarial testing: include edge cases, distribution shifts, malicious inputs, privacy leakage, and failure costs rather than only the happy path.
Ask the company to label each item as signed, in negotiation, a pilot, free usage, founder-led services, or non-binding pipeline. This prevents a term such as “customer traction” from hiding very different levels of commitment.
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1. Verify the company, ownership, and exact deal
Corporate and capitalization records
Request the certificate of incorporation, good-standing evidence, organizational chart, current capitalization table, option-pool schedule, founder vesting, board and shareholder consents, prior SAFEs and notes, side letters, debt instruments, litigation schedule, and tax or regulatory registrations relevant to the business. Confirm that the entity receiving your money is the entity that owns the intellectual property and signs customer contracts. If a parent, subsidiary, or founder personally holds assets, require the signed transfer or an explicit explanation of the structure.
Intellectual-property chain of title
Obtain invention-assignment and confidentiality agreements for every founder, employee, contractor, and material technical contributor. Review schedules of patents, patent applications, trademarks, copyrights, trade secrets, domain names, third-party licenses, open-source components, foundation-model terms, and application-programming-interface restrictions. Check whether any contributor remains bound by a prior employer’s assignment or whether an open-source license conflicts with the planned commercial distribution. An undocumented right is a diligence exception, not an asset.
Security and investor rights
Read the exact instrument and all related documents. Record valuation or conversion mechanics, liquidation preference, participation, pro rata rights, anti-dilution, information rights, voting and consent rights, option-pool treatment, debt seniority, use of proceeds, and any unusual side letter. Model ownership after the round, including the fully diluted option pool and conversion of earlier instruments. Attractive growth projections do not cure unclear ownership or control provisions.
| Request | What to verify | Pause condition |
|---|---|---|
| Cap table and equity instruments | Every holder, security, vesting term, conversion rule, and side letter reconciles to board records. | Unexplained entries, informal promises, or missing prior SAFEs or notes. |
| IP assignments | All relevant people assigned inventions and confidentiality obligations to the correct entity. | A founder, contractor, or former employer retains a plausible claim. |
| Offering documents | Rights, preferences, dilution, information access, and use of proceeds are unambiguous. | Terms differ between the term sheet, subscription document, and cap table. |
2. Prove customer value, market demand, and traction
Define the paid problem
Write down the buyer, daily user, decision maker, workflow being changed, measurable outcome, sales cycle, deployment friction, and cheaper substitutes. Ask why a customer pays instead of using an existing tool, internal automation, a general-purpose model, or a services provider. For regulated or operationally critical workflows, identify procurement, security review, data-processing, and insurance requirements that can delay or prevent deployment.
Reconcile traction to records
Request customer contracts, order forms, invoices, renewal and churn records, cohort tables, usage logs, implementation statements of work, reference contacts, and pipeline evidence. Reconcile reported annual recurring revenue or revenue to the accounting ledger, bank receipts, and contract terms. Separate signed recurring revenue from pilots, trials, free usage, usage-based estimates, founder-led services, one-time setup fees, and non-binding pipeline. Reference calls should confirm the buyer, use case, amount paid, renewal intent, and whether the product—not consulting labor—created the value.
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Test durability and gross margin
Recalculate retention after price changes, model changes, customer procurement requirements, and support escalation. Compute gross margin after inference, storage, observability, human review, customer support, implementation, and third-party API charges. A software-style margin assumption is not credible if every difficult case requires manual intervention or an expensive model call.
For market sizing, demand evidence-backed assumptions: a defined customer population, realistic price, reachable distribution, and a sales capacity that matches the forecast. Keep top-down market figures separate from the serviceable segment the team can actually sell to.
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3. Test the product and technical claims
Request a complete technical packet
Ask for a current system diagram; model and provider inventory; model-version history; prompts and configuration; data pipelines; evaluation protocol; representative test set; baseline comparisons; error taxonomy; latency and throughput; uptime; cost per task; monitoring; and rollback procedure. Require a live or reproducible demonstration tied to a real customer workflow. A one-off demo establishes neither repeatability nor economics.
Run value-driving evaluations
- Freeze the model version, retrieval corpus, prompts, thresholds, temperature or sampling settings, and test-set hash.
- Use representative production-like data, including rare and edge cases; document exclusions and class balance.
- Compare with the incumbent workflow, a simpler baseline, and any claimed alternative model.
- Measure task success, accuracy where appropriate, calibration or uncertainty, latency, throughput, cost per task, and human-review rate.
- Classify errors and price false positives, false negatives, abstentions, and harmful outputs in customer terms.
- Repeat the test after distribution shifts, prompt injection attempts, malformed inputs, and degraded dependencies.
- Record the operator, date, environment, model version, prompts, thresholds, raw outputs, and remediation decision.
Ask what evidence would disprove the product thesis. If the answer is “nothing,” the evaluation is advocacy rather than diligence.
Assess dependency and portability risk
Map concentration in foundation-model providers, cloud regions, hardware, vector databases, data vendors, and specialized contractors. Review provider terms, rate limits, retention settings, price-change history supplied by the company, and termination rights. Identify fallback models, expected quality loss, migration time, re-training cost, and portability of prompts, embeddings, fine-tuning artifacts, and evaluation data. Model a provider withdrawal or a material price increase before accepting forecast margins.
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4. Establish data rights and provenance
Require an inventory covering training, fine-tuning, retrieval, evaluation, monitoring, and production data. For each dataset or model component, record source, owner, license or other legal basis, consent and notice, permitted uses, geographic origin, retention period, deletion process, and downstream restrictions. Match the contract language to the startup’s actual activities: training, inference, monitoring, product improvement, support, and sharing with subprocessors.
“Publicly available” does not mean unrestricted training rights. Ask how the company handles takedown requests, data-subject deletion, license termination, and contaminated or disputed data. Where rights are uncertain, quantify remediation: replacement data, re-training, customer notification, forensic review, legal fees, and possible product downtime. Make the investment conditional if the uncertainty could alter the business model.
5. Examine security, privacy, and operational resilience
Security evidence to request
- Threat models covering prompt injection, data exfiltration, model abuse, supply-chain compromise, and privileged-access paths.
- Penetration-test or red-team reports, scope and date, findings, severity, owners, and closure evidence.
- Vulnerability-management and patch records, dependency inventory, secrets-management design, and secure-development procedures.
- Identity and access controls, least-privilege reviews, encryption in transit and at rest, key management, logging, alerting, and audit trails.
- Backup, recovery, business-continuity, and disaster-recovery objectives with evidence of restore tests.
- Incident history, breach notices, customer security questionnaires, and the scope and date of any SOC 2, ISO 27001, or equivalent report.
Distinguish a certification or report from the controls actually relevant to the product. Read exceptions, subservice organizations, testing period, and management responses; a logo or expired report is not assurance.
Privacy and cross-border processing
Determine whether the product handles personal, health, financial, employment, biometric, children’s, or other sensitive data. Map controller and processor roles, subprocessors, retention and deletion, data-subject rights, breach response, international transfers, and customer-configurable controls. Obtain qualified counsel’s view for every operating and customer jurisdiction. A voluntary framework or internal policy does not itself establish legal compliance.
6. Verify responsible-AI governance and regulatory exposure
Look for operating controls, not slogans
Identify the person or committee accountable for AI risk and the escalation path for incidents. Request model cards or equivalent documentation, risk registers, release criteria, monitoring dashboards, complaint and incident logs, customer-notification procedures, remediation tracking, and explicit pause, withdrawal, or shutdown criteria. Ask for evidence that these controls operated in a real release or incident, not merely a policy template.
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Use the OECD six-step cycle
The OECD Due Diligence Guidance for Responsible AI, published on 19 February 2026, provides a practical governance spine:
- “Embed RBC into policies and management systems.”
- “Identify and assess actual and potential adverse impacts.”
- Cease, prevent, or mitigate adverse impacts.
- Track implementation and results.
- Communicate how impacts are addressed.
- Provide for or cooperate in remediation when harm occurs.
Test each step against an owner, record, metric, and escalation deadline. The cycle applies across an AI value chain, so ask how the startup manages provider, customer, and subcontractor dependencies.
Map each use case and jurisdiction
Have counsel analyze privacy, consumer-protection, product-liability, employment, sectoral, intellectual-property, and AI-specific requirements for each deployment. Pay particular attention to hiring, lending, insurance, healthcare, education, biometrics, safety-critical systems, and customer-facing decisions. Record the legal classification, required notices or human oversight, prohibited uses, audit duties, incident deadlines, and contractual allocation of responsibility. Treat regulatory exposure as a use-case map, not a generic checkbox.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.7. Check the team and execution capacity
Verify founder and key-employee identities, employment and contractor agreements, prior-company IP obligations, references, role coverage, hiring plan, and retention risk. Test whether the team can run model operations, data engineering, security, compliance, sales, implementation, and customer support without one founder or one vendor being an unreplaceable dependency.
Tie the roadmap to measurable milestones: customer outcomes, quality thresholds, gross-margin targets, reliability, security remediation, regulatory approvals where applicable, and hiring. Ask what the next financing depends on, which assumptions are most fragile, and what evidence would cause management to change course.
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8. Rebuild the financial model and financing downside
Reconcile historical results
Review financial statements, bank statements, general ledger, tax filings, accounts receivable, accounts payable, payroll, cloud bills, model-provider bills, burn, runway, and forecast assumptions. Trace a sample of revenue and expenses from contract to invoice to cash. Separate capitalized development, one-time implementation, founder expenses, and recurring operating costs.
Model failure cases
Build downside cases for slower sales, lower retention, higher inference prices, a security incident, legal remediation, customer-required controls, and loss of a key provider. Show cash runway, hiring changes, pricing response, and milestone slippage in each case. Ask whether the company can remain operational while replacing a model provider or a disputed dataset.
Match terms to evidence
Review valuation, liquidation preference, participation, pro rata, anti-dilution, option-pool treatment, information rights, conversion mechanics, debt, side letters, and use of proceeds. Compare the protection and dilution in the instrument with the verified risk profile. An attractive headline valuation does not offset a preference stack or control provision that changes the downside materially.
Red flags that should pause the process
- Refusal to provide primary records or insistence that a slide deck is sufficient.
- Accuracy figures without test-set provenance, baseline, model version, error bars, or a reproducible protocol.
- Training-data, model, API, or open-source rights that cannot be documented.
- Revenue that cannot be reconciled to contracts, invoices, usage, renewals, or cash.
- One-provider dependency with no fallback, portability plan, or price-sensitivity analysis.
- Missing IP assignments, unexplained cap-table entries, undisclosed side letters, or informal equity promises.
- No incident owner, monitoring, withdrawal criteria, or remediation record.
- Regulatory exposure treated as a legal checkbox rather than a use-case and jurisdiction map.
A red flag is not automatically a rejection; it is a condition requiring an owner, evidence, deadline, and quantified consequence. Refusal or repeated missed deadlines are themselves evidence about governance quality.
Compare two or more AI startups on the same evidence
Use one template and one scoring scale for every company. Score each category only after reviewing the underlying evidence, and keep a separate note for uncertainties so a confident presentation does not win by default.
| Category | Evidence to compare |
|---|---|
| Customer pain and willingness to pay | Buyer, measurable outcome, sales cycle, substitutes, contracts, and reference calls. |
| Verified traction and retention | Invoices, cash, cohorts, renewals, churn, usage, and revenue quality. |
| Technical performance | Representative tests, baselines, edge-case errors, calibration, latency, and uptime. |
| Unit economics | Inference, storage, human operations, support, provider fees, and resulting gross margin. |
| Defensibility and portability | Owned data and workflows, switching costs, fallback models, and migration effort. |
| Data and IP rights | Assignments, licenses, consent, retention, deletion, and commercial-use permissions. |
| Security and resilience | Threat model, testing, access control, incidents, backups, and recovery evidence. |
| Regulatory and governance readiness | Use-case map, accountable owner, monitoring, incident process, and remediation. |
| Team execution | Role coverage, references, operating depth, hiring plan, and dependency risk. |
| Terms and downside | Preference, dilution, control, runway, debt, and sensitivity to adverse scenarios. |
Write the recommendation as one of four outcomes: invest; invest only after specified remediations; defer pending evidence; or pass. For every open question, record the evidence owner, due date, materiality, and event that would change the recommendation. Conditions should be written into closing requirements or board reporting rather than left as informal assurances.
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