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An impressive AI demo is only the starting point. Before investing, test six separate propositions: a real customer problem, measurable product value, reliable performance in the intended context, lawful and durable access to data and models, controlled operational risk, and deal terms that compensate you for what remains uncertain.
The process below is designed for angel and seed investors evaluating a specific company. It is a diligence framework, not an investment recommendation or a substitute for legal, tax, accounting or technical advice.
What you are actually evaluating
Early-stage AI investing combines ordinary startup diligence with system-specific questions. A company can have excellent model output and still fail because customers will not pay, inference costs overwhelm revenue, a supplier changes its terms, or a harmful failure makes the product unusable. Conversely, a modest prototype can become valuable if it solves an urgent workflow, improves with proprietary data and has disciplined controls.
Separate the decision into six tests:
- Problem and customer: Is there an expensive, frequent problem for a clearly defined buyer?
- Value and adoption: Does the product measurably improve an outcome, and do users repeatedly use and pay for it?
- Technical evidence: Does the system perform on representative cases, including difficult and adversarial ones?
- Dependencies and defensibility: Can the company legally and economically access its data, models, infrastructure and distribution?
- Risk and governance: Can it identify, mitigate and monitor failures and adverse impacts?
- Security and economics: Do the company’s controls, runway, margins, valuation and investor protections support the risk you are taking?
Do not compress these tests into a single “AI score.” Weight them according to the use case. A low-risk internal drafting tool should not face the same failure tolerance as a system influencing credit, employment, health, safety or access to essential services.
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Start with the business case, not the model
Define the job being changed
Ask the founders to describe the customer, the user, the current workflow and the specific step the product changes. Require a before-and-after description that a nontechnical operator can understand. “Uses a large language model” is an implementation detail; “cuts the time to reconcile an invoice exception from two hours to ten minutes while preserving approval controls” is a testable value proposition.
Request the cost of the existing approach: employee time, outsourced fees, errors, delays, lost revenue or regulatory exposure. Then ask which measurable outcome the product promises to change and over what period. If the benefit cannot be measured, customer references and renewal evidence become especially important.
Distinguish prototype, pilot and production
Classify every deployment precisely:
- Prototype: a demonstration or limited experiment, usually without production reliability, contractual commitments or representative operating data.
- Pilot: a bounded customer trial with defined success criteria, an owner and a decision about continuation.
- Production: a live workflow with recurring use, support obligations, security controls and an economic relationship such as payment or a signed commitment.
Ask for the number of customers at each stage, the dates they started, the users invited, the users active, the workloads processed and the commercial status. A logo on a slide is not equivalent to a paying production customer.
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Request contracts, invoices, purchase orders or other records that demonstrate payment where disclosure is permitted. Examine conversion from pilot to paid use, renewal or expansion, the length of the sales cycle and who owns the budget. Interview customers independently of the founder-led demonstration. Ask what they would do if the product disappeared, what alternatives they considered and which promised outcome has actually been achieved.
Separate usage that is subsidized, bundled, grant-funded or performed by a friendly design partner from repeatable commercial demand. At seed stage, incomplete evidence is normal; the important point is to label what is proven, what is directional and what remains a hypothesis.
Inspect the AI system in its real context
Request a task-specific evaluation
Ask for an evaluation that mirrors intended use, not only a public benchmark. The package should identify the task, success metric, test population, sampling method, baseline, confidence intervals or error ranges where available, and the date and software version tested. You should be able to reproduce the meaning of “accuracy” from the document without relying on a marketing adjective.
Review how test cases were selected. Are they drawn from real operating conditions, different customer segments, languages, input formats and edge cases? Were duplicates, leakage and cherry-picked examples removed? Ask to see representative failures, not just the best outputs. A benchmark can be genuine and still irrelevant if it does not resemble the customer’s workflow.
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Use TEVV as a review discipline
Testing, evaluation, verification and validation (TEVV) should cover the complete system: model, prompts, retrieval, tools, user interface, human review and deployment environment. NIST announced the TEVV-Athlon Framework for Evaluating AI Systems on August 7, 2026. The announcement describes a customizable four-stage assessment method; it is a draft framework, not a universal pass/fail score. See NIST’s TEVV-Athlon description.
Use the framework as a way to structure questions, not as a certification shortcut. Ask what the company evaluates before release, during controlled rollout, after updates and when incidents occur. The OECD’s guidance likewise points investors toward context-specific TEVV information, data quality and suitability, trustworthiness, human oversight and external expertise where appropriate. See the OECD Due Diligence Guidance for Responsible AI.
Map failure modes and human oversight
Ask the team to list credible failures and rank their severity and likelihood. Examples include fabricated answers, missed detections, unsafe recommendations, prompt injection, data leakage, discriminatory error rates, denial of service and silent degradation after a model update.
For each material failure, identify the control:
- What input validation, retrieval constraint or policy prevents it?
- When does the system abstain or escalate?
- What must a human verify before an action is taken?
- How are reviewers trained, sampled and audited?
- Who can stop the system, and how quickly?
- Which logs, alerts and incident records show that the control works?
Human review is not automatically a safeguard. If reviewers face unrealistic volume, cannot see the model’s uncertainty or routinely approve outputs without checking them, the stated control may be nominal.
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Check post-release behavior
Request monitoring dashboards or sample reports covering quality, latency, uptime, cost, abuse, drift, complaints and override rates. Ask how the company detects a distribution shift, rolls back a model, updates evaluation sets and informs customers about material changes. Reliability after an update can matter more than a one-time laboratory score.
Trace data, models and supplier dependencies
Build a supply-chain map
Draw the path from input to customer output. Include customer-provided data, internally collected data, licensed datasets, public or scraped sources, third-party foundation models, fine-tuning artifacts, open-source packages, cloud infrastructure, vector databases, monitoring services and human contractors.
For every component, request the governing contract, policy or license and record:
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- What the company is permitted to use, for which purpose and in which territories.
- Whether customer content is retained, used for training or shared with another provider.
- Security, confidentiality, deletion and audit obligations.
- Usage limits, rate limits, indemnities, service-level commitments and termination rights.
- Whether the provider may change price, access, model behavior or terms without meaningful notice.
Public guidance cannot establish that an unidentified startup owns valid rights or has favorable contracts. Inspect the actual records and have counsel assess material restrictions.
Stress-test substitution and cost
Ask how the product behaves if its principal model or cloud vendor doubles prices, limits throughput, changes an API, discontinues a model or becomes unavailable in a target region. Request a migration plan, the engineering effort required to switch, and the expected temporary performance loss.
Model unit economics at realistic usage levels. Include inference, retrieval, storage, labeling, human review, support, security and compliance costs, not only the headline API price. Show gross-margin sensitivity to longer prompts, higher resolution inputs, peak demand and customer-specific configurations. A technical dependency becomes an investment risk when the company cannot pass through its cost or replace it quickly.
Identify what could become a moat
Ask what improves with customer use: proprietary feedback, workflow integration, verified labels, distribution, switching costs or domain-specific process knowledge. Then ask whether a well-funded competitor could reproduce the same result by calling the same public model with similar prompts and data.
Do not treat “our model” as proof of defensibility. Require evidence of exclusive or difficult-to-recreate data rights, embedded workflow, measurable learning effects or a distribution advantage. Record which elements are legally protected, contractually restricted, technically difficult or simply faster to execute.
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Make responsible-AI diligence an investment task
Identify affected people and potential harm
List everyone affected by an output, including people who are not the paying customer. Consider users, customers’ employees, applicants, patients, borrowers, students, bystanders, downstream recipients and communities. For each group, ask what could go wrong, how severe the impact would be, how likely it is and whether the person can detect, contest or recover from it.
Ask why the company chose AI rather than a simpler rule, software workflow or human process. The rationale should explain the expected benefit and acknowledge the new risks introduced by automation, scale or opacity.
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Review mitigation as a continuous process
The OECD presents responsible-AI due diligence as improvement-oriented rather than a guarantee of perfection. Practical investor actions include adding adverse-impact risks to the investment analysis, engaging relevant stakeholders, discussing concerns directly with the investee, requesting additional information or corrective action and coordinating with peers where useful. The examples are adaptable, not exhaustive; see the OECD guidance and investor examples.
Ask for named owners, deadlines, acceptance criteria and evidence that mitigations are monitored. An ethics statement without incident records, escalation paths or accountable staff does not demonstrate operational control.
Check security and privacy fundamentals
Request the architecture diagram, access model, encryption practices, secrets management, vulnerability process, backup and recovery plan, retention schedule and breach-notification procedure. Ask whether customer data is isolated between tenants and whether employees or vendors can access production content. For a young company, controls may be proportionate rather than mature, but gaps should be explicit, budgeted and assigned to an owner.
Verify claims, promotions and offering documents
Reconcile every important statement
Compare the pitch deck, live demonstration, product documentation, customer references, financial model and securities offering documents. Create a claim register with the statement, supporting evidence, date, owner and any qualification. Investigate differences such as “customers” meaning sign-ups, “accuracy” meaning a curated sample, or “recurring revenue” including one-time services.
Claims about growth, performance and prospects should have a reasonable basis. Material AI risks should be specific to the company’s operations, dependencies and customers rather than copied boilerplate. In prepared remarks before Yale Law School on February 13, 2024, then-SEC Chair Gary Gensler said: “When disclosing material risks about AI—and a company may face multiple risks, including operational, legal, and competitive—investors benefit from disclosures particularized to the company, not from boilerplate language.” The remarks are not binding rules, but they are a useful standard for questioning vague disclosures; read the SEC remarks.
Recognize promotion and fraud signals
The SEC, NASAA and FINRA investor alert warns about AI-themed fraud, unsupported or guaranteed-return claims, high-pressure tactics and promotional activity that displaces substantive business development. It also advises checking the registration status of investment professionals and platforms where applicable. Read the Investor Alert on Artificial Intelligence and Investment Fraud.
Those warnings do not mean every enthusiastic founder is dishonest. They mean urgency, celebrity endorsements, screenshots and guaranteed outcomes are not substitutes for verifiable records. Offering rules depend on the instrument, exemptions, investor status and jurisdiction; qualified counsel should review the actual documents.
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Evaluate the company’s financial engine
Understand revenue quality
Request monthly revenue, bookings, cash collections, deferred revenue, refunds, credits and services revenue. Reconcile the figures to bank statements or accounting records as appropriate. Separate recurring software revenue from pilots, integration work, usage spikes and founder-related transactions. Examine gross retention, net retention, churn, expansion, customer concentration, average contract duration and the share of revenue from the largest customer.
Model runway and financing risk
Review cash on hand, monthly burn, hiring plan, cloud and model costs, contractual commitments and the assumptions behind the next financing. Calculate runway under base, slower-sales and higher-inference-cost cases. Ask which milestones the round is meant to achieve, what spending is discretionary and what happens if the next round is delayed.
AI companies can show rapid usage while losing money on every request. Require a contribution-margin view that includes variable compute, data acquisition, review labor and support. A path to sustainable economics may be distant at seed stage, but the drivers and improvement plan should be intelligible.
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Terms to inspect
| Area | Questions for the documents |
|---|---|
| Instrument and price | Is this common or preferred equity, a convertible instrument or another security? What valuation, cap, discount, interest and conversion triggers apply? |
| Liquidation and conversion | What preference, participation, seniority, conversion ratio and treatment of future financings apply? |
| Dilution | How does the option pool, promised equity, warrants and later financing affect your ownership? |
| Investor rights | Are there pro-rata, information, inspection, consent or protective provisions? |
| Governance | Who appoints directors, controls votes and approves a sale or major borrowing? |
| Use of funds | Which hiring, infrastructure, compliance and sales milestones does the round finance? |
| Liquidity assumptions | What plausible exit, secondary or dividend paths exist, and what assumptions make them possible? |
Compare the terms with the evidence and risks you found. A high valuation does not become reasonable because the product is technically interesting, and favorable terms do not repair an unproven customer problem. No valuation or return conclusion can be drawn without the company’s cap table, price, financial history, documents and jurisdiction.
Compare two AI startups on the same axes
When choosing between opportunities, use a common worksheet but keep thresholds use-case-specific.
| Axis | Evidence to compare | Why it matters |
|---|---|---|
| Customer urgency | Problem frequency, budget owner, alternatives and references | Urgent problems support adoption and pricing. |
| Product use | Active users, workload frequency, retention and expansion | Repeated use is stronger than registrations. |
| Measured quality | Representative tests, baselines, failure severity and review burden | Headline benchmarks may not predict field performance. |
| Data and model rights | Licenses, restrictions, provenance and customer-data terms | Unclear rights can halt sales or create liability. |
| Supplier concentration | Primary providers, switching time and price sensitivity | Dependence can compress margins or interrupt service. |
| Economics | Contribution margin, support cost and margin trajectory | Usage growth is not value if each transaction loses money. |
| Customer concentration | Largest-account share, contract length and renewal evidence | One lost customer can change the company’s outlook. |
| Risk controls | Impact assessment, incident response, monitoring and accountability | High-impact systems need stronger safeguards. |
| Execution team | Domain expertise, shipping history, hiring gaps and governance | Early companies must learn and adapt quickly. |
| Price and protections | Valuation, dilution, preferences, information and pro-rata rights | Terms determine how risk and upside are shared. |
Weight the axes explicitly. For example, failure severity and human-review burden should dominate a system that influences a consequential decision, while latency and integration effort may matter more for a high-volume workflow tool.
Use market statistics carefully
OECD guidance reports that global annual AI venture-capital value rose from about USD 6.4 billion in 2012 to USD 147 billion in 2024, and that AI represented 56% of the value of all venture-capital investment by the third quarter of 2025. The page attributes these figures to “OECD, n.d.” and presents them as aggregate market data. They do not establish attractive returns, a sensible valuation or the quality of any individual startup. See the OECD source and its attribution.
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Red flags that should change your next action
- Demo-only evidence: The company cannot provide representative tests, failure examples or production references. Pause the investment decision and request a scoped evaluation.
- Guaranteed outcomes: Promotional material promises returns, accuracy or growth without conditions or records. Verify independently and consider ending contact.
- Unclear data rights: The team cannot identify licenses, customer permissions or deletion obligations. Require documentation and legal review.
- Single-provider fragility: A vendor supplies the model, data and infrastructure with no migration plan or cost sensitivity. Model a failure and financing requirement.
- Nominal human review: Reviewers lack authority, time, training or visibility into uncertainty. Treat the claimed safeguard as unproven.
- Concentrated revenue: One customer, reseller or grant accounts for most activity. Test renewal, replacement demand and cash timing.
- Pressure to wire quickly: Urgency replaces answers about the company, security or offering documents. Do not let a deadline waive diligence.
A practical diligence request list
- One-page use-case brief naming buyer, user, workflow, baseline cost and promised outcome.
- Customer and pilot list showing stage, start date, contract status, usage and renewal or conversion evidence.
- Evaluation plan, results, representative test set description, failure log and human-review procedure.
- Architecture and dependency diagram covering models, data, vendors, open-source components and deployment regions.
- Data-provenance, licensing, privacy, security, retention and incident-response documents.
- Risk or impact assessment with owners, mitigations, monitoring and escalation records.
- Monthly financials, cash position, burn, runway model, revenue reconciliation and customer concentration.
- Cap table, charter or equivalent governing documents, financing instrument and investor-rights summary.
- Claim register reconciling the pitch deck, demonstrations, customer evidence and offering materials.
- References for customers, former employees or technical experts who can address the claims independently.
Turn findings into an investment decision
Write a short decision memo before the final discussion. State the customer problem, the strongest evidence, the three most material uncertainties, the downside if each occurs, the specific evidence that would resolve it, the amount and terms offered, and the milestones you will monitor after closing.
Then choose one of three actions for each unresolved issue:
- Proceed: Evidence is sufficient for the risk and the terms compensate you for uncertainty.
- Condition: Invest only after a defined document, test, reference, contractual protection or milestone is delivered.
- Decline or defer: The missing evidence cannot be obtained, the downside is unacceptable or the deal terms leave no margin for error.
After investing, repeat the highest-value checks at agreed intervals: customer retention, contribution margin, model or vendor changes, material incidents, security findings, runway and financing plans. Investor protection is an ongoing control, not a one-time demo review. As Gary Gensler stated in his February 13, 2024 prepared remarks, “Investor protection requires the humans who deploy a model to put in place appropriate guardrails.”
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