The right question is not whether the demo looks intelligent; it is whether a specific customer will repeatedly pay for a measurable result, whether the system works on that customer’s real cases, and whether the company can deliver it at acceptable risk and cost. Evaluate those questions in sequence, then price the investment and its protections. A polished model, a large market slide, or an “AI” label is not evidence of traction, technical reliability, defensibility, or attractive returns.
This process is designed for angel and seed investors reviewing an unidentified company. It tells you what to request, how to test it, what remains unproven, and how to separate technology diligence from the deal decision.
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What your diligence should establish
By the end of diligence, you should be able to answer six separate questions:
- Business: Who has the problem, how costly is it, and what measurable outcome changes?
- Product: Do representative users obtain that outcome consistently enough to keep using and paying?
- System: What data, models, people and controls make the result possible, and where does it fail?
- Dependencies: Can suppliers change prices, access or terms in a way that breaks the economics?
- Risk: Who could be harmed, how serious could the harm be, and does the company detect and mitigate it?
- Deal: Do valuation, dilution, rights and liquidity assumptions compensate you for the remaining uncertainty?
Keep evidence for each answer in a written diligence file. Mark every statement as verified, supported but incomplete, or unverified. A founder’s assertion belongs in the third category until contracts, records, tests or customer conversations support it.
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1. Clarify the use case and business case
Define the job before discussing the model
Ask the founders to describe one primary customer, the workflow being changed, the current alternative, and the point at which the product is used. “Companies need better automation” is not a use case. A useful description identifies the user, input, action, decision and measurable result—for example, reducing the time required to review a defined class of documents while keeping an agreed error rate.
- Who is the economic buyer, daily user and person accountable for the outcome?
- What does the customer do today: staff, rules-based software, an external service or nothing?
- What does the existing approach cost in money, time, delay, errors or missed revenue?
- Which metric changes after deployment, and over what period?
- Why is machine learning necessary instead of a simpler workflow, search or rules engine?
Request a product walkthrough using a normal customer workflow, not only a curated showcase. Ask the team to distinguish a prototype, a paid pilot and a production deployment. A pilot that never reaches recurring use is evidence of interest, not product-market fit.
Verify willingness to pay and repeated use
Request a customer list segmented by trial, pilot, paid production and renewed contract. For each reference, ask what problem triggered the purchase, who approved it, how often the product is used, what result was measured and what would cause cancellation. Where confidentiality permits, inspect invoices, contracts, usage logs and renewal records. A logo on a slide does not prove revenue, and a one-time payment does not prove a repeatable business.
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Test the economic promise
Translate the claimed outcome into a customer-level calculation. Identify implementation work, required human review, integration fees, support and any usage-based model charges. Then compare the customer’s total cost with the value of the claimed improvement. A product can produce impressive outputs and still destroy value if review labor or inference costs exceed the benefit.
Ask for the assumptions behind the forecast, the date each assumption was last updated and the sensitivity to lower usage, slower sales or higher model prices. Build a downside case in which the largest customer leaves, a pilot converts later than planned and usage doubles the expected compute bill.
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2. Inspect technical evidence, not headline benchmarks
Demand a task-specific evaluation
Ask for an evaluation built around the intended task and real operating context. Review how cases were sampled, who labeled them, what time period they cover, and whether they represent difficult, ordinary and adversarial inputs. A benchmark chosen because it flatters the product says little about performance on a customer’s data.
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| Evidence to request | Questions to answer |
|---|---|
| Test set and labels | Are examples representative, current, legally usable and independently checked for labeling errors? |
| Metrics | Which errors matter economically or legally? Are precision, recall, calibration, latency and abstention measured where relevant? |
| Baselines | How does the system compare with the customer’s current process and with a simple non-AI alternative? |
| Failure analysis | Which cases fail, how often, and can the system detect when it is uncertain? |
| Post-deployment evidence | Do results hold after updates, new customers, changing inputs and production load? |
The OECD’s responsible-AI guidance recommends examining testing, evaluation, verification and validation (TEVV), data quality and suitability, trustworthiness, human oversight and external expertise where appropriate. Read the OECD guidance and map each recommendation to a document or interview in your file.
Use the NIST framework as a checklist, not a pass/fail badge
NIST’s August 7, 2026 announcement describes the TEVV-Athlon Framework as a customizable four-stage method for assessing AI systems. It is a draft framework, not a universal score or certification. Use its staged logic to ask what is being tested, under which conditions, by whom and with what evidence; do not treat participation or a favorable result as proof that a startup is investable. See NIST’s TEVV-Athlon announcement.
Examine oversight, security and change management
For every consequential output, identify whether a person reviews it, what authority that person has and when the system must abstain. Ask for access controls, audit logs, incident response, privacy safeguards, retention settings and penetration-test or vulnerability-management records. Determine how a model, prompt, retrieval index or data pipeline is changed, who approves the change and how performance is re-tested afterward.
Request examples of incidents and near misses, including those that did not become public. A mature answer explains the failure, customer impact, root cause, corrective action and verification that the fix worked. “The model is monitored” is not sufficient without alert thresholds, owners and escalation times.
3. Map dependencies and defensibility
Build a data and model supply-chain map
List every material input: owned data, licensed data, customer-provided information, public sources, third-party foundation models, open-source packages, cloud infrastructure, inference APIs, labeling vendors and human reviewers. For each item, record the contract, permitted use, geographic limits, retention terms, audit rights, price formula and termination consequences.
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Public guidance cannot verify a startup’s licenses or supplier agreements. Inspect the actual records. Ask whether training and retrieval data may be used for model improvement, whether customers can demand deletion, and whether outputs can be transferred to another provider. Confirm that open-source components are tracked and that obligations such as attribution or source disclosure can be met.
Stress supplier and cost concentration
Model and cloud vendors can change price, rate limits, access policies or terms. Ask how quickly the company could substitute a provider, what engineering work that requires and whether quality would drop. Model gross margin at current usage, at two-times usage and after a plausible price increase. Include storage, monitoring, support and human review—not only token or API charges.
Identify single points of failure. If one vendor supplies the model, hosting and safety filter, an outage or policy change can halt service. Ask for service-level commitments, fallback modes, data export procedures and a tested recovery plan.
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Separate a moat from ordinary integration work
Ask what improves with customer use and what competitors could reproduce with the same public model and a small engineering team. Potential sources of defensibility include proprietary, permissioned data; workflow integration that is costly to replace; distribution; domain-specific evaluation assets; and operational know-how. Verify ownership, exclusivity and switching costs rather than accepting “our prompts” or “first-mover advantage” as a moat.
4. Review risk management and real-world impact
Identify affected people and plausible harms
Draw the system’s impact chain: whose data enters, who receives an output, who acts on it and who bears the consequence of an error. Consider privacy, discrimination, security, safety, misinformation, labor effects and access barriers. Severity and likelihood depend on context. An internal drafting assistant and a system influencing employment, credit, health or public services require different thresholds and controls.
Ask the company to document its rationale for adopting AI, the harms it considered, the controls selected and the evidence that controls work. Assign an owner for each mitigation and a date for review. Require a process for complaints, appeals, correction and deletion where people are affected.
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Use an improvement cycle
The OECD presents responsible-AI due diligence as an improvement-oriented process, not a guarantee of perfection. Its investor examples include adding adverse-impact risks to investment analysis, engaging stakeholders, discussing concerns directly with an investee, requesting additional information or action and coordinating with peers. The OECD framework and investor examples can structure these conversations.
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5. Verify claims and the offering context
Reconcile every public statement
Compare the pitch deck, website, demo, customer references, financial model and offering documents line by line. Investigate claims about growth, accuracy, savings, market size, partnerships, patents, regulatory approval and “guaranteed” outcomes. Ask what period, population and definition supports each number. Require a reasonable basis and contemporaneous records; do not accept a revised explanation after you challenge a claim.
The SEC, NASAA and FINRA investor alert warns about AI-themed fraud, unsupported or guaranteed-return claims, high-pressure tactics and promotion that crowds out substantive business information. Read the investor alert. Check the registration status of any investment professional or platform where applicable, and have qualified counsel review the actual offering and jurisdiction. The alert is staff guidance and has no legal force or effect; legal treatment depends on the transaction.
Demand company-specific risk disclosure
Generic language such as “AI may be inaccurate” is not enough. Ask the company to describe its particular operational, legal, competitive, privacy and supplier risks, their likelihood, and the controls in place. Gary Gensler said in prepared remarks on February 13, 2024: “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.” He also said, “Investor protection requires the humans who deploy a model to put in place appropriate guardrails.” These are prepared remarks, not binding rules, but they express the level of specificity an investor should seek. Read the SEC remarks.
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6. Evaluate the deal separately from the technology
Review the instrument and investor protections
Obtain the term sheet, governing documents, capitalization table, option pool details, prior financing terms and any side letters. Review:
- Valuation, price per share and the class or instrument you receive.
- Liquidation preference, participation, conversion, dividends and anti-dilution provisions.
- Current and fully diluted ownership, option-pool treatment and future dilution.
- Pro-rata or pre-emption rights, information rights, voting and board or observer rights.
- Transfer restrictions, redemption language and plausible liquidity or exit assumptions.
Model ownership after the proposed round, an additional financing and an expanded option pool. Compare the downside distribution under an ordinary sale, a low-value sale and a failure. A strong product does not repair terms that leave an early investor with little economic participation.
Test runway and use of funds
Reconcile bank statements or management accounts with the cash forecast. Identify monthly burn, committed costs, debt, outstanding payables, hiring plans and the date cash runs out under base and downside cases. Tie each proposed use of funds to a milestone: a production deployment, a retention target, a validated cost reduction or another observable result. Ask what happens if the milestone is missed and whether another financing is required sooner.
No valuation, return or investment conclusion can be drawn without the company’s actual price, cap table, financial history, documents and jurisdiction. Treat those as deal-specific questions, not as facts that can be inferred from sector enthusiasm.
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Compare companies on the same, use-case-specific axes and keep the evidence side by side. Weight each axis according to the consequences of failure; do not apply identical safety thresholds to low-risk internal software and a high-impact decision system.
| Comparison axis | Evidence to place in the comparison | Questions to ask |
|---|---|---|
| Customer urgency | Paid contracts, renewal history, deployment time | Is the problem budgeted and frequent enough to survive a priority change? |
| Use and retention | Usage by cohort, expansion, churn and concentration | Do users return without founder intervention? |
| Task quality | Representative evaluation, baseline and failure severity | What error rate is acceptable, and who reviews the rest? |
| Data and model rights | Licenses, permissions, supplier terms and portability | Can the company legally and operationally keep serving customers? |
| Cost to serve | Inference, storage, support and human-review costs | Does gross margin improve or deteriorate with scale? |
| Risk controls | Impact assessment, monitoring, incidents and remediation | Are safeguards owned, tested and updated? |
| Team execution | Relevant technical, product, sales and governance experience | Can this team ship, sell and respond to failures? |
| Terms and price | Valuation, dilution, preferences and information rights | Do protections match the technical and commercial uncertainty? |
A practical diligence work plan
- Before the first meeting: write a one-sentence use-case hypothesis and list the claims that would change your decision.
- After the product session: request the evaluation package, customer cohort data, architecture diagram, supplier list, security materials and complete financing documents.
- During customer calls: verify the buyer, use frequency, measurable result, implementation burden and renewal intent independently of the founder’s presence.
- During technical review: replay representative cases, inspect failures and ask an independent specialist to challenge the evaluation design when the use case is high impact.
- During legal and financial review: reconcile claims with contracts, invoices, bank records, cap table and offering terms; document every unresolved item.
- Before committing: write a decision memo with verified evidence, assumptions, downside cases, required conditions and the precise facts that would make you decline.
Red flags that should slow or stop a commitment
- A demo uses undisclosed cherry-picked inputs and the team will not show ordinary or failed cases.
- Accuracy is quoted without a task definition, baseline, test-set description or date.
- Customer logos cannot be tied to contracts, invoices, usage or a reference conversation.
- The company cannot explain data rights, model-provider terms or what happens after a supplier change.
- Human review is described as a slogan rather than a staffed, measured process.
- Material risks appear only as generic AI boilerplate, while promotions emphasize guaranteed outcomes or urgency.
- Runway depends on an optimistic financing date, uncommitted revenue or an untested margin assumption.
- The cap table, option pool, prior rights or liquidation terms are incomplete or inconsistent across documents.
How much weight to give market enthusiasm
AI investment has expanded sharply, but aggregate funding is not evidence of an individual company’s quality or your prospective return. OECD guidance citing an OECD data source reports global annual AI venture-capital value of about USD 6.4 billion in 2012 and USD 147 billion in 2024, with AI representing 56% of the value of all venture-capital investment by the third quarter of 2025. The figures are market aggregates attributed by the OECD to “OECD, n.d.”; they do not establish survival rates, valuations or returns for a particular startup.
Use market data to understand competition for capital and talent, not to replace customer evidence, system testing, contract review or deal analysis.
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