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Before investing in a little-known AI company, verify the legal issuer, customer demand, product performance, data and intellectual-property rights, security, capitalization, and the exact investment terms. Treat demos, funding announcements, pilot counts, and claims of proprietary technology as leads to check—not proof. This guide focuses on a practical diligence process, with U.S. SEC filing examples; legal and regulatory obligations vary by jurisdiction, industry, product use, and offering structure.
Start by identifying the company and the investment
First establish which legal entity you are assessing and what security you would own. Similar names, a founder’s former company, a venture fund, a product brand, and the issuer can be different entities. The distinction matters: one company may sign customer contracts, another may own the software, and a third may issue the investment.
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Build an issuer map
- Record the issuer’s full legal name, jurisdiction of formation, subsidiaries, trading or product names, founders, and directors.
- Identify which entity issues the security, owns material intellectual property (IP), employs the team, signs customer contracts, and contracts with key suppliers.
- Write down the security being offered—such as shares, a SAFE, a convertible note, or another instrument—and obtain the documents that govern it.
- Ask management to describe the customer, problem, workflow, product, and precise role AI plays. Separate live features from pilots, roadmap items, and demonstrations.
For each important statement, ask for a dated source record. Mark what you have independently verified, what a second source corroborates, what management supplied, what you inferred, and what remains unresolved.
Check public records, but understand what they do and do not establish
For a U.S. issuer, search the SEC’s EDGAR database using its exact legal name and, if known, its Central Index Key (CIK). Review relevant Form D filings and amendments, then compare the issuer name, related persons, reported offering information, and filing chronology with the company’s own documents. Depending on the company and jurisdiction, also consider corporate registries, court records, patent records, procurement records, and relevant regulatory records.
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The SEC says a Form D notice is required for specified exempt offerings and is generally due within 15 calendar days after the first sale. For this filing purpose, the first sale is when the first investor is irrevocably contractually committed. A Form D is a notice, not SEC approval, an audited financial statement, a complete capitalization table, or a guarantee that an offering is legitimate. The SEC Division of Corporation Finance also states that its Form D FAQ reflects staff views and has no legal force or effect. An absence of public records is not proof that a company has no business, dispute, or legal obligation.
Verify that customers pay, use the product, and want to keep using it
Ask for a customer list divided into paid production deployments, paid pilots, unpaid pilots, and prospective customers. These categories represent different levels of evidence; a demo, waitlist, benchmark result, letter of intent, pilot, and recurring paid use should not be treated as interchangeable.
Speak with customers and former customers
With the company’s permission, contact a representative sample directly. Ask what the product does in the customer’s workflow, what it replaced, who approved the purchase, how frequently staff use it, what measurable outcome changed, and whether renewal or expansion is planned. Where possible, confirm the customer’s account against contracts, invoices, or other records rather than relying on a prepared testimonial.
Reconcile the revenue claim
Compare reported revenue with signed contracts, invoices, cash collections, credits, churn, and customer concentration. Review retention and expansion by customer cohort, implementation time, and the portion of backlog that converts into paid work. Distinguish recurring subscription revenue from one-time integration, consulting, or services revenue. If the company reports “customers” or “AI users,” ask how it defines the unit and the reporting period.
Evaluate product performance and the cost of delivering it
Arrange a product demonstration, but do not let a curated showcase stand in for an evaluation. Define representative tasks yourself, including edge cases and examples likely to expose errors. Compare results with the existing workflow or another conventional baseline. Do not describe tests as having been performed unless they actually were.
Request evidence about quality and operations
Ask how the company evaluates the system and request the underlying methodology and results. Depending on the use case, useful evidence can include error rates by task or user group, human-review burden, latency, uptime, and whether results replicate beyond a selected demo. Establish what happens when the system is uncertain, produces a harmful or materially incorrect output, or cannot complete a task.
Trace the stack and unit economics
Map the production dependencies: foundation models, cloud and accelerator providers, retrieval or data vendors, open-source components, and human support. Request the cost per completed customer task at observed usage and under stressed usage, along with gross margin after inference and support. Check capacity commitments, rate limits, exposure to supplier price changes, and contingency plans if a critical provider changes terms or withdraws access.
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One free scan finds every outdated or missing driver and matches the right update for your exact hardware.Free scan · exact hardware matchA product built on third-party models can still be a viable business; the relevant questions are how dependent it is, how that dependence affects costs and control, and what makes the overall product hard to replace. A proprietary model is not by itself proof of performance, defensibility, or commercial demand.
NIST’s AI Risk Management Framework (AI RMF) is a voluntary framework for considering trustworthiness in AI design, development, use, and evaluation. NIST lists AI RMF 1.0 as released in 2023, a generative AI profile as released in 2024, and says AI RMF 1.0 is being revised. It can provide a vocabulary for risk review, but it does not certify a product or company or establish investment merit.
Check data rights, intellectual property, and security
Follow the data and rights
Request a data inventory covering training, fine-tuning, evaluation, retrieval, and inference. For every material source, ask who collected it, what contractual or legal permission supports its use, what restrictions apply, whether it contains personal or confidential information, whether customers can opt out, and whether inputs or customer data are retained or used to train shared models.
Review the licenses and obligations for models, datasets, and third-party code. Check employee and contractor invention assignments, relevant patent and trademark claims, trade-secret controls, and any disputes or notices. A pitch-deck description of technology or data as “proprietary” is not a substitute for underlying rights records and appropriate legal review.
Assess security and responsibility for failures
Ask for the security architecture and evidence covering access controls, encryption, logging, incident response, and vulnerability management. Review customer security commitments and any independent audit or certification. Confirm the report’s date, scope, exceptions, covered systems, and the legal entity it covers. Ask about past incidents and remediation, and inspect contracts to understand who bears responsibility when the system produces a harmful or materially incorrect result.
Map governance, regulation, and other exposure
Identify where the product is offered and what consequential decisions it informs. With counsel familiar with those markets, determine which privacy, data-protection, consumer-protection, employment, health, financial, safety, export, or other sector-specific requirements may apply. Review litigation, complaints, regulatory inquiries, insurance, indemnities, and contractual restrictions.
Establish who is accountable for model changes, evaluation, incident escalation, customer-facing claims, and board oversight. A SEC Investor Advisory Committee Disclosure Subcommittee document dated November 18, 2025, was a draft for discussion at a December 4, 2025 meeting. It recommended that the SEC consider issuer definitions of AI, disclosure of board oversight, and separate discussion of material AI effects on internal operations and consumer-facing matters. It was a committee recommendation draft—not an adopted SEC rule or a legal requirement for a private startup. It can serve as a set of questions about materiality and governance, not as a compliance checklist.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.Reconcile ownership, capitalization, and the security terms
Obtain the current fully diluted capitalization table and reconcile it against the stock ledger, charter, board approvals, and prior financing records. Include options, warrants, SAFEs, convertible notes, debt, liens, and promised equity. Look for side letters, liquidation preferences, anti-dilution provisions, conversion caps or discounts, information and voting rights, transfer restrictions, and follow-on obligations.
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Read the actual subscription, stock-purchase, SAFE, note, or other security documents. Model ownership and proceeds under multiple financing and exit outcomes, including dilution and downside scenarios. Compare valuations only after comparing the securities’ rights and the assumptions behind each valuation. Qualified legal and tax advisers can review the documents and your eligibility and jurisdiction. A public filing does not mean the SEC has checked the investment terms for you.
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Make the decision evidence-based
Write a short decision memo that separates evidence from belief. For each conclusion, label it as independently verified, corroborated, management-provided, inferred, or unresolved. Cover the central investment thesis, customer proof, product evidence, unit economics, defensibility, key dependencies, governance and legal exposure, capitalization, terms, and downside case. Name the specific evidence that would change your view and the conditions that must be met before funding—for example, direct customer verification, documented rights, security remediation, or clarified financing terms.
If you are comparing opportunities, use the same evidence standard for each. A single “AI moat” score can conceal tradeoffs that deserve separate scrutiny.
| Comparison area | Questions to answer |
|---|---|
| Demand | How urgent is the customer problem, who pays, and is paid use renewing or expanding? |
| Product | How does performance compare with the current workflow, and what implementation or review burden remains? |
| Economics | What are gross margins after inference and support, and how sensitive are costs to compute and supplier terms? |
| Defensibility | What rights, distribution, retention, or operational advantages make the product difficult to replace? |
| Risk and resilience | How concentrated are suppliers and customers, and what governance, regulatory, security, and runway risks matter? |
| Investment | What valuation, dilution, downside exposure, and security rights apply to the instrument being offered? |
This is a diligence framework, not a recommendation to invest. No general checklist can establish that a particular company or offering is sound; that conclusion depends on the issuer, evidence, instrument, and investor’s circumstances.
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