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You can investigate a private AI company without audited public financial statements, but you cannot replace missing facts with a single database score. Build a dated evidence trail, label every number as reported, estimated, or inferred, and test the company’s model, rights, customers, costs, and valuation assumptions separately. The result should be a confidence-bounded view of what is known, what is modeled, and what still requires documents from the company.
Why private-company research starts with incomplete evidence
In the United States, a private company generally does not have the recurring Securities and Exchange Commission reporting trail required of a public issuer. Exceptions can include a private company disclosed in a public-company acquisition or a business that previously traded publicly. The Library of Congress summarizes the practical consequence: “Unlike public companies, private companies are not required to file with the Securities and Exchange Commission (SEC), so the type of information and the depth of information that can be found in those documents is not necessarily going to be available for private companies.” See the Library of Congress guide to U.S. private-company research.
That limitation changes the question from “Where is the company’s annual report?” to “Which independent pieces of evidence can support each claim?” A financing announcement can establish that a round was announced; it does not establish revenue, gross margin, or profitability. An employee count can suggest operating scale; it cannot prove sales. A customer logo can show a relationship was publicized; it does not reveal contract value, renewal rate, or whether the customer is paying.
Because no universal disclosure rule covers every jurisdiction, start by recording the company’s legal entity, trading names, parent and subsidiaries, country and state of registration, and an as-of date. A result without that identity work can accidentally combine figures from similarly named entities or an acquired predecessor.
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Define the decision before collecting information
The evidence you need depends on the decision you are making. Write the purpose in one sentence and set a cutoff date before opening sources.
| Decision | Evidence that matters most | Common mistake |
|---|---|---|
| Company identification | Legal name, registration, ownership links, products and locations | Assuming a brand name is a single legal entity |
| Competitive research | Product capability, target users, distribution, partnerships and hiring | Treating marketing language as proof of customer adoption |
| Investment screening | Financing terms, revenue quality, burn, runway, concentration, rights and governance | Using a headline valuation as if it were current equity value |
| Acquisition diligence | Contracts, IP ownership, liabilities, data rights, security controls and normalized cash flow | Relying on a vendor profile instead of primary documents |
| Credit analysis | Cash balance, debt, repayment priority, recurring receipts and liens | Counting bookings or announced funding as available cash |
| Journalism or public-interest reporting | Attributable records, dates, corroboration and a clear distinction between claim and fact | Publishing an estimate without its methodology or date |
Define whether you need enterprise value, total equity value, or the value of a particular share class. Also state the geography, currency, period and valuation date. Those choices prevent a U.S. dollar estimate for a global group from being mistaken for a local subsidiary’s results.
Use an evidence hierarchy and preserve provenance
Put each statement into an evidence class rather than blending everything into one narrative.
- Company-reported: a signed filing, investor letter, contract extract, audited statement or dated company announcement. Record exactly what the company said and whether an independent party confirmed it.
- Independently documented: a government record, court docket, procurement award, public-company acquisition filing or a customer’s own announcement.
- Modeled estimate: a database or analyst output constructed from multiple inputs. The value may be useful for screening but is not a disclosure.
- Inference: your interpretation of several signals, such as concluding that a hiring slowdown may indicate a shift in cash management. Inferences must remain labeled as such.
Online material can be stale or wrong, so save the publication date, access date and the exact wording. The Library of Congress recommends using the company’s own website while recognizing that online information may be outdated or incorrect: https://guides.loc.gov/company-research/private.
Build a dated evidence ledger
A spreadsheet or database is enough. Give every claim one row and keep contradictory entries instead of averaging them away.
| Ledger field | What to record |
|---|---|
| Claim | The precise statement, such as “annual recurring revenue was $X for the year ended Y.” |
| Value and unit | Currency, thousands or millions, percentage, employee count or other unit. |
| Period and date | Measurement period, publication date and your as-of date. |
| Source and type | Filing, contract, company page, court record, customer statement, database or analyst model. |
| Status | Reported, estimated or inferred. |
| Geography and entity | Country, subsidiary, parent and the legal entity to which the number applies. |
| Corroboration | Independent sources that agree, disagree or cover a different period. |
| Confidence and limitation | Why the claim is strong or weak and what document would resolve the uncertainty. |
Do not average two vendor estimates merely because they are different. First check whether they define revenue, subsidiaries, fiscal periods and currency in the same way. A numerical average can create false precision when the underlying definitions do not match.
Step-by-step workflow for a private AI company
1. Establish the legal and corporate identity
- Copy the legal name from an authoritative record and list every trading name, former name, parent, subsidiary and acquisition.
- Record the jurisdiction, formation status and registered dates. State registry access and filing detail vary by state, so describe exactly what a record proves.
- Set the research cutoff date and currency. Mark facts that fall outside the period instead of silently mixing them with current information.
A registration or status record confirms that an entity exists in a jurisdiction; it is not a financial statement and normally does not disclose the economics you are seeking.
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2. Map the company’s own claims
Review product pages, customer stories, leadership biographies, hiring pages, technical documentation and dated announcements. Capture whether each statement is a company assertion, a customer quotation or an independently documented event. This material is useful for mapping products, target users, claimed use cases and executives, but promotional language alone does not establish financial performance.
Separate “serves 200 enterprises” from “has 200 paying enterprise customers.” Ask whether “customers” means trials, pilots, active accounts, historic accounts or contracted accounts. Preserve the original wording so a later reader can see what was and was not claimed.
3. Search public and quasi-public records
For a U.S. target, check SEC EDGAR for a former public-company filing, a public-company acquisition disclosure or an applicable exempt-offering notice. Check the relevant state corporation registry, then look for litigation, liens or UCC records, government awards and contracts, and bankruptcy records where those records are accessible. The Library of Congress explains the private-company exceptions and public-record limits at https://guides.loc.gov/company-research/private.
Interpret each record narrowly. An exempt-offering notice can document a securities filing, not the company’s revenue. A lawsuit can reveal an allegation or a disputed contract, not a final finding. A procurement award can show an authorized amount, not necessarily cash received or recognized revenue.
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4. Triangulate operating scale with several proxies
Use independent indicators to test whether the company’s claimed scale is plausible:
- Disclosed financing, security type, closing date and stated use of proceeds.
- Employee count, role mix and hiring trend, with the source date and whether contractors are included.
- Office, laboratory, data-center or cloud-computing footprint.
- Customer and partner announcements, procurement awards and renewal statements.
- Product usage disclosures, application downloads, API traffic or other operating measures when definitions are clear.
- Litigation, liens, transaction activity and changes in corporate control.
Each proxy has a hard limit. Headcount does not establish revenue; a logo does not establish contract size or recurring revenue; and announced funding is financing, not operating income. Use several dated indicators and write what each one can and cannot show. PrivCo describes employee fundamentals, operating footprint, industry benchmarks, filings, transactions, hiring and digital presence as inputs to its estimates; its methodology is available at https://www.privco.com/methodology.
5. Use commercial databases as leads and models
Paid databases can improve entity matching, financing-history searches and access to structured datasets. They do not turn an undisclosed number into an audited fact. For every provider, check:
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- Source notes and update cadence.
- Geographic and stage coverage.
- Definition of revenue, EBITDA, growth and valuation.
- Treatment of subsidiaries, acquisitions, currency and fiscal years.
- Whether a value is disclosed, modeled or inferred.
- How financing terms and security classes affect the displayed price.
PrivCo says its revenue, EBITDA, growth and valuation figures are constructed from multiple inputs, including fundamentals, benchmarks, hard-data events, comparables and growth signals, with anomaly and inconsistency checks. Those are the provider’s stated methods, not independent verification of a particular company profile.
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Separate reported figures from estimates and inferences
Use a three-column presentation whenever you publish or circulate numbers. The distinction is more important than the number of decimal places.
| Label | Meaning | How to write it |
|---|---|---|
| Reported | The company or an authoritative record states the figure for a defined period. | “The company reported $X of revenue for the year ended Y.” Include the document date and scope. |
| Estimated | A provider or analyst models the figure from partial inputs. | “Provider Z estimates revenue at $X; its methodology uses A, B and C.” |
| Inferred | You derive a conclusion from multiple signals. | “Hiring and disclosed contracts are consistent with expansion, but they do not establish revenue.” |
Never call a database estimate “the company’s revenue.” If two estimates disagree, explain the definition or date difference and retain both values with their provenance.
AI-specific diligence: rights, data and governance
An AI company can appear asset-light while depending on fragile rights to data, models, code and cloud capacity. Financial diligence should therefore be paired with a rights and governance review.
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- Identify who created or licensed each model, including base models, fine-tunes, retrieval systems and evaluation code.
- Confirm that employees and contractors assigned inventions, code and model-related work to the company, with no conflicting prior-employer obligations.
- Check open-source licenses, model licenses, restrictions on commercial use and obligations to provide notices or source code.
Training-data provenance and permission
List each material data source, its owner, license, geography, retention period and permitted purpose. Check whether the license covers training, fine-tuning, evaluation, customer-facing outputs and future model versions. Investigate sensitive personal data, deletion requests, provenance records and whether the company can obtain comparable high-quality data if a source disappears.
Debevoise & Plimpton states: “A private equity sponsor evaluating a target company with a proprietary AI model should identify the sources of training data and confirm that the target company has all necessary rights to use the training data as such datasets are used and intended to be used by the company in its model.” The discussion appears in its Spring 2024 Private Equity Report Quarterly: https://www.debevoise.com/-/media/files/insights/publications/2024/05/private-equity-report-spring-2024.pdf?hash=1A031EA669293B0F9F31672E0E3A9463&rev=99ce6ad94f48425eac00cf4604d033be.
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Customer inputs, outputs and third-party models
- Read every model-vendor term governing whether customer prompts, files or feedback are retained, used for training or reviewed by humans.
- Determine who owns generated outputs, whether the provider can reuse them and how confidential information is segregated.
- Check portability: can the company export prompts, embeddings, fine-tunes and evaluation data if a vendor changes price or policy?
- Map service-level commitments, outage history, rate limits and the cost of inference at current and projected volumes.
Privacy, security and accountability
Request data-flow diagrams, access controls, retention schedules, incident history, security assessments and deletion procedures. Test how the company handles bias, explainability, human review, model updates, policy changes and customer complaints. These issues affect durability and liability, but they do not substitute for evidence about customers, costs or cash generation.
Value the company with explicit assumptions
No valuation method removes uncertainty. CFA Institute groups private-company valuation into income, market and asset approaches and discusses normalization, discount rates, company size, limited public-market access and control or marketability adjustments: https://www.cfainstitute.org/insights/professional-learning/refresher-readings/2026/private-company-valuation.
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|---|---|---|---|
| Income | Discounts forecast cash flows or earnings to a present value. | Revenue growth, gross margin, operating costs, reinvestment, terminal value and discount rate. | Forecasts are immature, customer concentration is high or cash flows are negative and unstable. |
| Market | Applies multiples from comparable companies or transactions. | Comparable set, metric definition, date, growth, margins, size, geography, liquidity and security terms. | AI peers have different business models, data rights, capital intensity or stages. |
| Asset-based | Estimates assets less liabilities, adjusted to relevant values. | Value of cash, equipment, IP, data rights, obligations and contingent liabilities. | Most value lies in uncertain future growth, workforce know-how or unproven IP. |
Normalize unusual founder compensation, one-time cloud credits, acquisition costs and related-party transactions only when you can document the adjustment. State whether your result is enterprise value, equity value or a particular class of shares, and show dilution from options, warrants, convertibles and future financing.
How to interpret a prior financing price
The Financial Conduct Authority describes firms using the latest transaction price for venture investments before revenue generation and multiples after companies achieve revenue generation. It also reports difficulty identifying comparables and judgment in applying discounts, premiums, cash-flow forecasts and discount rates: https://www.fca.org.uk/publications/multi-firm-reviews/private-market-valuation-practices.
A round price is therefore a dated signal tied to a security class and transaction terms. Record the closing date, preferred or common shares, liquidation preferences, participation rights, conversion features, secondary or primary nature, investor rights and any unusual circumstances. A preferred share price cannot automatically be multiplied by all outstanding shares to produce a common-equity value. Reconcile the round with later milestones, new financing, customer results, market conditions and dilution.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.Compare valuation outputs without creating false precision
When methods or data products disagree, compare their assumptions rather than selecting the largest or smallest headline number.
- Purpose and standard of value: investment, acquisition, financial reporting, tax or a hypothetical market transaction.
- Subject: enterprise value, total equity, common stock or a specific preferred class.
- Valuation date: whether the input predates a financing, product launch, lawsuit or market change.
- Input status: disclosed, modeled or inferred.
- Comparable relevance: business model, stage, geography, growth, margins, data ownership and capital intensity.
- Terms and adjustments: control, marketability, liquidation preference, dilution and debt.
- Sensitivity: how much the result changes when growth, margins, terminal value or discount rate changes.
Present a range with the drivers of the range, not a spurious point estimate. If a small change in the discount rate or retention assumption moves value materially, that sensitivity is itself a central finding.
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Common failure modes and how to repair them
Calling a funding round revenue
Why it fails: funding is capital raised and may be debt, preferred equity or a convertible instrument. Repair: record the amount, instrument, closing date and stated use of proceeds separately from operating income.
Treating headcount as a revenue proxy
Why it fails: staffing varies with research intensity, outsourcing, geography and capital strategy. Repair: use headcount only as one dated operating signal and seek contracts, invoices, retention and cash records for financial conclusions.
Repeating an undated database number
Why it fails: the value may be modeled, stale or attached to a different entity. Repair: capture methodology, update date, definition, geography and source notes; label it as an estimate.
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Assuming a customer logo proves a durable contract
Why it fails: pilots, free trials, historical relationships and reseller arrangements can all produce public logos. Repair: request contract term, renewal, annualized value, implementation status and permission to use the logo.
Ignoring AI rights until after valuation
Why it fails: missing training-data permission, weak IP assignment or vendor lock-in can impair the product and create liabilities. Repair: run the rights, privacy, security and vendor review in parallel with financial analysis.
Presenting a prior round as today’s market value
Why it fails: terms, dilution, performance and market conditions change. Repair: anchor the price to its date and class, then reconcile it with current evidence and sensitivity analysis.
A practical diligence checklist
- Identity: legal entity, aliases, parent, subsidiaries, jurisdiction and cutoff date.
- Evidence ledger: claim, value, period, source, status, geography, corroboration and limitation.
- Corporate records: formation, status, prior public filings, acquisition disclosures, litigation, liens, contracts and insolvency records where available.
- Operations: employee composition, hiring, infrastructure, customers, partners, procurement and usage indicators.
- Financial quality: revenue definition, recurring versus one-time income, concentration, gross margin, burn, runway, debt and dilution.
- AI rights: model ownership, employee assignments, training-data licenses, output rights, customer-input handling and vendor portability.
- Governance: privacy, security, bias testing, human oversight, update controls, incident response and policy-change procedures.
- Valuation: purpose, date, subject, method, comparables, adjustments, financing terms and sensitivity range.
- Open items: documents requested, unresolved contradictions, responsible party and deadline.
The finished report should let a reader distinguish what the company has actually disclosed, what a provider has modeled and what an analyst has inferred. That separation is the defensible substitute for a nonexistent public financial trail.
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