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1Fix the driver behind crashes, sound loss and screen glitches2Clear out junk files and repair common Windows errors3Scan for outdated or missing drivers - takes under a minuteEvaluate a private AI company as both a software business and a high-dependency technology operation. Verify what it sells, how revenue becomes cash, how much margins depend on compute and model suppliers, whether its data and intellectual property can support the claims, and whether management has controls suitable for public reporting. Then examine AI governance, cybersecurity, capitalization and offering terms. A draft Form S-1 is a useful map when one exists, but it is not a substitute for company records, contracts or current legal advice.
Start by defining what “before going public” means
This question has two audiences. A prospective investor may be deciding whether to buy shares in a private financing or secondary transaction. Management may be preparing for an initial public offering (IPO). The same evidence is useful to both, but the decision is different: an investor is testing risk and value, while management is testing whether its business, controls and disclosures can withstand public scrutiny.
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The framework below is U.S.-focused. The Securities and Exchange Commission (SEC) says a Form S-1 registration statement describes the issuer’s operations, financial condition, results of operations, risk factors and management, and includes audited financial statements. The SEC also says an issuer must provide other information needed to keep required disclosures from being misleading. Its June 21, 2024 explainer is a disclosure guide, not an assessment of any particular AI company.
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Build a plain-language description of the business
Before looking at valuation, write a description that a technically literate non-specialist can understand. It should identify the product, the paying buyer, the end user, the problem solved and the event that causes a customer to pay.
Separate the revenue engines
Classify each source of revenue rather than calling everything “AI revenue.” Potential categories include:
- Access to a model through an API or hosted interface.
- Software subscriptions for an application or workflow.
- Usage-based charges tied to requests, tokens, seats, transactions or output volume.
- Implementation, integration, consulting or other services.
- Hardware sales or hardware-linked deployments.
- Licensing of software, models, data or intellectual property.
Ask management to distinguish revenue already earned under the accounting rules from pilots, bookings, backlog, usage commitments, letters of intent and non-recurring work. A large pipeline is not the same as recognized revenue or collected cash.
Describe the production use case
For every material product, ask what the system does in production, what task it enables and what a customer would lose by removing it. Identify the decision or workflow affected by an output. A demonstration can show capability; it does not establish repeatable customer value.
Record how customers buy and renew. Determine whether contracts are annual, monthly, consumption-based, cancellable, tied to minimum commitments or dependent on professional services. The answer affects revenue predictability, working capital and the cost of supporting each account.
Test the “AI” claim precisely
Determine whether the company owns a foundation model, fine-tunes a model supplied by another party, builds an application on external models, or combines several approaches. Map which components are proprietary, licensed, open-source or supplied by customers. A proprietary model is not automatically a durable advantage, and an application using an external model is not automatically weak; the question is what the company controls and what customers value.
Ask how performance is evaluated, how often people review outputs, what errors are unacceptable and why customers renew or expand. Require the task definition, evaluation data, scoring method and production observations rather than accepting a single benchmark number or a demo.
Reconstruct revenue quality and cash economics
Reported growth is only the starting point. Reconcile revenue with collections, contract terms, renewals, usage variability, discounts, customer concentration and the cost of implementation and support.
Request the evidence behind each revenue line
- Revenue by product, geography, customer type and contract structure.
- Customer cohorts showing renewals, expansions, cancellations and downgrades.
- The share of revenue from the largest customers and the terms that govern it.
- Deferred revenue, receivables aging, refunds, credits and payment disputes.
- Bookings, backlog and remaining performance obligations, clearly separated from recognized revenue.
- Discounts, free usage, credits and concessions granted to win or retain accounts.
- Implementation and support effort required before a contract becomes economically attractive.
Ask which revenue is recurring, which varies with usage and which is one-time. A usage-based business may grow quickly while its revenue and cash flow fluctuate with customer workloads. A subscription business may appear predictable while depending on expensive onboarding or unusually high renewal discounts.
Attribute the real cost of AI workloads
Request cost attribution for inference, training, data acquisition, cloud services, specialized compute commitments and customer-specific customization. Compare gross margin and cash requirements across products, customer cohorts and workloads when the company’s records permit it.
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Do not apply a universal “AI margin” benchmark. The available SEC materials provide no standard margin or burn-rate target for AI companies. The useful question is whether the company can explain its own margin movement and the assumptions behind future improvement.
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Analyze liquidity and runway
Read the financial statements and management’s discussion and analysis (MD&A) together. SEC staff observations on smaller-company IPO filings emphasize cash flows, liquidity, material trends, changes in line items, revenue recognition and whether historical performance is a useful guide to future performance. Those observations are from a page modified in 2009; use them as prompts, not as current legal thresholds.
Calculate a downside cash case using the company’s actual cash balance, committed spending and plausible changes in collections, renewals, compute prices and customer usage. A simple runway calculation—available cash divided by monthly net cash burn—can be a starting point, but it is only as reliable as the assumptions. Ask what happens if a major customer delays payment, a model provider raises prices, a cloud commitment cannot be reduced or a product launch slips.
Identify contractual commitments for cloud capacity, data, equipment, leases, debt and minimum purchases. Determine which commitments are cancellable, which require deposits and which could accelerate in a downturn. Liquidity analysis should include those obligations rather than treating the cash balance as freely available.
Read financial statements, accounting policies and MD&A together
Audited financial statements are necessary but not sufficient. Trace material changes between periods to contracts, pricing, customer mix, model costs, headcount, acquisitions and accounting judgments.
Revenue recognition
Ask management to explain when each type of arrangement becomes earned revenue, how variable usage is estimated, how services and software are separated, and how refunds, credits and termination rights are handled. Confirm that investor presentations use definitions consistent with the financial statements.
Non-GAAP and promotional measures
For every adjusted metric, identify the reconciliation to the audited numbers, the excluded costs and whether the measure changes from period to period. Treat annual recurring revenue, bookings, active users and similar indicators as useful only after their definitions, period and calculation are documented.
Material trends and uncertainties
Look for explanations of declining gross margin, rising receivables, increasing customer concentration, unusual deferred revenue, higher capitalized development, restructuring, warranty exposure or changes in supplier terms. A narrative that reports the result without explaining the driver is an invitation to ask for more evidence.
Map the technology stack and dependency risk
Draw the path from data acquisition through training or fine-tuning, inference, application logic, hosting and delivery to the customer. At each layer, record ownership, license rights, service-level commitments, pricing exposure and the practical alternative if access is lost.
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Review contracts for usage restrictions, data rights, indemnities, audit rights, termination provisions, rate changes, service levels and restrictions on competing products. Ask whether the company can move to another model or cloud provider without retraining, redesigning the application or violating customer promises.
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Data and intellectual property
For training, fine-tuning and retrieval data, document the source, permission, geographic scope, retention period and permitted uses. Determine whether customer information can enter training, whether deletion requests can be honored and whether the company can prove provenance for material datasets.
Compare public claims about model ownership, customer relationships, performance and competitive advantage with employment agreements, licenses, technical records and customer contracts. Confirm that employees and contractors assigned intellectual property to the company and that open-source components comply with their licenses.
Operational resilience
Ask how the service behaves when a model endpoint is unavailable, latency rises, a data source changes or a provider deprecates a model. Review fallback architecture, capacity planning, backup and restore tests, incident communications and customer remedies. A dependency is material when its failure could interrupt revenue, breach a contract or force an expensive redesign.
Demand evidence for performance and customer value
Do not treat a benchmark, pilot or showcase customer as proof of durable commercial advantage. Request the measurement task, dataset provenance, evaluation design, test-set controls, independent replication where available, production error rates, latency and cost under realistic workloads.
Connect model metrics to business outcomes
For each claimed improvement, ask what changed for the customer: fewer manual hours, faster processing, higher conversion, lower losses or another measurable result. Establish the baseline, observation period, sample size and role of human review. If the company cannot connect a metric to a customer outcome, classify it as a technical indicator rather than evidence of economic value.
Look for evaluation drift
Record when a model, prompt, retrieval index, safety filter or data source changes. Determine whether the company reruns evaluations after changes and whether customers are notified when behavior, latency or cost may change. Production performance can diverge from a controlled test as user inputs and data distributions evolve.
No independent benchmark or named AI-company technical study is established by the available materials. Therefore, do not infer a league table, market share or performance ranking from this framework.
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Evaluate AI governance, data controls and accountability
Identify the executives and board members responsible for AI development and deployment. Document who approves a material model or data change, who monitors outputs, who escalates incidents and who has authority to stop a deployment.
Board oversight
On December 4, 2025, the SEC Investor Advisory Committee approved a recommendation that issuers disclose whether the board or a board committee oversees aspects of AI deployment. This is an advisory recommendation, not a Commission rule. Ask whether the company has assigned oversight, how often the board receives reports and what information those reports contain.
Use a risk-based control inventory
The committee recommendation cites the National Institute of Standards and Technology’s definition of AI as a machine-based system that, for human-defined objectives, makes predictions, recommendations or decisions influencing real or virtual environments. A risk-based approach can organize diligence around behavior, evaluation and controls without turning guidance into a legal requirement.
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For each material system, document:
- The intended use, prohibited use and affected people or processes.
- Training, fine-tuning and retrieval data, including provenance and access controls.
- Known failure modes, testing results, human review and escalation paths.
- Change management for models, prompts, tools, data and safety filters.
- Monitoring for drift, bias, security abuse, hallucination and performance degradation.
- Retention, deletion and customer-notification rules.
Check whether contracts promise capabilities or safeguards that the operating controls cannot deliver. A mismatch between sales language and technical reality is a disclosure, litigation and renewal risk.
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Test cybersecurity and incident disclosure
Inventory sensitive data, critical systems, outsourced functions and interruption points. Include model endpoints, training repositories, source-code systems, identity providers, customer integrations and operational technology where relevant.
Review incidents and near misses
Request incident logs, response timelines, customer impact assessments, remediation records, insurance notices and post-incident testing. Include near misses that exposed a weakness even if no reportable loss occurred. Determine whether an incident affected revenue, expenses, contractual obligations, availability or the reliability of financial reporting.
Reject generic risk language
SEC cybersecurity guidance says registrants should evaluate prior incidents, severity and frequency, likelihood, potential consequences and preventive actions, and explain how material cyber risks affect the issuer. It warns that risk factors should not be generic. The guidance is an older staff document, so confirm the current SEC rules, forms and issuer-specific requirements for a live offering.
“Registrants should not present risks that could apply to any issuer or any offering and should avoid generic risk factor disclosure.”
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Ask whether the company can explain who or what was affected, how operations changed, what was spent, what remains unresolved and why the controls now reduce the risk. Do not claim that a company has suffered an incident unless its records or public filings establish it.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.Assess management, reporting controls and IPO readiness
Review leadership experience against the company’s current scale and complexity. Identify gaps in finance, security, legal, compliance, sales operations, model evaluation and customer support. A strong technical founder does not by itself demonstrate readiness for audited reporting or public-company obligations.
Financial and disclosure controls
Trace how sales contracts reach accounting, how usage is measured, how costs are allocated, how estimates are approved and how management reviews unusual transactions. Check whether investor presentations, board materials, customer claims and filed disclosures use consistent definitions and numbers.
Related parties and prior financing
Review related-party arrangements, founder or executive transactions, loans, side letters, consulting agreements and unusual supplier or customer relationships. Examine prior unregistered securities sales, option grants, convertible instruments and any unresolved accounting or legal questions. SEC staff observations identify these as recurring review areas, but they do not establish current thresholds or requirements.
Management incentives
Understand compensation triggers, performance targets, vesting, change-of-control terms and incentives tied to an IPO date or valuation. Incentives do not prove misconduct, but they help explain which assumptions management may emphasize and which risks deserve independent verification.
Understand the cap table and the security you would own
A company valuation is not the same as the value of a particular share class. Obtain the fully diluted capitalization table and reconcile it to legal agreements.
- Preferred and common shares outstanding.
- Options, warrants and their exercise prices.
- Convertible notes, SAFEs or other instruments that may convert.
- Liquidation preferences, participation rights and anti-dilution provisions.
- Voting rights, protective provisions and board appointment rights.
- Secondary sales, transfer restrictions and lockups.
- Employee equity reserved but not yet granted.
Ask how a new financing, conversion or IPO would change ownership and voting power. Confirm whether a secondary buyer receives the same rights as the original investor. Current offering terms and issuer documents are required to analyze dilution or fair value; there is no universal multiple or valuation method supported by this framework.
Compare private AI companies on consistent evidence
When evaluating more than one candidate, use the same questions and definitions. Compare:
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- Unit economics: gross margin and cash sensitivity to inference, training, data, support and compute commitments.
- Defensibility: intellectual-property ownership, data rights, distribution, switching costs and evidence that customers receive durable value.
- Dependencies: exposure to model, cloud, data and infrastructure providers, including practical alternatives.
- Production reliability: evaluation quality, error handling, latency, monitoring, security and incident response.
- Governance and leadership: board oversight, accountable executives, reporting controls and consistency of disclosures.
- Terms and liquidity: valuation, dilution, preferences, lockups, voting rights and the credible path to a public or private exit.
For every comparison, preserve the definition, period, source document and assumptions. State when two companies measure a similar concept differently. Do not rank companies from promotional metrics alone, and do not create a numeric score unless the evidence and weighting are transparent and decision-relevant.
Use a disciplined diligence sequence
A repeatable sequence reduces the chance that an impressive demo hides a weak financial or control foundation.
- Write the business map. Identify products, buyers, users, revenue streams, contracts and the production task.
- Request primary records. Obtain financial statements, MD&A, revenue schedules, customer cohorts, material contracts, supplier terms, technical evaluations, security records, governance materials and the cap table.
- Reconcile claims. Tie investor presentations and sales materials to accounting records, contracts and production measurements.
- Model downside cases. Stress collections, renewals, usage, inference costs, cloud prices, provider availability and hiring needs.
- Interview independent functions. Ask finance, engineering, security, sales, legal and customer-success leaders the same core questions and investigate differences.
- Map disclosure exposure. Identify facts that could materially affect operations, finances, customers, security or the offering, and test whether the proposed explanation is specific and balanced.
- Document open items. Assign an owner, required evidence and decision consequence to each unresolved issue. Do not convert an unanswered question into an assumption.
Warning signs that require deeper work
- Revenue growth is presented without collections, retention, concentration or contract detail.
- Bookings, pilots or usage commitments are described as if they were earned revenue.
- Gross margin improves only when customer-specific labor or compute costs are excluded.
- A performance claim lacks a task definition, data provenance, baseline or production measurement.
- The company cannot explain what happens if a major model, cloud or data provider changes terms.
- Customer data rights, training permissions or open-source obligations are unclear.
- AI oversight is assigned informally, with no accountable executive, board reporting or change approval.
- Cybersecurity risk factors are boilerplate or omit the operational and financial consequences of known events.
- Investor materials use definitions that do not match the financial statements or customer contracts.
- The cap table omits convertibles, options, warrants, preferences or secondary-transfer restrictions.
- Management refuses reasonable evidence requests while making precise claims about growth, margins or readiness.
None of these signs alone proves that an investment is unsound. They indicate where the evidence burden is highest and where a decision should remain conditional.
Questions to put to management
Business and customers
- Who pays, who uses the product and what measurable problem does it solve?
- Which revenue is recurring, usage-based, service-based or non-recurring?
- What causes renewal, expansion, cancellation or a reduction in usage?
- How concentrated are revenue, receivables and dependency on individual customers?
Technology and data
- Which model, application, data and infrastructure layers are owned, licensed, open-source or supplied by customers?
- What evidence supports production accuracy, latency, cost and reliability?
- What rights permit training, fine-tuning, retrieval and retention of each material dataset?
- How quickly can the system operate if a provider is unavailable or changes price or terms?
Finance and liquidity
- What are the main drivers of gross margin and cash burn?
- What commitments exist for cloud, compute, data, equipment, leases and debt?
- Which assumptions determine runway under a downside case?
- How are usage, credits, refunds, implementation and support treated in revenue recognition?
Governance and security
- Who can approve, monitor, pause and investigate a material AI deployment?
- How are model, prompt, data and safety changes tested and communicated?
- What incidents and near misses occurred, what did they cost and what remains unresolved?
- How does the board or a board committee receive information about AI and cybersecurity risk?
Offering and ownership
- What are the rights, preferences and restrictions attached to each security?
- How will options, warrants, convertibles and a new financing affect dilution?
- What lockups, transfer limits or liquidity conditions apply after an IPO?
- Which related-party transactions or prior securities issuances require special explanation?
What this process can and cannot tell you
This framework can reveal whether the company’s commercial story is supported by contracts and cash, whether AI costs and dependencies are understood, and whether governance, security and reporting controls are developing in proportion to the business.
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For a live transaction, verify the current SEC rules and forms, the issuer’s jurisdiction and offering structure, and obtain advice from qualified securities, accounting, tax and technical professionals. Treat a draft S-1 as a disclosure map and test every material claim against primary records.
The decision standard
A private AI company is ready for serious pre-IPO consideration only when its revenue and cash story survives reconciliation, its technical advantage is demonstrated in production, its critical dependencies and data rights are documented, its AI and cybersecurity controls have accountable owners, and its capitalization and offering terms are understandable. If any of those foundations remains unverified, the correct conclusion is not a precise valuation or an IPO prediction; it is a clearly documented list of conditions that must be resolved.
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