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There is no universal AI scorecard. Evidence thresholds vary with the fund’s investment thesis, the startup’s stage, sector and geography, and the type of AI product. A pre-product company may be judged mainly on insight, technical feasibility and customer discovery; a later-stage company can be tested against production reliability, renewals, margins and a longer operating record. The frameworks described by CRV and PwC are useful lenses, not rules every firm applies identically.
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The investment questions behind AI due diligence
Most investment committees are trying to answer four linked questions:
- Is there a painful, valuable problem in a market the company can actually reach?
- Does the product produce a measurable outcome that customers will keep paying for?
- Can the team build, sell and operate it despite technical, legal and competitive risks?
- Can the business grow into venture-scale returns without costs rising as fast as usage?
AI adds a second layer to each question. Investors may inspect model behavior, evaluation methods, data rights, infrastructure dependencies, security controls, human oversight and inference costs. Commercial diligence connects those findings to positioning, adoption, pricing and likely financial outcomes, as described by Deloitte Canada.
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1. Team and ability to execute
Relevant insight, not a prescribed founder profile
Investors assess whether the founders understand the customer’s workflow, can make sound technical decisions and can deliver against the company’s plan. That may come from domain experience, engineering depth, research expertise, commercial knowledge or a combination. It is an assessment of execution capacity and insight—not proof that one résumé pattern predicts success.
They will typically map responsibilities: who owns product decisions, model or data work, security, sales and operations; which capabilities are missing; and what hiring is required for the next milestone. They also test whether the founders can explain trade-offs clearly when a model fails, a customer changes requirements or a supplier alters an API.
Evidence that makes the assessment concrete
- A clear division of founder and employee responsibilities.
- Milestones for the next financing or product stage, with dependencies and dates.
- Demonstrated ability to recruit or retain the specialists the plan requires.
- Examples of customer discovery, technical delivery and decisions made under constraints.
Team assessment appears in the Allied Venture Partners investor framework, while PwC’s diligence description asks whether management can execute and whether the organization can support AI capabilities.
2. Market, problem and distribution
Is the problem urgent enough?
Venture investors test whether the product addresses a costly, frequent or strategically important problem rather than offering an interesting capability without a budget owner. A pitch deck’s total-addressable-market figure is an assumption to investigate. Diligence links the proposed market to the specific buyer, workflow, procurement process, regulation and competitive alternatives.
Can the company reach buyers?
Distribution is part of market size. Investors examine the route to customers—direct sales, channel partners, embedded distribution, a developer ecosystem or another repeatable motion—and how long adoption takes. They look for evidence that the person who experiences the problem can influence or approve a purchase, and that implementation requirements do not make every sale a bespoke consulting project.
CRV’s criteria and Deloitte Canada’s framework both connect market claims to product positioning, customer evidence and financial outcomes. Neither establishes a universal market-size threshold.
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3. Product value and customer evidence
From impressive demo to useful workflow
A demonstration shows that a system can produce an output under selected conditions. Investors want to know whether it improves a real workflow: does it save time, reduce errors, increase revenue, lower risk or enable work that was previously uneconomic? The answer should be expressed in an acceptance criterion a customer can verify.
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Adoption quality matters more than a logo list
Diligence separates pilots and experiments from repeatable production use. Useful evidence can include active usage, deployment scope, paid contracts, expansion, renewals, user references and measured outcomes. A pilot may be valuable at an early stage, but it should not be presented as equivalent to recurring production revenue.
There is no source-backed universal traction number for AI startups. The appropriate evidence depends on stage, sales cycle and product type. A company selling into regulated enterprises may have fewer customers but longer, deeper deployments; a developer product may show broad usage before it has large contracts. Investors test whether the evidence is durable and repeatable rather than applying one headline metric.
Questions investors ask about product behavior
- What happens when the model is uncertain, wrong or given an unfamiliar input?
- How are quality thresholds defined for each important use case?
- What human review, fallback or escalation path protects the customer?
- Can the company observe usage and demonstrate improvement after release?
4. Technical quality, data and legal foundations
Architecture and dependencies
Technical diligence can inspect the application architecture, model or platform dependencies, data pipelines, deployment environments, monitoring and roadmap assumptions. Investors want to understand which components are controlled by the startup, which are supplied by third parties and what would happen if a provider changed price, limits, terms or model behavior.
Kesho Partners’ technical diligence guide specifically identifies architecture, model dependency, data, evaluation, security, scalability, the technical team and intellectual-property or legal concerns. PwC describes reviewing AI maturity, talent, technology, governance, data foundations, architecture and roadmap realism.
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Founders should be able to identify where training, fine-tuning, retrieval and evaluation data came from; what permissions apply; how retention works; and whether contracts restrict training, deployment or onward use. “We have a lot of data” is not enough if the company cannot show lawful access or a right to use it for the claimed purpose.
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Evaluation, reliability and security
Investors may ask for evaluation sets, baseline comparisons, error analysis and known failure modes. The relevant baseline could be a previous workflow, a human process or an incumbent tool, but it must be defined for the use case. They also examine access controls, isolation of customer data, incident response, auditability and safeguards against misuse. A single favorable demo cannot establish reliability across inputs or customers.
Scalability and roadmap realism
Technical scale includes latency, throughput, storage, model limits, observability and operational staffing. The roadmap should show which constraints are solved now, which require capital and which depend on unproven research. Investors connect those assumptions to hiring, infrastructure spend and delivery dates.
5. Defensibility and the risk of commoditization
What could become harder to copy?
Potential advantages include differentiated technology, lawful access to distinctive data, deep integration into important workflows, distribution, switching costs and feedback or learning loops. Investors ask how each advantage compounds with adoption and why a well-funded competitor could not reproduce it quickly.
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Using a commercial foundation model, fine-tuning a public model or labeling a dataset a “data moat” does not by itself establish durable advantage. The company must show a specific asset, process or network effect, the legal right to use it and a credible path for it to improve as customers arrive. Defensibility is assessed alongside distribution and economics, not as a slogan separate from them.
6. Business model, operating costs and scale
Unit economics for AI workloads
AI products can incur direct inference, hosting, storage, monitoring or customer-specific training costs each time a customer uses them. Investors therefore model cost to serve by workload, customer or transaction instead of importing generic software margins. They test how gross margin changes with longer prompts, higher-resolution inputs, more frequent requests, human review and service-level commitments.
Pricing and margin path
A credible model explains who pays, what unit is priced, how usage is metered and what happens when a customer’s demand grows. Founders should show pricing assumptions, current cost to serve, sensitivity to model and infrastructure prices, and the operational changes required to improve efficiency. Growth that increases revenue but loses more money on every use is not automatically attractive.
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Operational scalability
Scaling also means onboarding, support, quality assurance, security reviews and compliance. Investors ask whether each new customer requires a large implementation team or whether the product becomes more repeatable. PwC’s framework ties technology and governance diligence to the organization’s ability to support AI capabilities at scale.
7. Fund fit, stage, financing terms and valuation
Fit with the fund’s strategy
An attractive company can still be outside a fund’s mandate. Investors compare the opportunity with their stage focus, sector thesis, ownership target, reserve strategy and expected return profile. They also consider whether the fund can provide useful networks, recruiting help or follow-on capital.
Stage changes the evidence available
Early companies may have stronger evidence in customer discovery, prototypes, technical feasibility and founder insight than in renewals or mature margins. Later companies can be examined against production reliability, a larger operating record, retention, expansion and cost trends. Asking a pre-launch startup for later-stage metrics can obscure the real risk; accepting a late-stage company’s prototype evidence without operating data creates the opposite problem.
Terms and valuation are case-specific
Investors evaluate the proposed price and financing terms against dilution, ownership, future capital needs and the return the fund needs from its portfolio. The available frameworks do not establish a universal AI valuation multiple, scorecard or premium. Any valuation claim must specify its dataset, geography, stage and year; a single number presented without those qualifications is not a reliable benchmark.
The Allied Venture Partners framework and CRV’s criteria both emphasize fit and investment assumptions while treating the criteria as contextual rather than universal.
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A founder can make the process faster and more credible by organizing evidence around the questions investors will test.
- Document the customer problem. Name the buyer, workflow, current alternative, urgency and measurable outcome. Separate the market you can reach from a broad theoretical market.
- Show product performance in context. Provide a live workflow, acceptance criteria, baseline comparison, evaluation results and known failure modes. Explain when a human reviews or overrides the system.
- Separate pilot from production. For every customer, state whether the deployment is an experiment, paid pilot, production contract, renewal or expansion; include scope and duration.
- Map the technical stack. Diagram application services, models, data stores, vendors, deployment environments, monitoring and scale limits. Note what changes if a dependency’s price, terms or availability changes.
- Prove data and IP rights. List sources, permissions, retention terms and restrictions on training, retrieval and deployment. Identify open issues rather than hiding them.
- Model economics by workload. Calculate cost to serve for representative customers and usage levels. Show pricing, gross-margin sensitivity and the efficiency improvements required for scale.
- Explain the compounding advantage. Identify the specific technology, data access, workflow integration, distribution or switching cost that competitors cannot readily reproduce, and describe how adoption strengthens it.
- Connect the team to milestones. Assign owners, identify hiring gaps and state the resources needed to reach the next stage. Tie the funding request to those milestones rather than to an undifferentiated expense budget.
This preparation list synthesizes the diligence areas described by PwC, CRV and Kesho Partners; it is not a formal checklist used by every VC.
Comparing AI startups consistently
When an investor compares opportunities, the same evidence should be examined across each company. The weighting changes with stage and thesis; the axes below are not a numerical universal score.
| Axis | Questions to answer | Evidence to compare |
|---|---|---|
| Customer urgency and market access | Is the problem important, budgeted and reachable? | Buyer interviews, sales cycle, market assumptions and distribution route |
| Product performance and adoption | Does the system improve a workflow reliably? | Evaluation results, baseline, usage, deployment scope, paid contracts and renewals |
| Data quality and rights | Can the company lawfully use data for the claimed purpose? | Provenance, permissions, retention terms and restrictions |
| Model and infrastructure dependence | What happens if a supplier changes price, limits or behavior? | Dependency map, fallback options, latency, throughput and operating limits |
| Defensibility and distribution | What compounds and why is it difficult to copy or bypass? | Distinctive technology or data, workflow integration, switching costs and channel evidence |
| Cost to serve and margin path | Does growth improve or damage unit economics? | Inference, hosting, training and support costs by workload; pricing and sensitivity analysis |
| Execution against milestones | Can this team deliver the next stage? | Responsibilities, hiring plan, delivery record, roadmap assumptions and required resources |
These comparison axes synthesize the commercial and technical frameworks from PwC, Deloitte Canada and CRV. A fund may weight them differently: reliability can dominate in a safety-critical product, while distribution or speed of learning may matter more in another category.
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Common diligence problems and how to address them
A demo is treated as product-market proof
Why it fails: selected inputs and a guided script do not show repeatability. Better evidence: publish evaluation criteria, representative results, failure cases and production usage separately.
A large market number hides a narrow buyer
Why it fails: a theoretical market does not show reachable demand. Better evidence: connect the target segment to a named buyer, sales process, alternatives and customer references.
“Proprietary data” has no rights record
Why it fails: unclear provenance can create legal and commercial risk. Better evidence: maintain a source-and-permission inventory, including retention and training restrictions.
Model dependence is ignored in the financial plan
Why it fails: supplier pricing, limits or behavior can change cost and quality. Better evidence: show dependency exposure, fallback plans and sensitivity to usage and provider prices.
Revenue growth is shown without cost-to-serve
Why it fails: high-usage customers may be unprofitable. Better evidence: report workload-level costs, pricing assumptions and a concrete efficiency roadmap.
What the available evidence does—and does not—establish
The cited investor, advisory and institutional frameworks consistently support combining ordinary company diligence with AI-specific examination of technology, data, governance, reliability and economics. They do not support a universal pass/fail score, a required traction number, a standard valuation premium or a single founder archetype. The OECD’s 2026 report on venture-capital investment in AI through 2025 provides market context and explains its classification methodology, but it does not establish an individual startup’s valuation or investment prospects.
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