As a private equity or venture capital firm, deciding whether a startup’s technology can support the growth plans behind your investment is difficult. Many of the most important risks aren’t visible in a pitch deck or product demonstration.
What matters is what the startup’s artificial intelligence (AI) can do compared with what’s claimed, who owns the technology and data behind it, and what it costs to run as usage grows These aren’t always easy to verify without specialist AI engineering expertise, making a leading technical due diligence service for AI startups essential.
However, knowing which service provider to trust can be just as challenging. To help you understand what’s involved and how choose the right partner, this article covers:
- What Technical Due Diligence on an AI Company Covers
- Where AI Startup Claims And Technical Reality Often Don’t Match
- Key Due Diligence Deliverables and Timeframes
- How To Choose a Technical Due Diligence Provider
- How Neurons Lab Helps PE Firms With Technical Due Diligence
- FAQs
Need to conduct technical due diligence on an AI startup before investing? Neurons Lab can help. Book a call with us today.
What Technical Due Diligence on an AI Company Covers
Technical due diligence helps you establish what an AI startup has actually built, whether its claims hold up under testing, and what it would take to scale the business after investment. This includes identifying technical strengths, weaknesses, and risks.
This matters because early-stage AI companies often build quickly, only to discover later that their technology requires substantial additional work to deliver what was promised. For an investor, that work can mean unplanned follow-on funding and slower returns.
While traditional software due diligence focuses on code quality, infrastructure, and security, AI technical due diligence goes further. It looks at a startup’s technology stack, AI capabilities, operating model, technical leadership, and ability to develop, maintain, and extend its systems.
The main areas covered AI due diligence typically range from architecture to business feasibility, which we summarize below:
Table summarizing what technical due diligence for AI covers
| Area | What’s typically examined | Why it matters |
|---|---|---|
| AI architecture and technology stack | AI models, harnesses, software architecture, integrations, technical debt, and third-party dependencies | Establishes what the company has built, how its systems work, and whether the technology supports its business model. |
| AI models and performance | Model selection, testing methods, accuracy, reliability, and performance under different conditions. | Verifies whether the AI can consistently deliver the results the company claims. |
| Data architecture and ownership | Data sources, quality, storage, access rights, licensing, and data management practices. | Identifies issues that could affect AI performance, intellectual property rights, or legal use of data. |
| Machine learning operations (MLOps) and production readiness | Model deployment, testing, monitoring, version control, updates, and incident management. | Determines whether the company can operate and maintain its AI systems reliably beyond initial demonstrations. |
| Scalability and integrations | Infrastructure capacity, system performance, integration requirements, and constraints on future growth. | Assesses whether the technology can support increasing demand without major performance issues or unexpected costs. |
| Security, governance, and compliance | Security controls, access permissions, audit trails, human oversight, and applicable regulatory requirements. | Identifies potential security, operational, and compliance risks that could affect the business. |
| Team and leadership capabilities | Engineering expertise, technical leadership, team structure, development practices, and staffing requirements. | Establishes whether the company has the people and skills to maintain its technology and support future growth. |
| Unit economics and business feasibility | AI infrastructure costs, model usage, automation rates, human intervention, and cost per task. | Tests whether the technology can deliver the expected financial benefits and support the company's projected margins. |
Where AI Startup Claims and Technical Reality Often Don’t Match
Claims about an AI startup’s technical capabilities, technology ownership, and ability to scale can mean different things in practice, and a deal’s valuation, staffing and margin assumptions often rest on them.
One risk to watch for is AI washing, which occurs when a company overstates its AI capabilities or misrepresents what it has built. For example, a startup might claim to have developed its own foundation model when it relies on a third-party API, or describe a partly manual workflow as fully automated. Agent washing is the same issue applied to AI agents, such as presenting a basic chatbot as an autonomous AI agent.
These misrepresentations aren’t always deliberate. They can also come from untested assumptions, incomplete information, or different interpretations of what the technology can deliver.
Here are some common areas where claims and reality may differ:
Proprietary AI Versus Third-party Models and Harnesses
A startup might describe its AI system as proprietary, even though it relies on an existing foundation model, such as Claude or GPT, with a custom layer of code around it, known as an AI harness.
A harness connects the model to tools, business data, workflows, and controls, creating value without requiring the company to develop its own model. So relying on a third-party model isn’t a problem in itself.
It becomes a problem when a valuation assumes the company owns the technology, but the core capability depends on a third-party provider whose pricing, terms, and models the startup doesn’t control. It can also be a concern if competitors could reproduce the product using the same model. That’s why investors need to know which parts of the technology the startup built and which it rents.
Claimed Data and IP Ownership Versus Actual Rights and Restrictions
A startup may say it owns its data and technology. But parts of them may actually come from third-party providers, licensed datasets, or customer data it has no right to use beyond its original purpose, such as for training models.
Data rights can also restrict expansion into regions with different data privacy and residency requirements. Code written by contractors belongs to the startup only if the contracts assign ownership rights to the company. If the startup doesn’t clearly own or have permission to use its data and code, it could face restrictions on commercial use, costly licensing changes, or legal disputes.
Automation and Headcount Claims Versus Actual Work
A startup may claim substantial automation and reduced staffing benefits when AI only handles individual tasks rather than complete workflows.
For example, an AI system might extract information from documents, but teams still have to verify the results, correct errors, and transfer the information into other systems.
Technical due diligence checks the share of workflows AI completes end-to-end without human intervention, which tasks still require manual work, and the cost of human review. As a result, the startup may need more staff than projected.
Successful Demos Versus Performance at Scale
An AI system might perform well in a controlled environment with a small number of users and carefully prepared data.
However, as the system begins processing real customer information, handling higher transaction volumes, and supporting more complex workflows, it may encounter performance and reliability problems.
A product that works for early adopters might require significant infrastructure changes, additional engineering, and stronger operational controls before supporting more customers or wider expansion. And these requirements can increase development costs, delay customer onboarding, and push back expected revenue growth.
AI Cost Savings Versus Actual Unit Economics
A startup might estimate AI-driven savings based on how quickly its system completes a task, without accounting for the full cost of operating it. These costs can include model API usage, cloud infrastructure, accessing relevant data, storage, monitoring, and repeating failed tasks.
Without calculating metrics such as cost per completed task and inference costs (the cost of running an AI model each time it processes a request), it’s difficult to establish whether the technology can deliver the expected financial benefits as it scales.
If these costs are higher than projected, the startup may struggle to achieve the gross margins assumed in the investment case.
Key Red Flags to Look For
Use these to test whether a startup’s claims hold up in conversation with its founders:
| Red flag | What it looks like | What to ask for |
|---|---|---|
| Unclear AI architecture | Founders can’t explain which parts of their technology are proprietary and which rely on third-party providers. | A system architecture diagram and a list of third-party models and services. |
| No verified AI performance results | The startup can’t provide testing records or evaluation results to support its claims about accuracy and reliability. | Model evaluation reports, test results, and performance benchmarks. |
| Missing data and IP documentation | The startup lacks records confirming its rights to critical technology, data, or contractor-developed code. | Data licensing agreements, IP ownership records, and contractor agreements. |
| Limited evidence beyond demonstrations | The startup can’t demonstrate how its AI performs under expected workloads or real customer conditions. | Production usage records, performance logs, or load-testing results. |
| No monitoring or recovery procedures | The team lacks clear processes for detecting AI failures, tracking system changes, or restoring previous versions. | Monitoring records, version control history, and documented recovery procedures. |
| Reliance on a single engineer | One engineer holds most of the knowledge needed to operate and maintain critical systems. | Engineering documentation, system ownership records, and evidence of backup technical support. |
| Unverified automation claims | The startup can’t show how many workflows AI completes without human intervention or which tasks remain manual. | Workflow documentation, automation metrics, and human-review records. |
| No clear breakdown of AI operating costs | Management can’t account for model usage, infrastructure, repeated failed tasks, and human review when estimating service costs. | A breakdown of AI operating costs, including cost per completed task or customer workflow. |
Key Due Diligence Deliverables and Timeframes
Technical due diligence typically results in a written report covering a target startup’s technology, AI capabilities, and potential risks. While the level of detail and format vary depending on the provider and scope of the review, common deliverables include:
- Technology and engineering review: An overview of the startup’s AI architecture, codebase, technology stack, third-party dependencies, technical debt, and the engineering team’s capabilities.
- AI model and production review: Findings on model selection, performance testing, reliability, MLOps practices, monitoring, and readiness for live operations.
- Data, IP rights, and security review: An assessment of data quality, ownership, licensing, access controls, governance practices, and potential security or compliance concerns.
- Scalability and cost assessment: Findings on infrastructure capacity, integration requirements, automation rates, model usage, and the cost of running AI workflows as demand increases.
- Technical risk summary: A record of identified weaknesses, their severity, and the estimated effort required to address them.
Providers typically start by agreeing on the scope, reviewing the technology, meeting with the startup’s engineers, and presenting their findings.
As an investor, you’ll need to coordinate access to the startup’s codebase, technical documentation, performance records, and relevant engineering teams. At the end of the assessment, you should also receive a report you can take to your investment committee.
For complex AI systems, providers may quote around two to three weeks for a detailed technical due diligence assessment1. However, timelines can vary depending on the scope of the review, the startup’s technical complexity, and access to its systems and engineering team.
How to Choose a Technical Due Diligence Provider
Choosing the right technical due diligence provider for AI claims is important because the final report provides evidence to support your investment decision. Here’s what to look for in a provider:
- AI engineering and production experience: Look for providers with proven experience building, deploying, and maintaining AI systems used by real customers. This helps them determine whether a startup’s architecture is technically feasible and what it would take to scale beyond a prototype. Ask which AI systems the team has built and run in production and who would work on this review.
- Relevant target industry experience: Providers who understand a startup’s customers and operating environment can better identify technical constraints affecting deployment, customer adoption, and commercial viability. Ask for examples of previous work with similar companies and how the provider handled their specific technical or regulatory requirements.
- Hands-on technical assessment: Choose providers who go beyond reviewing documentation to examine the startup’s codebase, production logs, and performance results, and speak directly with its engineers. This helps uncover potential AI washing by testing whether claims about AI capabilities, proprietary technology, and automation are supported by evidence. Ask what technical access the provider requires, which tests are performed, and how startup claims are verified.
- Clear findings tied to investment decisions: Before signing, confirm the report format, that findings are ranked by priority, and that the report is suitable for presentation to your investment committee. Request a sample report to see how findings and recommendations are presented.
- Independent assessment: Choose a provider whose findings aren’t influenced by the opportunity to sell additional services. The ability to help address technical issues after investment is a useful bonus, but shouldn’t determine your choice of provider. Ask how the provider keeps its findings separate from any post-deal proposals, so the report reflects the startup’s actual technical risks rather than opportunities for follow-on work.
How Neurons Lab Helps PE and VC Firms with Technical Due Diligence
Neurons Lab offers a two-week, hands-on technical due diligence engagement to help private equity and venture capital investors assess the technical feasibility of AI startups, verify their claims, and identify risks before investing.
As an AI enablement partner serving organizations across the US, Europe, and Asia, Neurons Lab combines executive training, AI adoption programs, and custom AI agent builds to support secure, practical deployment. Through our end-to-end delivery model, clients build operational AI capabilities aligned with their workflows, governance requirements, and business priorities.
Trusted by 100+ clients, including HSBC, Visa and AXA, we’ve accelerated AI integration across banking, wealth management, private equity, investment firms, fintechs, and other highly regulated industries.
Here’s how we support you throughout the process:
Verify a Startup’s AI Ownership Claims with an Engineer-Led Assessment
Without AI engineering expertise, it can be difficult to verify a startup’s proprietary technology claims, ownership rights, and third-party dependencies. With Neurons Lab, you get the technical guidance needed to assess these claims before investing.
Our experience building AI for financial services means we understand the sector’s regulatory requirements and how they affect data access, system integrations, and security controls. This helps us identify industry-specific risks that a generic technical review might miss.
Our engineers have built AI systems using foundation models, agent architectures, and custom integrations, so they can tell which parts of a startup’s technology is proprietary and which come from third parties.
During the first week, we conduct a review of what the startup has built, what technology it relies on, and who owns it (the AI Automation and Ownership Baseline assessment). This includes a documented walkthrough with the startup’s engineering team covering:
- Technology and infrastructure
- Data and model rights
- Code ownership
That way, you can distinguish proprietary technology from third-party components, and establish what’s automated versus manual. You’ll also gain visibility into ownership gaps and dependencies that could restrict commercial use, introduce unexpected costs, or affect the startup’s competitive advantage.
Understand Production Readiness with an Assessment of AI Architecture, Governance and Risk
A successful product demo doesn’t necessarily mean a startup’s AI can handle the demands of live production. Neurons Lab helps you identify these risks early and assess whether a startup’s AI is ready for live operations.
With hands-on experience moving AI systems beyond prototypes into production environments, our engineers understand the challenges that can stall deployments, from regulatory reviews and legacy integrations to data preparation, AI guardrails, and role-based permissions. This means an assessment reflects what it actually takes to run AI reliably in production rather than just making a demo work.
In the second week, Neurons Lab conducts a hands-on review of the startup’s AI systems, covering its model strategy, MLOps maturity, security architecture, governance controls, monitoring, and auditability. This AI Architecture, Governance and Risk assessment helps you understand whether the technology can handle real-world operations and meet regulatory requirements. You’ll also gain visibility into any weaknesses that could cause system failures, compliance issues, or additional costs.
Test Whether a Startup’s AI Will Hold Up as Usage Grows
A startup’s AI may work well today but struggle as customer numbers, documents, and transaction volumes increase. This growth can slow performance, increase model and infrastructure costs, and put pressure on a small engineering team.
Neurons Lab helps you assess whether the startup’s AI can support projected demand and identify which technical limitations could emerge as usage grows.
During the third week, we speak with the startup’s engineers, review evidence of how its AI performs in practice, and check the information supporting its technical claims. This Findings and Scalability Assessment helps establish what’s currently working, whether the technology can support projected demand, and which limitations could affect future growth. You then receive consolidated findings, ranked by priority, with estimates of the cost, time, and engineering resources needed to address each limitation.
Key deliverables you can expect in this final stage include:
- Technology and engineering review
- AI model and production review
- Data, IP rights, and security review
- Scalability and cost assessment
- Technical risk summary
Once you have the findings and decide to proceed with a deal, you have the option to use our post-deal support to address any identified issues. This is offered separately from your assessment, the findings of which don’t depend on any follow-on work.
Work With an AI Implementation Expert for Technical Due Diligence Services
Technical due diligence helps you look beyond an AI startup’s claims to understand what its technology can deliver, which risks could affect your investment, and what it would take to address them. Working with a specialist AI enablement partner can help you get a more accurate assessment of technical feasibility, scalability, and the work required after investment.
At Neurons Lab, we go beyond reviewing documentation to examine the technology firsthand, verify technical claims, and provide clear findings to guide your investment decision.
If you’re evaluating an AI startup, we can help you understand what you’re investing in so you make your next decision with confidence. Let’s talk.
FAQs
How is technical due diligence for AI startups different from traditional software due diligence?
AI technical due diligence verifies whether a startup’s claims about its AI capabilities, technology ownership, and production readiness hold up. To do this, it examines factors such as model accuracy and reliability, third-party dependencies, and data and IP rights. It also checks how much work the AI actually automates, how errors are monitored, and the cost of completing AI tasks. Traditional software due diligence, on the other hand, focuses on code quality, infrastructure, security, and engineering capabilities.
How long does technical due diligence for an AI startup take?
A full technical due diligence assessment can take a few weeks, depending on the provider. The process typically starts with agreeing on the scope and arranging access to code, documentation, and performance results. Engineers then review the technology, interview the startup’s team, and prepare a findings report. Complex systems or limited access can extend timelines.
Can technical due diligence verify whether an AI startup has proprietary technology?
Yes, depending on the access it’s given. Technical due diligence can verify an AI startup’s proprietary technology claims by reviewing its codebase, AI architecture, and third-party dependencies to establish what it has built and which components come from external providers.
What is AI washing and how can investors spot it?
AI washing is when a startup overstates its AI capabilities deliberately or unintentionally. Investors can spot potential misrepresentation through technical due diligence that reviews system architecture, model dependencies, performance results, and automation records.
Sources
- https://akfpartners.com/growth-blog/technical-due-diligence-for-ai-deals-in-2026/
