IP Due Diligence Before Your AI Raise (Founder Checklist)

Why IP Due Diligence Matters Before an AI fundraise? In 2025, AI had the highest deal value and deal count. However, when it came to average deal size, AI was 7^th among the top 20 sectors by deal value. The top spot was claimed by Big Data.

One possible contributor to this trend could be the ownership of the intellectual property that generates much of the business valuation.

An AI company may have impressive revenue, a strong engineering team, and a capable model. However, if the data, code, architecture, or know-how that goes into building that model is difficult to protect or easily replicable, investors may discount the value attributed to the technology.

That’s why IP due diligence is particularly important before a funding round. Understanding what your company actually owns, what it merely licenses or accesses, and what competitors could reproduce can influence how investors assess the durability of the company’s revenue.

In this article, we examine the key IP questions AI founders should answer before a funding round and how each can influence the value assigned to their AI assets.

Key Takeaways

• Establish exactly how your training data was sourced, what rights you have to use it, and how exclusive it is to your business
• Document human authorship and contractual ownership of source code, model architecture, pipelines, and other critical AI assets
• Treat trade secrets as an actively managed IP category by controlling access and maintaining reasonable confidentiality measures
• Test whether a technically capable competitor could replicate your AI assets using the same publicly available resources
• Separate the value of proprietary technology from the value provided by open-source components

AI IP Due Diligence Checklist: What to Verify Before You Raise

Before entering fundraising discussions, make sure you can answer these five questions:

• Where did your training data come from? Document the source, license or consent basis, restrictions, and permitted uses.
• Who owns your technology? Confirm that employees, contractors, founders, and other contributors have transferred the relevant IP rights to the company.
• Are your trade secrets protected? Identify confidential information and document the safeguards used to keep it secret.
• How difficult would your AI assets be to replicate? Assess whether competitors could rebuild the technology using the same public tools, models, and information.
• Which parts of your stack are proprietary? Separate the value created by your company’s IP from third-party and open-source components.

Completing these checks can help founders identify ownership gaps and give investors a clearer picture of what makes the technology defensible.

What determines the value of your company’s collective AI IP?

The value of your AI IP depends on more than whether you have filed a patent or registered a copyright. It depends on where your data came from, who created the technology, how well it is protected, how difficult it is to replicate, and how much of the underlying value comes from components you own.

Where Did Your AI Training Data Come From?

Training data can be created internally, licensed from third parties, scraped from publicly available sources, or supplied by customers with appropriate consent. You need to be able to prove where your training data came from and that you had the right to use this data to train your model.

This matters for valuation for two reasons.

Firstly, there is legal risk. Copyright law around AI training remains an evolving area, with the US Copyright Office continuing to examine the use of copyrighted works in AI training. In Part 3 of the Report of the Register of Copyrights, the US Copyright Office stated that AI should benefit the general public and the content creators in addition to the developers.

Secondly, exclusivity matters. If competitors can obtain the same underlying datasets, the data may contribute less to your defensible competitive advantage. Conversely, hard-to-recreate data assets like proprietary datasets and customer-generated information obtained with appropriate rights can strengthen the economic value of your AI technology.

Before fundraising, document the source, license or consent basis, restrictions, exclusivity, and permitted uses for each material dataset.

Who Owns Your Source Code, Model Layers, and Data Pipelines?

Your AI stack may have been built by employees, contractors, founders, or through AI-assisted development. From an IP perspective, knowing who actually created each material component is essential.

Under current U.S. Copyright Office guidance, AI-assisted works may qualify for copyright protection when sufficient human authorship is present; merely generating material through prompts does not necessarily establish copyrightable authorship.

For founders, that means maintaining evidence of human contribution and, just as importantly, reviewing the contracts governing that contribution. Employment agreements and contractor agreements should clearly establish that the relevant rights stay with the company.

So, you will need to build an IP ownership record showing who built each critical component, when it was created, what tools or third-party materials were used, and what agreement transfers the resulting rights to the company.

Are Your AI Trade Secrets Actually Protected?

Not every valuable AI asset will fit neatly within patent or copyright protection. In many cases, trade-secret protection may be the more practical route for things like model optimization techniques, prompts, or technical know-how. But simply calling something a trade secret does not make it one.

Trade-secret protection generally requires that access to the relevant information is limited, the secrecy gives rise to a certain commercial value, and reasonable steps to maintain that secrecy. Those steps can include confidentiality agreements, restricted access, and need-to-know policies.

Before your raise, identify the information you believe is a trade secret, document who can access it and the key protections, and preserve evidence that those protections are consistently enforced.

Could a Competitor Easily Replicate Your AI Assets?

A central question in technology valuation is not simply whether something works, but how difficult it would be for someone else to recreate it.

This is particularly relevant to technology you want to patent, where novelty and non-obviousness can determine whether an innovation receives meaningful protection. A technology that can be developed through routine steps by someone with ordinary skill may have weaker defensibility than one requiring significant technical insight.

Also, just because your technology doesn’t mean your competitive advantage is defensible.

Consider what would happen if a well-funded competitor hired a capable engineering team and had access to the same publicly available knowledge, open-source tools, and commercial models that you used. Could they reproduce your product in 6 months? 2 years? Or would they need your proprietary data, specialized architecture, accumulated know-how, or unique experimentation history? If competitors cannot legally use the technology or production method they need to compete with you, your competitive advantage is defensible. Otherwise, the protection provided by patents will be limited, and your AI assets may be assigned a lower value.

The higher the barriers to replication, the stronger the case for assigning value to the underlying IP rather than simply valuing it at the cost of rebuilding the technology.

How Much of Your AI Value Depends on Open-Source Components?

Modern AI development depends heavily on open-source software. Libraries such as TensorFlow, PyTorch, and scikit-learn are examples of widely used components that can accelerate model development without requiring companies to build every layer internally.

That is not a weakness. In fact, rebuilding commoditized infrastructure from scratch would often be economically irrational.

But you need to know how much of your AI asset’s value comes from components that you do not own.

An IP valuation analyst will typically look at the individual components contributing to the overall asset and assess where the components actually create value for the business. Value attributable to freely available or third-party components cannot simply be treated as proprietary company IP.

The objective is not to eliminate open source. It is to understand the distinction between the open-source elements you are using and the proprietary layer that creates your competitive advantage.

Frequently Asked Questions About IP Due Diligence 

Turn IP Due Diligence Into a Stronger Valuation Case

IP due diligence and IP valuation answer different questions.

Due diligence asks: What does the company own, and what risks could affect those assets?

Valuation asks: What are those assets worth economically?

Once founders have documented ownership, licensing rights, proprietary technology, and replication risks, they have a stronger foundation for assessing the economic contribution of their IP.

Eqvista helps companies quantify the value of intellectual property using valuation methodologies designed to support a defensible, data-backed valuation. For founders preparing for a fundraise, that analysis can provide another piece of evidence behind the company’s broader valuation story.

If you’re preparing for a funding round, explore Eqvista’s IP valuation services to assess the economic value of your technology. Contact us to learn more!