AI and machine learning due diligence (+ checklist download)

AI and machine learning due diligence (+ checklist download)

AI technical due diligence: Technical due diligence on AI companies

You may be considering making an investment in a startup (your “target”) that claims to use machine learning/AI and would like an impartial assessment of their technology along a practical checklist, or involved in planning a corporate merger. We can perform an evaluation of a company’s technology stack. This should be done in tandem with a traditional due diligence exercise. You can download our AI due diligence checklist.

We have performed a number of AI due diligence exercises in different industries. You can read our AI due diligence case studies.

Some of the points we will look at in our technology/machine learning due diligence checklist include:

  • is the product a real product and capable of being sold as is, or just a demo?
  • how viable and scalable is the technology long term?
  • where will the data for the AI come from? If from users, then does the product suffer from the cold start problem?
  • can the product scale in the real world?
  • how reproducible is the product?
  • how sustainable are the development workflows?
  • are code and models documented and under version control?
  • are the target’s founders’ credentials and experience valid and as claimed?
  • is there any crucial employee, or exceptionally talented employee on whom the target business depends?
  • is the team well-balanced with complementary experience, or have the founders recruited individuals with the same profile?
  • how is the product’s performance measured (see posts on evaluating generative models or healthcare applications)?
  • is sensitive data sent to third party generative AI providers such as GPT? Is the start-up a thin wrapper around a well-known GenAI offering?

We have broken down machine learning due diligence into code and product due diligence, model, data, IP, workflow and people.

  • how robust is the target organisation’s structure? Does the company have a low bus factor?
  • How is the organisation’s approach to data science project management?
  • how is the technology licensed?
  • how will costs scale as the operation scales?
  • if we are talking about a healthcare or medical device, is the product likely to need licensing?
  • how reproducible is the technology, irrespective of patents and legal protection?
  • what are the future risks from a technology perspective?
  • is there any risk of reputational damage from the technology?

Once you’ve downloaded the AI/technology due diligence checklist you can read more about how technical due diligence can help you, and how Fast Data Science conducts technical due diligence on AI companies.

Due diligence on AI companies

Performing AI/machine learning due diligence?

NLP, ML and data science leader since 2016 - get in touch to talk about AI or due diligence on AI and machine learning companies.

Read Fast Data Science’s AI due diligence case studies.

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