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

You may be considering making an investment in a startup that claims to use machine learning/AI and would like an impartial assessment of their technology.

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.

Some of the points we will look at 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 founders’ credentials and experience valid and as claimed?
  • how robust is the organisational structure, does the company have a low bus factor?
  • 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?