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