We were contacted by a Private Equity company which invests in the energy sector. They were interested in acquiring a computer vision startup which had developed a prototype model to detect defects in machinery.
We went to the offices of the company and spoke to the founders and a number of employees. The employees demoed the product and we were allowed to test it. We were also given access to the company’s code repository.
The audit of the company followed the Fast Data Science due diligence checklist with some custom checks agreed by the client. We found that the product was original and well designed, but still in a prototype stage and without a live demo. We produced a 16-page written report, and presented our findings to the client. In particular a number of recommendations were made regarding personnel, technology, and cloud computing costs.
Fast Data Science - London
The client packaged our due diligence report together with the accompanying accounting and legal due diligence reports, so our report was formatted to be consistent with those reports and in a language that investors were able to understand.
The client was happy with our report and proceeded to acquire the startup.
An investment company was interested in adding a university spinoff to its portfolio. The spinoff was run by a postdoc who was attempting to commercialise his research.
The researcher had invented a device which had a potential medical application. We visited the company premises and tried out the product, which was itself very impressive. We identified that the company’s code base could do with some documentation and following development best practices, and the company staff were generally not full-time but had other commitments in academia. All staff had impressive academic credentials.
The investment house were impressed with the startup and proceeded to acquire it, albeit at a lower figure than previously discussed due to some of the concerns that our report had raised.
A consortium of American investors was interested in investing in a European academic who was applying natural language processing to make a product with potential uses in the pharmaceutical industry.
We were given access to the datasets and source code and quickly determined that the software had been developed only with open data, and the valuable datasets were hard to obtain because they were in the possession of large corporations.
The investors promptly put their plans on hold pending a licensing agreement to obtain training data.
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