Founded in 2019, by then 17-year-old Oliver Edholm (CEO) and Anton Osika (CTO), Depict.ai is the leading solution in Scandinavia, and increases sales by 4-6%. The giants within e-commerce have been able to build huge in-house teams that leverage AI to deliver clear bottom-line impact. For instance, Amazon is reported to earn 35% of its revenue from their recommendation engine (McKinsey report). However, most e-commerce stores don't have the technical capabilities, nor the large amount of sales data to make existing recommendation systems work. To counteract this imbalance, Depict.ai

Since Sweden's EQT Ventures embraced AI to drive the way it makes investments, the company has learned that reaping the benefits of algorithms is a journey full of detours that involve experimenting, fine-tuning, and adaption to achieve the promised efficiencies and insights. Following the firm's launch in 2016, a team there developed Motherbrain, an AI-driven system whose goal is to help EQT spot the hidden gems that no one else sees and back them early. So far, Motherbrain has directly led to investments in five startups out of the 50 the firm has made. That may seem like a disappointment. But according to Henrik Landgren, the EQT partner who took the lead on developing the system, the practical value so far has been the ability to make partners more productive by prioritizing which companies are worth spending time getting to know.

A proprietary machine-learning platform has driven more than $100 million in portfolio-company investments for the venture capital firm EQT Ventures, according to numbers first seen by Axios. Why it matters: EQT Venture's home-brewed Motherbrain platform represents a notable effort to adapt machine learning to the failure-prone process of picking and choosing early-stage tech investments. How it works: Motherbrain monitors over 10 million companies and takes data from dozens of structured and unstructured sources to identify patterns that may be useful as EQT Ventures searches for possible investments. Among the companies discovered with Motherbrain's help is Standard Cognition, which develops AI-powered autonomous checkout technology. The bottom line: We often think of automation as outright replacing human jobs, but the Motherbrain platform is an example of another trend: augmenting how humans work.

In a venture capital firm, you want different talents that will enrich the investing team, such as a person from industry, say, mixed with people from the finance world, and perhaps people with a legal or public policy background. You may even want an automaton that crunches numbers. "Motherbrain" is the name that Henrik Landgren, operating partner, and his colleagues at venture capital firm EQT Ventures have given to the computer program that they increasingly turn to in order to get an early read on potential investments. Motherbrain uses convolutional neural networks, or CNNs, the most popular form of machine learning, to review time-series data about companies to help guide where the firm should invest. The technology has seriously improved EQT Ventures's ability to scope out deals early in the pipeline, Landgren said in an interview with ZDNet.

At a meeting in Berlin with venture lead Ashley Lundström, Anydesk cofounder Philipp Weiser learned about Motherbrain, a machine learning system EQT Ventures built to find under-the-radar startups. "She told us we were among the first companies that were discovered by this software," says Weiser. In May, AnyDesk, which sells remote desktop software powered by a proprietary compression system, closed a funding round of $7.6 million with EQT Ventures. Whether the money EQT is putting into Anydesk will turn into a success story remains to be seen. But the firm has applied the Motherbrain algorithm to historical data and shown that it would have identified some of today's highest-flying tech companies as promising investment candidates before they became phenoms.

Give us your feedback Thank you for your feedback. One of the biggest challenges for venture capital companies is finding interesting investment targets before anyone else. It is often a laborious, travel-intensive job. But machine learning and predictive analytics are starting to transform how an investor puts a portfolio together. "My job used to be about getting on a plane once a week and going to a different European city to try to find people who were doing interesting things," says Roberto Bonanzinga, co-founder of InReach Ventures and previously a partner at Balderton Capital, a UK-based VC firm which invests primarily in early-stage European technology companies.

The phone rings and you pick up an unsolicited call from a friend of a friend, who is, in this case, representing a venture capital fund that has identified your business as a potential candidate for Series A finance. The business has been growing fast and more capital is required. And while you haven't as yet gone public with your intention, a Series A round is definitely on the agenda. You talk for a while and agree to take the discussions further. So when the conversation ends do you: a) call in the other founders and collectively punch the air, or b) wonder how on earth you managed to get on the radar screen of an interested VC that was hitherto outside your network?