LEA Partners appeared to be the VC, which was created in 2002. The company was established in Europe in Germany. The main department of described VC is located in the Karlsruhe.
The standard case for the fund is to invest in rounds with 2-3 partakers. Despite the LEA Partners, startups are often financed by TEV | Tengelmann Ventures. The meaningful sponsors for the fund in investment in the same round are Wecken & Cie., Vito Ventures, VC Fonds Baden-Wurttemberg.
Besides, a startup needs to be aged 2-3 years to get the investment from the fund. Among the most popular fund investment industries, there are B2C, Indoor Positioning. Among the various public portfolio startups of the fund, we may underline understand.ai, sevDesk, INTRANAV For fund there is a match between the country of its foundation and the country of its the most frequent investments - Germany. The fund has exact preference in a number of founders of portfolio startups.
Besides them, we counted 5 critical employees of this fund in our database.
Considering the real fund results, this VC is 3 percentage points less often commits exit comparing to other organizations. The fund is generally included in 2-6 deals every year. The high activity for fund was in 2017. This LEA Partners works on 12 percentage points less the average amount of lead investments comparing to the other organizations. The usual things for fund are deals in the range of 5 - 10 millions dollars. The higher amount of exits for fund were in 2019.
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– Quality Match, a Heidelberg, Germany-based quality data annotation provider, today announced that it raised a €5 million ($6 million) seed round from LEA Partners.
– The company says it’ll use the proceeds to expand its team and accelerate product development.
– Training AI and machine learning algorithms requires plenty of annotated data. But data rarely comes with annotations.
– The bulk of the work often falls to human labelers, whose efforts tend to be expensive, imperfect, and slow.
– It’s estimated most enterprises that adopt machine learning spend over 80% of their time on data labeling and management.
– In fact, in a recent survey conducted by startup CloudFlower, data scientists said that they spend 60% of the time just organizing and cleaning data compared with 4% on refining algorithms.
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