simple data sarl

Total investments

4

Average round size

209K

Portfolio companies

1

Rounds per year

0.18

Follow on index

0.75

Areas of investment
SoftwareInformation TechnologyHealth CareMobile AppsMedical DeviceMedicalPersonalizationInternet of ThingsPrivacyQuantified Self

Summary

In 1999 was created simple data sarl, which is appeared as Corporate Investor.

Deals in the range of 100 thousands - 1 million dollars are the general things for fund. The important activity for fund was in 2013. The average startup value when the investment from simple data sarl is 1-5 millions dollars. The fund is constantly included in less than 2 investment rounds annually.

The typical case for the fund is to invest in rounds with 1 participant. In the next rounds fund is usually obtained by Foundation for Technological Innovation (FIT), Cosa Trade SA.

Besides, a startup requires to be at the age of 2-3 years to receive the investment from the fund. Among the most popular fund investment industries, there are Personalization, Quantified Self. Among the various public portfolio startups of the fund, we may underline Pryv

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Investments analytics

Analytics

Total investments
4
Lead investments
0
Rounds per year
0.18
Follow on index
0.75
Investments by industry
  • Medical Device (4)
  • Internet of Things (4)
  • Software (4)
  • Mobile Apps (4)
  • Quantified Self (4)
  • Show 5 more
Investments by region
  • Switzerland (4)
Peak activity year
2016

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Quantitative data

Avg. startup age at the time of investment
9

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Latest deals

Company name Deal date Industry Deal stage Deal size Location
Pryv 01 Apr 2016 Software, Information Technology, Health Care, Mobile Apps, Medical Device, Medical, Personalization, Internet of Things, Privacy, Quantified Self Seed 90K Switzerland, Vaud, Lausanne
How we get our data

At Unicorn Nest, we combine cutting-edge technology with human expertise to build one of the most reliable venture capital databases in the market. Our process begins with automated AI-enhanced data collection, leveraging the full potential of Large Language Models (LLMs).

Later, our team of analysts takes it further with manual verification, using proprietary tools for data cleaning and validation to ensure accuracy and reliability. We cross-check and enhance our findings through press and media monitoring, integrating information from trusted news outlets and venture capital aggregators. Finally, we stay ahead of the curve by monitoring social networks like LinkedIn and X.com.