ESOP

Total investments

1

Average round size

1M

Portfolio companies

1

Areas of investment
SoftwareHealth CareMedical DeviceMedical

Summary

Among the most popular portfolio startups of the fund, we may highlight Swift Medical. We can highlight the next thriving fund investment areas, such as Medical, Medical Device. Besides, a startup needs to be aged 2-3 years to get the investment from the fund.

The usual cause for the fund is to invest in rounds with 7 partakers. Despite the ESOP, startups are often financed by Ryerson Futures, MaRS Investment Accelerator Fund, Hacking Health Accelerator. The meaningful sponsors for the fund in investment in the same round are Relay Ventures, Real Ventures, MaRS Investment Accelerator Fund. In the next rounds fund is usually obtained by Relay Ventures, Real Ventures, Data Collective DCVC.

The important activity for fund was in 2017. The usual things for fund are deals in the range of 1 - 5 millions dollars. The fund is constantly included in less than 2 deals per year.

Show more

Investments analytics

Analytics

Total investments
1
Lead investments
0
Investments by industry
  • Software (1)
  • Health Care (1)
  • Medical (1)
  • Medical Device (1)
Investments by region
  • Canada (1)
Peak activity year
2017

Discover reliable insights

Leverage validated data, identify key contacts and secure funding opportunities for your business.

Quantitative data

Avg. startup age at the time of investment
6
Group Appearance index
1.00

Need more data?

Get access to full data about investors, including their team, contact information, and historic data.

Latest deals

Company name Deal date Industry Deal stage Deal size Location
Swift Medical 24 Jan 2017 Software, Health Care, Medical Device, Medical Seed 1M Ontario, Old Toronto, Canada
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.