Follett Knowledge Fund
2
2M
2
1
- Stages of investment
- Areas of investment
Summary
The standard case for the fund is to invest in rounds with 6-7 partakers. Despite the Follett Knowledge Fund, startups are often financed by Chuck Templeton, Nessan Fitzmaurice, MATH Venture Partners. The meaningful sponsors for the fund in investment in the same round are Great Oaks Venture Capital, TAL Education Group, StartX (Stanford-StartX Fund). In the next rounds fund is usually obtained by TAL Education Group, Signe Ostby, Scott Cook.
The fund is constantly included in less than 2 investment rounds annually. The important activity for fund was in 2015. The usual things for fund are deals in the range of 1 - 5 millions dollars. Speaking about the real fund results, this VC is 13 percentage points more often commits exit comparing to other organizations.
Among the most popular portfolio startups of the fund, we may highlight ThinkCERCA. We can highlight the next thriving fund investment areas, such as EdTech, Education. Besides, a startup needs to be aged 4-5 years to get the investment from the fund.
Investments analytics
Analytics
- Total investments
- 2
- Lead investments
- 1
- Investments by industry
- Education (3)
- E-Learning (2)
- EdTech (2)
- Apps (1)
- Investments by region
-
- United States (3)
- Peak activity year
- 2014
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
- 8
- 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 |
---|---|---|---|---|---|
ClassOwl | 22 Jul 2014 | Apps, Education | Seed | 850K | United States, California, San Francisco |
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.