Grab Ventures
6
3M
5
1.20
3
0.17
- Stages of investment
- Areas of investment
Summary
The overall number of key employees were 3.
The high activity for fund was in 2019. The fund is constantly included in less than 2 investment rounds annually. Deals in the range of 10 - 50 millions dollars are the general things for fund.
The standard case for the fund is to invest in rounds with 9 partakers. Despite the Grab Ventures, startups are often financed by Vertex Ventures Southeast Asia & India, Vertex Ventures, SMDV. The meaningful sponsors for the fund in investment in the same round are Vertex Ventures Southeast Asia & India, Vertex Ventures, Singha Ventures.
Among the most popular fund investment industries, there are E-Commerce Platforms, Mobile. Among the various public portfolio startups of the fund, we may underline HappyFresh
Investments analytics
Analytics
- Total investments
- 6
- Lead investments
- 3
- Rounds per year
- 1.20
- Follow on index
- 0.17
- Investments by industry
- Internet (2)
- E-Commerce (2)
- Cloud Management (2)
- Software (2)
- Taxi Service (2) Show 13 more
- Investments by region
-
- Indonesia (5)
- Singapore (1)
- Peak activity year
- 2020
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
- 7
- Avg. valuation at time of investment
- 5M
- Group Appearance index
- 0.17
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 |
|---|---|---|---|---|---|
| Luna POS | 15 May 2020 | Accounting, Software, Information Technology, Information Services, Cloud Management, Taxi Service | Seed | Jakarta Raya, Kebayoran Baru, Indonesia |
Similar funds
By same location
By same geo focus
By doing lead investments
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.