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scherbak

96 karma · joined October 16, 2019

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scherbak··on [dead]
We've built an algorithm that analyses historical data of 38,000 VC funds and suggests relevant investors for startups (seed, round A, B and C).

We use an innovative approach, scoring instead of filtering. You only need to fill in the questionnaire. We will search among 300,000+ deals in the database to find the funds investing in startups like yours. The funds are given points each time there's a match with your questionnaire.

What you can do with us: 1. get a list of relevant VC investors completely for free 2. save the funds you like to favorites to check them out later and compare 3. get a list of all available decision makers in the funds 4. see contact details of relevant decision makers (GDPR doesn’t allow sharing emails publicly but we can give you email formation rules that you can turn easily into actual emails) 5. send chosen funds to our email outreach system to start a fundraising-optimized email campaign

scherbak··on [dead]
After 2 years of experiments we have fine-tuned our algorithm that matches startups with relevant VC investors. We have collected data about 170,000 deals and 45,000 funds and learned to score funds based on how much their portfolio companies correspond to the startup looking for funding. You need to fill in the questionnaire and will get the list of suggested funds that are likely to respond if you contact them.
scherbak··on Show HN: Startup Valuation Nest – Check startup valuations in your industry
Discover aggregate data on average startup valuations and revenue multipliers in 55 industry groups, based on real data sourced from 200,000+ deals. Learn who are the top VC investors in similar rounds.
scherbak··on Dataset of VCs investing in seed and Series A+ rounds
we meant prohibition of use in a commercial manner. Will have to adjust the terms accordingly. Thanks for pointing this out.
scherbak··on Dataset of VCs investing in seed and Series A+ rounds
Thanks for pointing this out. We've actually meant that any use for personal purposes is allowed. Not as strict as it sounds in the ToS.
scherbak··on Dataset of VCs investing in seed and Series A+ rounds
We don't see any intrinsic value in owning the dataset. We are working on a tool that optimizes the search of investors and saves dozens of hours. We are happy to share the dataset with those entrepreneurs who know what they need and what type of an investor they are searching for.
scherbak··on Dataset of VCs investing in seed and Series A+ rounds
No, that's fine. We don't consider this as academic purposes.
scherbak··on Dataset of VCs investing in seed and Series A+ rounds
Today we published the database of funds for free. This is currently the largest, most complete and most relevant dataset in the world. It contains more than 500,000 data cells about 26,000+ venture funds and more than 30,000 employees who make investment decisions, including the rules of their email formation (we cannot share emails directly because of GDPR). We will update investor profiles on our website on a weekly basis, and this database on a quarterly basis.
scherbak··on Show HN: Unicorn Nest – algorithmic matching with relevant VCs for fundraising
Hello everyone.

I want to show you the tool that helps startup founders at the seed and following stages find the VCs who would invest in their startup with the highest probability.

Instead of searching for particular funds using certain criteria, we offer the opposite process: you type the information about your startup, and our algorithm will pick the ones that are most likely to invest in the startup taking into account the criteria such as MRR, cumulative revenue, HQ location, your sector (industry) etc. We have more than 30 000 funds in our database, and we analyzed more than 90 000 deals to tailor our algorithm for these needs.

Please check it out. We want to make it as good as we can. There's a ton of work to be done, but still it's important to know that we are headed in the right direction. I will appreciate any comments and feedback.