Finally, real data on how every VC partner actually invests - founder backgrounds, universities and prior companies. Paste your profile and find your best matches in seconds.
When founders raise, they pitch dozens of investors, get a handful to back them, and spend months figuring out who the right ones were all along.
InvestorFinder shortens that gap massively. Based on our database of 1000s of investors, our AI match you to the right investor based on your profile and product.
You also get 12+ rich datapoints on every partner:
🎓 Where their founders studied 🏢 Where their founders worked 📍 Where their founders are based 🧠 Founder archetypes 💰 Check size
The use cases are straightforward:
🚀 Founders raising: Stop cold pitching. Find the 10 partners whose portfolio data looks like you and go deep on warm intros to those specific people.
🌱 First-time founders: You don't have a network yet. Use the data to identify exactly which investors have backed founders with similar backgrounds to yours.
🧑💻 Solo founders: See which partners have consistently backed solo founders vs. those who only back co-founder teams. Don't waste a pitch on the wrong fit.
📋 Preparing for a pitch: Before any meeting, pull up the partner's profile and know their investment patterns. Walk in more prepared than any other founder in the room.
🔍 Founders doing investor research: Cross-reference check size, stage, sector focus, and founder background all in one place before you add anyone to your target list.
1000+ investor profiles across Sequoia, a16z, Lightspeed, and more. Free. 🎉
Is there AI search? For example, it would be very useful for me to find investors with a background in the travel industry who are currently investing in AI startups. It’s hard to find something like that using filters.
I just ran the search for a founder I am working with on a consumer product. I was blown away with the results and the matches found. This is so cooooool.
Pattern-matching investors by their actual portfolio (founder backgrounds, prior companies, universities) is the right primitive — VCs themselves are doing this exercise manually all the time, so codifying it is overdue. The interesting frontier is "signal-of-signal" data — what large prediction-market or live-trade flows say about which sectors are getting believed in next. We tried building a small version of this on PolyMind (PolyMarket alert layer) and got some unexpected reads on consumer-AI categories weeks before standard sentiment caught up. Would love to see InvestorFinder layer that kind of forward-looking signal on top of the historical-portfolio match.
Im actually looking for some seed capitol, are there already a good amount of investors on here?
This basically exposes what VCs actually do vs what they claim. Most say they're thesis-driven but the data shows clear patterns toward certain founder types. Anyone pushed back on this kind of categorization?
Really useful timing — we're building Faindo and plan to raise soon, so the "wrong investor" problem is something we're already thinking about.
One question: do you factor in whether an investor has backed tools in emerging categories before? We're in AI lead interception — a category that barely existed 18 months ago. Curious if InvestorFinder surfaces partners who have a pattern of backing category-creating products vs. established markets.
This is super useful for solo founders like me. As someone building FinTrackrr (a free personal finance tracker), finding the right investors who've backed similar bootstrapped fintech tools has always been a pain. The idea of matching based on actual portfolio patterns rather than just categories is smart. Great execution!
interesting tool for both founders and investors... will you add PM function in future?
Love the concept! How do you source the VC data — is it manual curation, public APIs, or scraping? And do you cover European/Asian investors or US-focused?
Some VCs publish why they invested in a company in their blogs and others appear to actively promote their portfolio companies on LinkedIn and Twitter.
Do you scan for all these for signals?
This is actually useful. Things like background, past companies, and even patterns in who partners tend to back matter a lot more than people think.
Congrats on the launch 🙌 This is the kind of tool I actually need right now as I am looking for early-stage investors. When I sign up, how does it figure out which investors are the right match for me?
Cool concept (already found my potential matches) :D
+ I must say that the design of the page is composed pretty well!
About InvestorFinder on Product Hunt
“Find investors who've backed founders just like you”
InvestorFinder launched on Product Hunt on May 10th, 2026 and earned 276 upvotes and 23 comments, earning #2 Product of the Day. Finally, real data on how every VC partner actually invests - founder backgrounds, universities and prior companies. Paste your profile and find your best matches in seconds.
InvestorFinder was featured in Investing (26.6k followers), Venture Capital (49.5k followers) and Tech (623.2k followers) on Product Hunt. Together, these topics include over 171.3k products, making this a competitive space to launch in.
Who hunted InvestorFinder?
InvestorFinder was hunted by Rohan Chaubey. A “hunter” on Product Hunt is the community member who submits a product to the platform — uploading the images, the link, and tagging the makers behind it. Hunters typically write the first comment explaining why a product is worth attention, and their followers are notified the moment they post. Around 79% of featured launches on Product Hunt are self-hunted by their makers, but a well-known hunter still acts as a signal of quality to the rest of the community. See the full all-time top hunters leaderboard to discover who is shaping the Product Hunt ecosystem.
Want to see how InvestorFinder stacked up against nearby launches in real time? Check out the live launch dashboard for upvote speed charts, proximity comparisons, and more analytics.
Rohan Chaubey
Hey PH! 👋
When founders raise, they pitch dozens of investors, get a handful to back them, and spend months figuring out who the right ones were all along.
InvestorFinder shortens that gap massively. Based on our database of 1000s of investors, our AI match you to the right investor based on your profile and product.
You also get 12+ rich datapoints on every partner:
🎓 Where their founders studied
🏢 Where their founders worked
📍 Where their founders are based
🧠 Founder archetypes
💰 Check size
The use cases are straightforward:
🚀 Founders raising: Stop cold pitching. Find the 10 partners whose portfolio data looks like you and go deep on warm intros to those specific people.
🌱 First-time founders: You don't have a network yet. Use the data to identify exactly which investors have backed founders with similar backgrounds to yours.
🧑💻 Solo founders: See which partners have consistently backed solo founders vs. those who only back co-founder teams. Don't waste a pitch on the wrong fit.
📋 Preparing for a pitch: Before any meeting, pull up the partner's profile and know their investment patterns. Walk in more prepared than any other founder in the room.
🔍 Founders doing investor research: Cross-reference check size, stage, sector focus, and founder background all in one place before you add anyone to your target list.
1000+ investor profiles across Sequoia, a16z, Lightspeed, and more. Free. 🎉
Try it at: https://tools.crustdata.com/inve...