InvestorFinder
Find investors who've backed founders just like you
515 followers
Find investors who've backed founders just like you
515 followers
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.
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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...
@nithish_a1 Investor–founder fit is so broken right now, this feels like a much‑needed reset.
What I like is that, it doesn’t just dump a generic VC list but actually pattern-matches on founder background, past companies, universities, geo, check size and even solo vs team preference that’s how partners really decide anyway.
Turning that implicit pattern-matching into explicit, searchable data is not only novel but also necessary for first‑time and solo founders. The “paste your profile and let AI map you to 1,000+ investors” flow turns a 4–6 week spreadsheet grind into a single working session.
Excited to see this become a default pre‑raise step for focused, high‑intent outreach.
Many congrats on the launch Nithish and team. :)
@lakshminath_dondeti Not yet, but that's a great idea. We'll be adding that soon.
@lakshminath_dondeti Love this idea. This will be a great addition.
@francesco2689 Thanks Francesco. We have a database of 1000+ investors and we'll look at the type of people they've invested in- what their backgrounds are, what type of products they invested in. When you enter your profile URL and your product idea, our AI matches you to the right investor.
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?
@krwdko Thanks Konstantin! We have a database of investors (global coverage) and we'll look at the type of people they've invested in- what their backgrounds are, what type of products they invested in. When you enter your profile URL and your product idea, our AI matches you to the right investor. We used our own API to create the database for this tool.
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.
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.
@thamibenjelloun Glad you found it useful Thami!
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.