Launching today
FindThem
Describe ideal lead or investor - get their Linkedin & email
52 followers
Describe ideal lead or investor - get their Linkedin & email
52 followers
FindThem is an AI-powered search engine across 1B+ LinkedIn profiles enriched with Web Data. Find angel investors, sales prospects, hiring managers, and decision-makers with verified emails. Try free, credits per profile found & enrichment [No subscription].
@kuda Congratulations on this. I'm going to try it out. But I must say the pay-per-profile model is smart, it removes the commitment barrier that makes founders hesitate to try new tools. The real question is what happens after the export. A CSV of verified leads is only as useful as the system receiving it. Most founders plug it into whatever CRM they already have, which often isn't set up to handle enriched data properly. I'm talking about wrong pipeline stages, no follow-up sequences, contact records that just sit there. The prospecting problem gets solved and the conversion problem quietly gets worse.
Quite useful! But if lead discovery becomes trivial with tools like this, do you think the real bottleneck shifts to outreach quality?
@lak7 outreach quality was always the most important - this tool gives you more options
This is going to be really helpful for HR's at our company, just a quick question how do we make a bulk search and bulk export?
@nayan_surya98 hi you just search for how many profiles you need up to 1000 and then export as csv
Features.Vote
does natural language search actually handle descriptions like 'fintech operator who's exited once and is now investing in b2b saas' differently from keyword search, or is the 'describe your ideal lead' interface just a more polished way to run the same structured filters?
from what i can read, the differentiation is in the enrichment layer. 1b+ profiles with web data layered on top of linkedin means the matching isn't limited to what's in someone's headline or job titles. if the enrichment actually captures context that structured fields miss, like conference speaking history, writing, or other signals, then natural language queries start to mean something qualitatively different. for use cases where the right person is hard to find by title alone, that's where this earns its pitch.
@gabrielpineda The natural language search here is not polished filters , it is semantic search + verifiable requirements that get evaluated against 1B+ profiles enriched with web data, so "fintech operator who's exited once" can match someone whose LinkedIn says "GP at [fund]" but whose writing and talks reveal the operating background.
But yeah the enrichment layer is what makes natural language queries mean something qualitatively different.