Clado: AI people search across 200M profiles finds the 25 people in the world who fit any niche query in 30 minutes

the year. Out of the year. Right. and and and right at the perfect time because we we were just hitting like peak vibe reel, right? If they dropped it even a month later, it would have been gone. Welcome to the stream. I'm John. Nice to meet you. Nice to meet you. I'm John. How you doing?

PMF for or die since they everyone died. Everyone died. You have a story about PMF or die? They died. They died. It happens sometimes. Sometimes. Never lock yourself in a room for 90 days. Move to sunny San San Francisco. do YC. That's my recommendation from here on out. Philadelphia.

So anyway, uh please introduce yourselves in the company. Yeah, I'm Tom. I'm Eric. You guys, you guys look you're not related at all, right? You could go by brothers. People ask us, people ask us brothers. Uh what does the company do? We're doing aic people search.

So we have a database, people database of around 200 million people.

And um we use a genetic search to search over that for uh companies and businesses to do like sales recruiting uh GTM and uh so so are we talking about specifically like I am a recruiter and I need a saleserson and I'm going to yod you to try and hire them.

No no so basically like you can put in a criteria like essentially what we do is hold the microphone up here. Push the microphone a little closer to there. Yeah.

I mean, essentially what we do is we like like like deploy an LLM, assign every profile to that LLM, and then given your criteria that could be like one paragraph long.

We we just ask the LLM if if this profile like meets that criteria and then and then we built like a the load load distributor to run like 10,000 LM calls in parallel. Wow. Do do that at scale. You know, we can we can search over like 5 million profiles in like 30 minutes and insane.

Uh so so walk me through one of the key examples. I'm sure this is live. You have customers What's an example of someone using this? So, for example, um you know, we had a query come in yesterday actually.

It was just like every founder that was acquired that was the CTO of their startup was acquired by data bricks or snowflake in the last three years and they and they still work there.

And there's only probably like 25 people in the world that fits that profile and we found all 25 of them just by using because we can use have an LM go in just like because we have like we can throw as much compute at the problem as we want and then and then and then the LM get better the more comput.

It's interesting to be able to find you're able to find information that is historically effectively impossible to find without doing all the work that you guys did ahead of time. Makes tons of sense.

So, uh, who's who what buyer or what buyer archetype within an enterprise or business is most excited to buy your product? Yeah. I mean, like mega recruiters by far recruiting firms. Yeah. Oh, no. Just like people that you know or like a big recruiter at Yeah.

I mean we work with mercore right I know for example mercore yeah so so you know like like just like people hunting for talent but it's like a generalized type skill right so for example you know like an AI lab's training a new voice model and they need like people that speak Cantonese then it's like okay find me every Cantonese speaker in the US that has like a podcast presence and then like okay great these guys can come train our voice models right like that's kind of or they might even need someone who speaks Cantonese and also is an expert in biology so they so they can talk about the biology terms and that's something that how are you going to search that It's so funny because I you I run these type of queries in my own head where I'm like we need a videographer who's in LA that has experience in film but you know but it's also part of teapot that's basically what we need often times like has a sense of humor.

It's like how would you even know that? Well, if you look through their post I'm sure you can figure out basically like as close as you can if you just give like a criteria to human recruiter.

So you can imagine like 5,000 human recruiters just like manually looking over profiles and then you know okay uh business model are you uh most recruiting firms they charge on like a per fee basis $30,000 to place an engineer somewhere um are you are you doing like a seatbased pricing consumption based pricing this sounds expensive if you're talking about running 20 million LLM queries at the same time it's actually not as inexpensive as people think because because you know open source models have gotten so good yeah um like like I'm guessing like you know any like deep research search query cost us like $10 maybe.

Okay. But then um you know like we we we charge for conversion with with our PP conversion. Okay. Yeah. We charge by conversion customer. So that's probably pretty expensive. Exactly. And also um we have like a platform that's that's just available to everybody.

So they can pay us a subscription fee search as much as they want and do email enrichment phone number. I feel like we could use this. This is we might be customers. Yeah. We need a guest that doesn't hate tech that can about this. you two meet uh what were you doing before YC?

Yeah, so we met in elementary school actually. So we were originally from Canada. Let's go. Um we met in elementary school and we became close friends. Yeah, it's been great here. But um uh we became close friends when Eric he uninstalled Windows on my computer.

Um and I was really pissed at him for a day and then we fixed it. So then we became great friends after that. Windows couldn't load anything homework. That was it was during school and then uh ultimate prank is uninstall Windows on your best friend's computer. Boys being boys. We spent one semester of college each.

So So I was at Penn, he was at UC San Diego and then and then come January we were both like like why are we even there? Let's start 18 19. We're both 18 right now. Here we go. Came down here. What's What's the youngest team that you've met here besides yourselves? 17. There is a high school senior. Okay.

I met when I was in jail. Okay. Yeah. And it's getting younger. Yeah. Yeah. Yeah. We have a bottle of wine here. We're going to give it to the I'm only hopefully you're not going back through YC in 3 years. Hopefully you're at the NASDAQ or something. Talk to me about traction metrics.

Anything that you're sharing here at demo day? Anything to get the venture capitalist excited? Yeah. 270 paying customers. First version of the So, I don't know if you guys heard about linked linked. No. Yeah. Yeah. All right.

So, so, so, so a very early version of this product we launched which is like rank Stanford like like Stanford rank. Okay. We which was so we basically built this we basically scraped the entire alumni the database of Stanford. I'm sure they love that. Yeah. They were okay with it.

We we put it online and then uh and then and and then and then we had this uh we had we had this app where people can like see two random alumni put next to then next to each other and then you and then you vote for who's more correct. Who's more correct? Correct. And that version of the Yeah.

And that version of the app picked up like 80,000 users was how we got into YC and stuff. Very cool. Very cool. And then Yeah. And then you know we came down here uh 270 paying customers about 16,000 my monthly recurring revenue. Um since ago Yeah. No, but Yeah. Wow. Isn't just you two? It's a So there's three of us.

We were two combo founding engineers from high school. She just graduating. No, I'm super excited for you guys. Congratulations on the product and I actually have a bunch of people to send this to. Yeah, this is fantastic. We should give it a try. Fantastic. Thank you so much. We'll talk to you soon.

Great meeting you guys. Fantastic. Uh