How to Use Ai To Find Companies Before They Raise Millions

Hey, welcome to the Next Wave podcast. I'm Matt Wolfe. I'm here with Nathan Lands. And today we're talking about the future of investing. We're going to be talking about how you can leverage AI using tools built on top of Crunchbase to figure out what to invest in, what your sales team should go and focus on all sorts of really cool strategies to leverage data and AI around the world of investing. And Nathan, in this episode, had one of the most brilliant ideas I've ever heard for a salesperson. If you're actually out there trying to sell your product to companies, like, you need to stick around, because this idea, I think, could totally change the game for your business. But this is an amazing episode. It's with Jagger McConnell, the CEO of Crunchbase. And I'm not going to say anymore. Let's just go ahead and jump in and talk to Jagger. We're going to jump into what Crunchbase is doing with AI, how you can leverage Crunchbase. We're going to get a little tour, a demo of what Crunchbase AI capable of, and then we're going to talk about the future of AI and investing. So thank you so much for joining us, Jagger. It's great to have you on the show.

Great to be here.

Yeah, awesome. Well, let's just dive straight into it and talk a little bit about Crunchbase. I know that Crunchbase over the last, I don't know, however long, maybe the last year or so, has really, really gone deep into the AI world and sort of shifted what Crunchbase is into, like an AI first platform. So let's, let's talk about that. Like, what, what is the sort of grand vision with Crunchbase? Who is it for? You know, what's, what's the plans with it? Let's just get right into it.

Look like we, we've been for the last 14, 15 forever years this sort of historical record of what's happened with a company. And we realized with AI happening and like all this sort of, yes, it's structured data, but, but huge amounts of structured data, were there insights that maybe were more interesting than what's happened in the past with the company? Can we use AI to figure out what's going to happen next with a company? How accurate would it be if we go and try to figure out, you know, what's what next funding round is going to happen with what company or what company is going to get acquired next, or who's going to go public? So we took all of this data, all of this historical data and combined it with data that no one else has access to. Things like our usage data, anonymized, of course, sort of looking at trends of investor flow or corporate flows and said, what can we learn from all this? And that's where we launched this sort of prediction engine that now is. It just wasn't possible a couple of years ago just because given that petabytes of data that we have sort of behind the scenes here. So it's pretty exciting times.

Yeah.

I'm curious, so is, are the models that you're using, are they like external models or is this stuff that Crunchbase is sort of developing internally?

Yeah, it's sort of a combination. Obviously we don't have the billions, tens of billions of dollars to go and build our own stuff, but we. So we certainly are leveraging the latest and greatest tech, as you might imagine.

OpenAI as part of that equation.

TensorFlow is part of that equation. So there's a lot of these latest and greatest tech, but we're building our own stuff on top of it. Right. So we're going in and taking a lot of the proprietary stuff that no one else would build because they're not crunch based and we're leveraging the pieces that we need to, to pull the right spaces. So in other words, how do you go and generate the content of how we present to the user? Sure, we're going to use OpenAI for that. But then the day there's still a massive machine learning problem that's hiding in the Crunchbase side, that it takes more.

Than just uploading it up to ChatGPT to sort of answer for us, it's.

Really fascinating to me, like what, what sort of like data points would you even look at to sort of predict the future? Because that's kind of what Crunchbase is trying to do, right? It's kind of predict what companies are going to IPO next, which ones are sort of acquisition targets. Like what are we looking at?

People already kind of use Crunchbase that way, right? Like in the past, that's, I think it's like evolution of the platform because, you know, for context, I lived in San Francisco for 13 years, did some tech startups know a bunch of VCs and people always check Crunchbase and they look at who's in the round or when did they raise around or what's recent news.

You're absolutely right though. The use case before was are they going to raise money soon? So they would go and look at a profile and say, well, it's been about 18 months, it's probably about time for fundraising. And, and that would be the one data point that they're using to answer the question. We're, we're using thousands of feature vectors to go and figure this out. So like an easy to understand example would be, is the company going to go and fundraise soon? So we'll go and say, sure, is it about time for them to fundraise? Then we'll go and look at the entire industry and we'll say, okay, well like how long does it usually take for a company to fundraise in this space for this size? But then we'll go a little deeper and we'll go and say, well, has anyone updated that company profile recently? And if the answer is yes, that gives us a little signal that maybe there's something going on at the company. And then another signal might be has investor flow to this profile changed significantly compared to the past? So if there's more investors looking at the profile, well, why, what would drive them to go and look at this profile? And then how are they looking at it? Are they searching for this organically, came upon it, or was it a link or did they come from a Gmail account? You know, so you have this sort of like two way sort of conversation happening on our profile. Then is the investor or the entrepreneur looking at those same investors who were looking at them? Well, that's a little signal. Right, right. So each, each one of these steps along the way gets us more and more confident that a funding round is maybe happening behind the scenes. And if it is, we can sort of signal at least some level of confidence out to our end users. And now that's just one again of thousands of these things. What's happening in the news, just how, how is traffic to the site in general? There's a lot of signals that we can go and combine together to sort of find the right pattern to say this is a company that's going to.

Fundraise soon as an example.

Yeah, that makes a lot of sense. I mean, just thinking about it, yeah, if a company's in there going and updating their crunch based profile and adding new information, they're probably doing that because they're expecting, you know, investors or people to be going and looking at that page.

It can't be, it can't be the only signal, but it certainly is one of more signals. Right. And when we, the nice thing about AI is we don't really need to figure out exactly the right combination. That means it, it's just like it can look at every company that's ever raised funding and historically, look at all the data we had collected at that moment to say, well, here's the 16 different paths of a company that might lead to a funding round. That's. And that, that gets me excited. Right, because this is, this is stuff that no one else can do. It doesn't matter which competitor we're talking about, they don't have that 80 million people using our site to go and drive and inform those prediction decisions.

Since you've actually pivoted to more of like the sort of AI based analytics of like, you know, figuring out where the, you know when there's going to be an IPO or who's going to raise or those kinds of things. Have you sort of like figured out the accuracy level of it? Like, how, how accurate has it been so far?

Yeah, this is maybe the biggest challenge is that when anyone ever tells you, hey, I've got a prediction engine, you're like, that's garbage. Because every prediction engine ever is garbage. So this is very different. And so we do do a lot of analysis. We can do a lot of back testing and sort of figure out how.

Would we have done. So you take sort of 2/3 of all of our data and you build the models on that and then the remaining 30 you use to sort of test to see would it have done it correctly had this model existed.

And by using that, that, that framework and looking at the kind of companies.

That we're trying to make these predictions.

Against, we've got something. There's, there's precision and recall and we have a 95% precision on fundraise and 99% recall. So in other words, when we make a prediction about a fundraise, it's 95% correct and we make predictions against 99% of the companies that match the criteria we're looking for. So it's ridiculously high how accurate we are now. Now you can get, you can, that's, that's the easy answer. The more complicated answer is as you add timescale to it, it gets much harder. So who's going to fundraise tomorrow is a very impossible question to answer. Crunchbase will do a better job than you guessing, but it's still going to be fundamentally a guess.

This is actually what I was going to say. So before we got on here, I checked a few companies that I know when they're fundraising. You were super accurate on they were going to be fundraising a bit off on the timelines.

Right.

So, so that's, and so we Put that in there. So we say, look, here's what we think, but we're never going to be 100% confident, oh, in the next six months it's going to happen. But we might say in the next 80, like 80% chance it's going to happen the next six months. Because that timing, scale, there's so many factors that are impossible for us to.

Know unless we're inside the brain of the founder to know if they're going to fundraise.

But that signal, getting back to a question you asked earlier, helps the use case of, well, I'm not, I'm an investor, I'm not looking at this company, but maybe I should be because it looks like current space things are going.

To be fundraising soon, or I'm a.

Large public company, I want to acquire this company, they might be going to fundraise soon, and I want to get them before they raise that money or increase their valuation.

There's a lot of different uses for.

Even just that one fundraising prediction among.

The now almost dozen different insights and.

Predictions that we have.

I mean, I think would be great to just jump in and sort of get a little demo, a little tour of what is capable of. And this is like a kind of a YouTube first podcast where we like to be really visual and show what we can. So love to jump in and just sort of get a sneak peek and give people a little demo of what it can do.

So this is the new homepage that's very different than the old homepage of yesteryear. And you'll notice that we're right up front saying sort of the new data is coming in. I think we've got something like 12 and a half million new predictions in just the last 30 days. So this thing is, this engine is constantly running, constantly updating, trying to find not just the next funding round, but also, you know, what is the next acquisition, what is the next ipo, which companies do we predict to grow? So we're really looking at, like a lot of different aspects of what a company is.

We've got this new sort of AI agent that can help you get sort of navigate Crunchbase, but you can just type in, you know, Blue sky as an example. And now we're looking at the Blue sky profile and you're going to see.

Right up front, this didn't used to be there. Now up front, we put some of the biggest predictions up at the top. So in Blue Sky's case, we think it's probable they're going to raise another.

Round of Funding, we think that there is likely that they're going to get.

Acquired at some point, which not a lot of people are talking about. And we don't think Blue sky is going to go public.

Certainly there's no signals that they are.

And then as we scroll down, we sort of took this approach to the profile page of, you know, what is the stock ticker equivalent of a profile? So there's, there's no way we can put valuation day by day over time, which is what a stock ticker does. But what is the private company equivalent of that? So we've got these things called heat score and gross score.

And you know, there's a little definition.

Of what these things are. But like looking at how the company is interplaying with the public web, how it's interacting with us, how is the, how does it rank among all the other companies within our corpus of companies that we track, that gives us these axes of data like this heat score and growth score, and that again drives other pieces of the application and even some of our predictions so we understand what's happening and what's going to happen next to the company.

You can play around with this and sort of make it do different whiz bang things. So if you're a data nerd, you can kind of get into this. And all the raw data is available in the API, of course.

And then as I scroll down, you're going to find predictions and insights. So here's where I can see. We predict they're going to be growing. We don't just say it's going to be growing. We actually explain in with our own words why we think that this company is currently growing and if there's a growth prediction, why we think it's going.

To grow in the future.

And then here's some of these predictions like we were talking about. Like here's we think there's a 37%.

Chance that they're going to go and fundraise in the next six to 18 months. There's a good chance they don't fundraise. Right. So we're kind of transparent that none.

Of these numbers don't necessarily lead up to 100% because there's still some percentage chance that they're not going to raise at all.

So we'll go and put that in there. Are they going to get acquired? We give reasons as to why in the API. We give all of the detailed reasons, right? So we actually get percentages and we go and say here are the drivers that, that, that are, that we believe lead up to this thing.

So as an API user, you can.

Discount things that we think are true that maybe you don't want to sort of incorporate into the prediction. Or you can just use a prediction score on top of your own prediction algorithms, which is what a lot of VCs do. They sort of use as an input into their own proprietary algorithms.

So this is some of the stuff that we're doing nowadays. There's a lot around, like we grok all the news that is happening on a company and sort of summarize it for you so you don't have to read 30 news articles to figure it out.

We sort of bring it all together.

So there's just a lot of different pieces that help you understand. And of course we still have the funding data, but that's all sort of just drivers now into these sort of.

Bigger, meatier questions that we're trying to answer.

There's a lot around even on that whole page, sort of seeing what's important, what's trending, what's happening in all the data that we're tracking. And you could decide, I want to look at these particular predictions for these types of industries and sort of get.

A daily feed of all the stuff.

That'S happening and what we think is.

Going to happen next in these companies, which is pretty exciting as well. So lots of interesting use cases. And again, you can always go and have a conversation with Scout, which is our little sort of dog fetching thing that will go and sort of do the sort of some of the logic stuff that you couldn't do in Crunchbase before, right before you couldn't figure out sort of what's the business model of this company or how does this compare to another company? Or how do public events impact these particular private companies? Now you have a way to have that conversation with Crunchbase programmatically, which is kind of cool.

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Yeah, I mean there's, there's meaty like policy questions right. Like so we see whatever someone says something happens in the government, you know, people come to crime space, they go type in how are tariffs going to affect this company and, and, and, and it will do a pretty good job of sort of speculating and sort of figuring out what, what's going to happen next.

I think those are, those are some.

Of the interesting ones and then just.

The, the analysis right.

It's, it can be hard like if we go over to AI search builder so now like you can just natural.

Language in your, your query and it's going to go and build sort of.

These very complex searches because you know.

You think about multi joint searches and how to build those.

It's always been sort of cumbersome. Now you can say show me all.

The CEOs at companies where they used to work at Salesforce and then they went to Stanford. You know like you could type that.

All in in a huge run on.

Sentence and it will go and show you exactly which companies do it because it builds the join for you and just the lat alone is a huge time saver for our users.

I wonder what this is going to do to like startups. Like it feels like it's going to like really increase like the velocity of rounds. Like how, how fast rounds will you know, close because you know you guys are kind of creating like the ultimate like FOMO machine right where we're like people are like oh my God, look, it's a hot company. They crunch base just told me it's hot. I got to get in.

Yeah. I mean anecdotally there's hard for us to have data on this but, but anecdotally we've heard that when we go.

And signal that is very imminent that a company is fundraising that they get.

A lot of inbound interest from investors because there's now awareness that it's happening.

Oh you notify people or we don't.

But people set up their own alerts, right.

I can just say, you know, show me biotech companies that are very likely to fundraise. Right. I can just make that search. It's going to go and you know this is a live demo, we'll see.

But there you go. So it did, it said industry is.

Biotechnology funding predictions here is very likely.

So these are all the companies that.

Are very likely to fundraise soon and.

We can go and, and, and create.

An alert off of this, right. So you've got investors who are subscribers of ours, say when a new company.

Shows up on this list, shoot me an email, right? Go and let me know that that's happened.

And so we don't need to set them, send them emails, they'll get their emails themselves because they've set up their alerts the right way.

Yeah, that's awesome. So I could do like AI coding or something like that and I could just like as soon as some, you guys have a new prediction in that category, you guys will email me or something. That's amazing.

Yeah. And that's, and that's just one, one of the predictions, like another very common.

One is I'm looking for these sorts of companies to acquire. Let me know when a company of.

This size, no bigger than Series C, I don't want them who's raised more than $100 million, whatever it is, when a new one shows up is very.

Likely to get acquired.

Let me know because I'm in the.

Space of acquiring those companies. So a lot of corp dev departments get excited about that.

Now all the companies that are in here, are they all, are they all like self added or is it is all the data sort of pulled by Crunchbase?

In 2014 it was 100% from our users. Today it's about 5%. So we've sort of transitioned but the brand belief is that still like if you ask our users where do we are there from there mostly we'll say it's user generated content, but really that's only for the smallest companies, ones that haven't had a news article yet, no one knows they exist, they go and put themselves in, they sort of announce themselves on Crunchbase. But the reality is we have, we invest tens of millions of dollars now.

Every year into getting data from a lot of different ways, lots different sources. We have 5,000 partnerships of data coming in. We of course go look at government filings. We've got lots of data partnerships to go and give us seed data. And then we have our own AI.

Systems that go out and find the data. So if we hear about a company that we don't is in our, in.

Our data set, we very actively go and fill out the profile as best as we can assume it fits a certain set of criteria. And that plus the engagement data, plus the generated data. Right. Like the biggest source of data now is Crunchbase generating its own data on the data that we have. That is that huge, huge, huge data set that we've collected over the years now. But not to devalue the user data entered the user generated data. That's still very important stuff, right?

Yeah. Because it just seems like it could be such a really good discovery engine for very small new startups. Right. You know, you want to make sure you're in Crunchbase because then you're sort of in that algorithm, you're in that system where now people might discover you if they're looking for, you know, small startups in X niche. Right. So that, that's why I was curious, like can companies just go and sort of input their data in there to make sure that they get discovered when people are making those sort of queries?

Yeah, they absolutely can. And, and it's. And we do a bad job of this like giving reasons why you should. Because they asked you get discovered by VCs who wouldn't normally have found you. And if you're like, well, I don't need, I'm a bootstrap company. I never want venture funding. I don't need to be in Crunchbase. Job Seekers is like a good solid 10% of our users are going and researching your company to see if it's a company you want to work, they want to work at. If you're not in there and it's not up to date, they're like this.

This, this isn't something I want to go and participate in. Because they couldn't grok your website or they couldn't find your website or whatever the case happens to be, there's a.

Good chance that Crunchbase's profile comes up.

Higher than the website of the smaller company. So it's usually a good idea to have that data correct In Crunchbase.

It used to be traditionally like VC backed companies that were mostly on Crunchbase. Right. Is that still the case? Like, you know, are there companies or private companies that are non vc? Like you just said that you suggested that they should do that. But also like I can see this working for even like public companies. Like just like a general tool to help me guide my investments in companies in general.

Yeah.

Would be awesome.

Another, another brand challenge that we have. So, so about. There's about 300,000 companies have ever received funding. We have about 4 million companies in Crunchbase. So that, that math is very different because there's a lot of companies that never get to funding. There are a lot of bootstrap companies, there are a lot of companies that will do acquisitions that we had not seen before that we'll go and put in Crunchbase and then every public company is in Crunchbase. So we do track all those things. Now are you going to get all the information you want to about a public company? Probably not. Like you should go to Yahoo Finance or Google Finance or whatever the latest agreements is because there' public data out there that we just aren't going to track because it doesn't apply to private companies. But for the data it does overlap for sure. So for instance, when like Virgin America back in the day that was a hot startup airline so it was definitely tracked in Crunchbase. Alaska Airlines was not tracked in Crunchbase, but when Alaska Airlines bought Virgin then we started tracking Alaska Airlines. So like this ecosystem and sort of the spider web of how companies are.

Interconnected with one another expands, expands, expands and we'll just keep adding the company.

Said, but we're not going to put.

In, you know, Joe's Pizza Shop on the, on the corner because it probably isn't relevant to our community unless it's got some cool tech.

I can say do you see value for like, let's say you're not a VC or an angel investor or something like that. Do you see value in using Crunchbase for just like, you know, general people that are interested in investing that. What's the value of Crunchbase to the people that can't write big checks and get into like early startups?

Sure. Actually it's a minority.

Part of our users are actually VCs who have funding that want to go invest. So that is an important use case to us. But it's certainly not the biggest. You'll see use cases across sales. Let me go and find the companies that are going to have money soon. I'm going to go and start a sales cycle with them is a good time.

And it's better than waiting for the fundraise to happen too because everyone knows when the fundraise happens, if you can know it six months, a year in advance, maybe you can sort of get entrenched earlier than that. So that's a pretty big use case for us.

I mentioned Corp dev. Right. Anyone who's buying companies. That's important.

We do have, on the, on the self service side we do have job seekers who go and are paying us to say I want to find hot level companies in my area because I want to be there at their early stage. Right. So you've got that use case pretty heavy. A lot of researchers and analysts and.

That from the largest consulting firms all.

The way down to students who are.

Trying to figure out sort of some interesting Trends.

So you've got a lot of that sort of use case lurking in Crunchbase.

You know, it's really exciting to see how many different people have different uses for that private company data.

Right, right.

I know there's, there's Crunch Fund, which has no connection.

Right.

Like, you know, I still get confused. TechCrunch Crunch Fund. You know, I chatted with Michael Arrington back in the day. I have a crazy story where I was raising money for a startup and we had a call and he's like, I'll be right back. I gotta go give milk to my baby goat or something like that. I'm like, okay, cool.

We kind of like became like Twitter friends after that and occasionally chat, but so my mind still connects all that, but. But it feels like you guys, you know, you guys have all this data, you know, which companies are going to raise money. Some somebody should be like piggybacking off of that, like making a lot of money off this. Like you guys have all the data, find out the right companies, get into them, even like small allocations.

Yeah, I mean, we've toyed with the idea of doing ourselves honestly.

Like one of the ideas that we have lurking out there, it's on the roadmap for not this year, but maybe some future year is. So you just, I showed you a profile that has sort of our equivalent of a stock ticker.

Right.

Which is this growth in heat score. What if we aggregate those? Right. What if we do that across entire industries? So now you've got the AI, heat.

Score and growth score over time.

We're using our stuff, we're predicting is this moving up or down in the right direction.

What if we worked with maybe a secondary provider and created a little index for those companies so you could invest in some subset. Right. Elaine, the retail investor, maybe start playing around with this. It's still an idea. We've sort of had some early chats about it, but you'd sort of stumble.

Into a lot of regulatory issues pretty quickly. And the ROI is way out there. Right. So like, yeah, we could start a fund.

It's a ten year thing.

We're a little bit more focused on the present than that.

Far out.

Yeah. I'm kind of curious about like what the sort of future of investing looks like. And I don't necessarily know the exact question to ask because I don't, I don't know what, I don't know when it comes to like AI and investing, but I'm trying to figure out, like if the general population has access to the information and like what's likely to sell next. And you know, this, this information is, for lack of a better term, democratized. Right. The, the common folk could have access to the data. I'm curious about what the world looks like as we move closer and closer to that reality. I'm just curious if you have thoughts on that.

Yeah, I mean I think there's a lot of potential disruption across a lot of different industries.

I think data is included in that and that's honestly why we moved the way we did. Like I would argue funding data is already commoditized. Right. So like yes, we, we think we have the best. Yes, there's a lot of.

But if we just kept resting on.

Those laurels like that, that company goes out of business when all of the data gets absorbed into our LLM masters, right. Like they're going to, like there's going to be, you know, what else is there.

Once it goes in, it's not going to come back out.

So, so facts are a dangerous business to be in.

So speculation and predictions is at least dynamic and changing.

I think, I think a lot of data companies are going to be thinking like that.

If you deal even in, if it's hard to get facts or you've got once it, if you lose it all, if someone takes it on uploads it.

Is your business in trouble or not? That's the, that's the question I think everyone should be asking now to the broader question, you know, of how does it affect the entire industry?

Generic tools that do not have proprietary.

Pieces of the story are going to be very, very hard. First mover management isn't going to be a thing. It's always going to be a challenge. So how do you go and build a thing that is uniquely yours? I don't know of how to do that. Unless you are building the foundational models, right, like you are the open AI and everyone's on top of you or.

You'Ve got something that truly changes all.

The time and is only available to you and it's critical to people's business workflows. I don't know how else you survive. It's a, it's a. You know, I go to a lot of these AI conferences and I see a lot of people like building AI on top of their product. But I really think there's a day, not in the, I mean replace almost there where you can just describe the thing and it's going to build as good as the thing that you have. As long as you've Got a good product manager with a good set of ideas that tech is not that far away. And then you just fast forward five years, it's going to do, it's going to suggest things to do to beat the competition. Right. It's going to code it for you and build the features so all of.

That becomes commoditized essentially.

So there is now it's just like companies are going to go back to building their own internal tools because their customs bespoke for what they need rather than trying to fit into someone else's package.

So in those scenarios you've got to.

Bring some other value other than that into the equation. And that's why being a data data company that has some stuff that no one else has feels pretty good for that long term vision. But I'm biased.

You also mentioned that that Crunchbase has an API as well. So I mean that that API can sort of work into your own sort of proprietary stuff. I'm sure there'll be people out there that figure out some like really good prompts and really good data points to look at to sort of make their own predictions and then not want to share them with the world because, you know, that's their sort of little secret sauce that they figured out.

Yeah, almost every major VC now has.

Their own data science team and we have conversations with them saying, hey, how would that API feed into your team?

We don't want to replace that team.

We'Re just going to supplement them. It's really exciting to sort of see the innovation and see how people incorporate our data into their own tools to make them successful.

Another angle we're thinking about is how do we take what's happening in public markets and interlock it with private market data.

So for instance, if a certain set of companies, let's say biotech companies, are suddenly the stock market is tanking and they're doing really poorly, how does that.

Affect the VC market? How does that affect our predictions? Right. Like there's these external influences and then can we report on that and say, look, based on what we're seeing in these sort of external, sort of public markets, we think we predict there's a cooling happening on this side of the house and that and those dollars are.

Going to get redirected to, you know, whatever, whatever the other hot trend is at the time, like we can start.

Making more of a commentary on what's.

Happening in the world then just leave it to others to interpret.

I do think out of the use cases you mentioned, like for me the sales one is super interesting. Like, if I could talk with AI and like, okay, I've got a marketing agency and maybe, you know, you could even get really detailed. Like I went to Stanford, maybe like look up startups that they went to Stanford, so I can like bond over that. Like, if you could get like really detailed like that. Right. And then reach out like, hey, we both went to Stanford, we both went wherever and then start conversation, I think that could be like super powerful for a lot of people.

Yeah, that sort of. That, that we're trying to change the definition of what an ICP is. Right. Because. Because look, here's exactly why we always.

Sell to successfully and maybe it is I. And we also went to school together.

Those are still historical facts. So the thing we're trying to change in people's minds are, is, is it the right time for you to talk to them? Cool. Here's this list of companies which is the right one to talk to you right now. For this particular accounting executive.

Right, right.

So this account executive historically has been great at selling these sorts of deals. Here's companies that match the kind of companies that they would successfully sell to. But here's ones that we are predicting are going to grow, growing quickly. They're probably fundraising in the next 18 months. They're not going public and they're not going to get acquired because that would distract the sales cycle. So you can kind of like sort of tee up. This is the right time for this. This ICP is the one you want.

To talk to right now.

Yeah, that's. That's worth a lot.

Hope so. We'll find out.

Now, can you use Crunchbase to sort of discover emerging markets as a whole? Like, obviously you can look at in a specific market and find the companies that are sort of, you know, making moves in those industries. But can you find like, you know, you mentioned biotech and you know, had you known like what AI was going to do over the last, you know, six or seven years if, like, is there. Are there any ways to sort of see that stuff coming a little bit sooner?

Yeah, it is one of the, one of the harder to find features in Crunchbase, honestly. So, so it's really possible to do. There's things called hubs and not many people know what even the hub is. But we basically took every major piece of metadata that has data.

So for instance, like industries.

So we have every industry that's one axis, Geo is another axis.

You know, there's gender, there's a founder.

There'S stage of company, how much they raised and we basically intermixed that. So we made these pages. So somewhere on Crispies there is a sort of female founder in crypto in.

Europe, there's a page for that.

And we originally did it just for SEO reasons, but what that also did is it collects all the data. So it's like, here are all the latest funding rounds, here are all the people, here are the companies, and they're all ranked by which ones are trending the most in Crunchbase. So you take that and then you say, well, if. So, every single thing in Crunchbase has a rank. So hubs have ranks. So which is the hottest one right now, today, based on what's happening in CrunchBase, there's a first, second, third, fourth, all the way down. For every single combination of the, I.

Don'T even know, 10, 100,000 of these.

Different hub pages that we've created, then you can go and say, well, which ones are trending? So if you start looking at which ones are trending and which ones had the low rank that are trending upwards quickly, that's where you get to see which of those combinations is the hottest. So you'll find some really just fascinating.

Things lurking in there. Some of them is going to be weird, you know, but, like, I'm just. I'm just playing around with it right now. But, like, like Taiwan artificial intelligence companies are on, like a hot thing right now, you know, like. Like very.

I love that. I used to live in Taiwan. It's great.

Okay. Yeah.

So there's. So there's these little pieces of. That you wouldn't normally otherwise know. And I think if you were a savvy investor who was really trying to figure out what is an emerging trend.

Or even just, again, an analyst or even a journalist who. They also use French base. You can find some interesting things lurking in Hub pages. I think it's just. It's a sleeper feature that we have.

Very cool. Well, this has been an absolutely fascinating conversation. Like, you know, I don't want to be like a salesperson for you, but I'm actually a subscriber of Crunchbase. I actually do have a subscription and I get in there and I play around with the data from time to time. But no, this has been a great conversation and I really appreciate you taking the time to hang out and give us the demo and everything like that. You know, obviously Crunchbase is the place to go. Crunchbase.com, if anybody listening wants to go check it out. Is there anything else that they should know? Any other, you know, places they can maybe follow along with you. Anything like that that you want to shout out before we wrap it up?

Yeah, I mean, follow, follow crunchbase on LinkedIn. I think that's probably our top channel of sort of sharing stuff out.

And you can follow me on LinkedIn as well because I usually leak roadmap stuff.

So if you want to see what's coming before it does, my product teammates.

But I usually will post stuff about what's coming soon.

Awesome. Jagger, this has been, this has been great. Thank you so much for hanging out with us today. And for anybody listening, if you like content like this, make sure you like this video and subscribe wherever you listen to podcasts. And thank you so much for tuning in. Hopefully we'll see you in the next one.

Sam.