---
title: "GTM Product Demos: Exploring Ocean.io"
episode: 29
podcast: "The LeanScale Podcast"
publisher: "LeanScale"
guest: "Michael Heiberg"
guest_title: "Founder & CEO, Ocean.io"
date_published: 2025-10-28
date_modified: 2026-07-22
duration: 00:27:04
word_count: 4094
topics: ["outbound-sales", "ai-in-gtm", "gtm-strategy", "demand-generation", "revenue-operations"]
canonical_url: https://leanscale-knowledge-hub.netlify.app/podcast/michael-heiberg-ocean-io-gtm-demo/
source: "LeanScale Podcast Knowledge Hub — https://leanscale-knowledge-hub.netlify.app"
license: "Free to quote and cite with attribution to The LeanScale Podcast."
---

# GTM Product Demos: Exploring Ocean.io — Full Transcript

> Episode 29 of The LeanScale Podcast, with Michael Heiberg.
> Published October 28, 2025 · 00:27:04 · 4,094 words.
> Machine-transcribed and **not diarized** — speaker attribution is inferred, so verify
> attribution against the audio before quoting a specific person.
> Structured breakdown: https://leanscale-knowledge-hub.netlify.app/podcast/michael-heiberg-ocean-io-gtm-demo/

## 00:00 — Welcome from the new LeanScale Studio

**[0:00]** (logo whooshes) - Michael, we're so excited to have you here today. We have the founder and CEO of Ocean.io, one of the most exciting AI companies in go-to-market tech. Michael, thank you for taking the time to be here. We're also in the brand new LeanScale Studio, and I think it's an amazing way to kick it off. So appreciate it. I know there's so much going on in this space, but the way you've approached go-to-market and leveraging some really powerful AI techniques is really, really exciting for us. I wanna know, and I think our audience is always interested in hearing, what kicked off this idea, and how did you get the idea to found Ocean?

## 00:41 — The origin of Ocean: from lookalikes to vector search

**[0:41]** - Yeah, sir, it has to take us back to around 2018, 2019, and we were experimented with the ability to do localize for companies, and what inspired that was basically the whole look-alike concept out of the BTC space.

**[1:01]** We wanted to be able to clone your best performing customers, and as you know, the traditional way of doing that didn't really work out. So we experimented around with keywords, it's not precise enough, and then 2019, 2020, we stumbled into the whole natural language processing and contextual understanding of massive tech bodies, and that got us on a trajectory where we are today, but it was long and hard work because we were on the leading edge of technologies in terms of vectoring databases and what is translation and multilingual abilities and everything else, and the first two or three versions of this the whole way up to 2022 was not really workable.

**[1:52]** It required rocket science to basically do it, and so it's not until late '22, early '23, it became really robust, and that's where the company as such started to take off. - That makes a lot of sense, and you said you took some of the inspiration from B2C or B2B companies. How was that initial adoption for those teams? I mean, when you got into the market, what was this just general concept? How did people take to this type of idea of finding lookalikes for their customers to go target those people? - Our biggest problem and our biggest challenge in the early days was actually explaining why we are different and how we are different,

**[2:41]** and we tried several iterations on our landing page. Here's what we do and explain and then everything else, and it didn't really work out for us, and we were only used by very dedicated companies like your IOTs and things like that that was looking very niche-oriented customer segments. Then we had an epiphany, and that was basically, why don't we put our technology on the landing page? Just bring it up there, and then for people to test it out themselves, and that was pretty much the part where the whole thing started to move. We didn't have to explain anything. People keyed it in. We had the wow moment within three to five seconds

## 03:28 — The landing-page epiphany: put the product in front of them

**[3:28]** because they had never seen anything like that before. I just wish we had done it earlier, but that was really what made this whole thing take off. And then obviously our self-sign ops or PLG, the ability to, you don't have to get a demo on this. You can just demo yourself on the landing page, and from there you go into a free trial and test it out on your own data, and that is really what got Ocean going big time. - Yeah, I think we push all the companies that we work with to implement a PLG motion as much as possible, and that's why sometimes there's so much complexity in what's going on. It's tough to explain it. You just have to see it.

**[4:14]** - Yeah, I can only advocate for it. That made the difference for us. Explaining it on a landing page or in all kinds of content, it doesn't really make any sense until you key in your best performing customer and find the exact local likes, and among that local like list, you can see four, five, or six companies you're actually trying to target because you know it's the same thing. That validates the quality of what we do. And it was like no discussion beyond that point. It's spot on. - Yeah, that's really interesting 'cause we talk a lot about go-to-market ops as a way to increase distribution before big tech or other technology companies

**[4:54]** build that technology. So for you, you got a chance to basically use this motion to get it into more companies with less friction, and that's what opened it up. You just got them to use the technology out the gate. - Yeah, exactly. We have, I think we have something like five to 1,000 onboardings a week and on a free trial. We would never reach that by demoing it and everything else. And it puts a lot of things on the line for you as a company because people has to get, and we talk about moments to wow. How long does it take for someone logging onto Ocean and before they get it? And we can actually monitor that based on the searches they do.

## 05:41 — 'Moments to wow': two minutes down to 30 seconds

**[5:41]** And we went from I think more than two minutes down to 30 seconds. That means within 30 seconds, on average, our trial users, which is to the June or several thousand on a monthly basis, spend around less than 30 seconds or above that to get to that. Whoa, we get it now. And that's very powerful. That is one of our key metrics for success in Ocean is to make it so easy, intuitive that you can get to that. And that's a rippling effect throughout the company. Once you put your whole thing off in front of someone who has no other commitment, then there are time spent on your application. - Yeah, that's amazing. That's amazing.

## 06:35 — Demo: company + people lookalikes in one search

**[6:35]** And I think in the spirit of moments to wow, I know Joe and I are really excited to see what you prepared today. And we can't wait to share it with our audience. Can't wait to share it with our customers and using it for ourselves as well. - Thank you very much, Anthony. This is actually the newest version of Ocean. And what that does is it takes what everyone knows Ocean for the company Lookalikes, which is 65 million companies with that has been pictured in a way that you can actually find the perfect Lookalikes. On top of that, we have built a complete victory of 230 million people's LinkedIn profile. That means we have run embedding and victory

**[7:21]** on everyone's in this rooms and everyone pretty much on this calls LinkedIn profile. That allows us to combine two things into one search. That means if I key in the argument for one individual, which is their LinkedIn profile, then I will be able to find the Lookalike origins based on that individual's title and skill sets and everything else in the Lookalike companies that individual look work for. And let me show you an example of that. If I go to Crystal Hergar from DocuP, DocuP is in their e-signature space. And if I go back to Ocean and kick off a company Lookalike for DocuP,

**[8:22]** this is what you've seen before. This is Ocean finding the exact Lookalikes to DocuP. That means all the companies that is the exact Lookalikes to DocuP. If I then go into the new aspect of Ocean, that is basically taking the LinkedIn handle of Crystal

**[8:48]** and do a...

**[8:55]** You see what I just did? Well, based on that LinkedIn handle, I'm now able to find the Lookalikes to Crystal among the Lookalike companies of DocuP. See, I then have the ability to limit it to, let's say, I only wanna see three individuals. I can then include a CMO into this from also a document company. And the moment I can then build my origins into this, I basically start to expand it into in one argument, which is the LinkedIn handles of these individuals, I now build my entire target origins. Obviously, traditional filters like location, like size of companies and keywords and everything else you know is in the system.

## 09:56 — Preview, then export to Clay — no credits spent

**[9:56]** I can target it and refine it and by the way, I haven't used any credits yet for all the clay users out there. I haven't used a single credit yet because I'm refining my search. Well, now comes the trick here. I can do many things with this. And I can actually, let's say I wanna save the first 50 just for the sake of, I have more than 10,000, but just for the sake of argument for this demo, it takes a little bit to stop a large file. I now have the ability to, I can basically save it and I can also enrich it or I can actually export it. And here's where I can export it to anything you wanna export it to. And again, I have not used any credit yet.

**[10:49]** The moment I press play, I know this entire target origins directly into play. And from there, you basically, as you can see in the bottom right, you can see it's being loaded over to play. Now I take that entire thing. This is like finding your target origins, honing it in, doing all the filtering, preview the whole thing. Once you're ready, you load it into play and now it's over in play. You see, that is taking this whole thing to a very different level. And we all know that Ocean works inside play, but this, where you actually have the ability to really go into an origins builder capability. And if you go to any sales or any marketing,

**[11:41]** they know exactly who they sell to. Drop in their LinkedIn handle and you get these results in. So guys, what do you say? - That's incredible, that's incredible. It is such a difficult problem to solve that you're able to do in literally minutes and then completely integrated in all the systems that you're using today. It's really, really intentional. And I love that about it. And this is something, this is the type of work that we do with our customers every day and the type of work that we do ourselves. You're looking for the right companies. You know there's gonna be some firmographic components that are relevant to what you're offering.

**[12:19]** But then at the end of the day, you have to find the people that you can relate to and sell to and the people that feel the biggest pain. And to be able to combine that into one easy search, bring in your best customers to find the local likes and then put that into your systems, assign them out, start your outreach, start your way to getting in front of them. It's very impressive, very, very impressive. - If you put a mic in front of anyone, they will know exactly who they sell to. They'll know, yeah, I sell to John, I sell to, you know, they know them. And now it's basically just dump them into Ocean and you have the local likes.

**[12:59]** And for any agency out there, they can adapt it to their target origins per client. And it's extremely powerful and you can save all the searches. You can fine tune the searches and you can actually then load it into whatever, you know, CRM or whatever clay you wanna load it into. And we see that as a game changer in the market. You know, obviously we are first mover again, we were first mover on the company lookalike and now we overlay that with the entire victory of LinkedIn on top of that. So we think this is gonna be a game changing for the entire go to market. And by the way, on our API side, this has been available for some months.

## 13:50 — Building on the API: agents, CRM, and inbound

**[13:50]** You saw Freggle actually released this a couple of months ago. And can you imagine building an agent on top of your CRM system, which is what copy AI is doing, using this ability to on a close one, you can basically pick it directly up from the CRM from Salesforce and then initiate a lookalike search on the individual or on the company or both. And what you get back into that agent is the perfect target audience. You could do it on your landing page. Guys like RB2B, they resolve all the inbound traffic, we can put it into context of your best performing customer. All these things is what is going out on the our API side. But that's to give you a little bit of

**[14:41]** for what we're doing at Ocean right now. - Yep, and I think you also, for all the clay users out there, it sounds like starting in Ocean now could be a better workflow if you're trying to do this. So I think that's a really big takeaway as well as you don't have to go and put this into an individual column or so. Start in Ocean, kick it to clay and then keep the enrichment process going. - I would lead it up to the individual users who have seen this, if they believe this is a smarter, more smoother way of doing it, easier way of doing it. And everyone knows it's the preview that will show you whether you're on the right track.

**[15:29]** And those preview, you need to be able to validate. And once they are validated, then you know your list is correct. And then you can do the enrichment. Why do you want to enrich something that is not on target? You don't want to do that. You don't want to spend credits on that. So I think your spot on. - If it were me, I would start the workflow in Ocean to get the preview. Because I do think there's some experimenting that you might want to do. Like you added a head of growth, then a CMO, then maybe DocuBee, but maybe it's not just DocuBee. Maybe there's some other ones that you want to kind of relate to start to build a different persona or profile.

## 16:09 — Why vectoring beats title-based targeting

**[16:09]** And then once you feel like, hey, yeah, these results are starting to feel like what I'm looking for. Then pump it into Clay and start using your credits on other things. - Exactly. And as you can see, anyone who has used title searches and everything else, it gives you really limited because it goes only by the title. It goes by the picture of those individuals. That means you get a much more, or, you know, origins that is on the level you actually want. Because in some companies, on the bigger companies, they are cheap marketing officers. In smaller companies, they have VP of marketing or head of growth. That is what Victoring does.

**[16:48]** It doesn't tie you into a title. It ties you into a contextual understanding of what that individual does for this company. That's the big difference, huge difference. And you can look at the titles if you go through it, it relates to the size of the company. - No, and that's something, normally we're doing this manually. We're saying, okay, I need to do a profile of, if it's the size of the size, I'm looking for these titles 'cause they're typically running sales. They're typically running marketing. And then if it's at this stage, I'm actually looking maybe for like a director level 'cause maybe they have the budget for this thing that we're selling.

## 17:23 — 11X and the two generations of GTM AI

**[17:23]** So I think this takes so much manual work and investigation out of that process. - If we look to Think, which is a very small company, they don't have the VP. They have a head of marketing. But that is their decision maker within the marketing sphere for that particular company. That's what Victorin does for you. - So Michael, I know something that's, it's been in the news a lot right now, quite a few headlines on 11X and what they're doing and how they've approached their business. I think from your perspective, how do companies like 11X relate to what you're doing and relate to the future of AI and where AI is going?

**[18:10]** 'Cause I think you planted a lot of seeds during that demo of some of the agentic capabilities that you're gonna be building the foundation for. From your perspective, where do you see this all going? - I think we're moving fast into second generation right now. I think first generation was, let me put it in popular terms, was basically an LLM wrapper round analog database. Why is that significant in terms of using the processes, the go-to-market processes? That means you have a non-normalized, and now I speak, data science speak, you have a non-normalized data set where you put a pretty face on it, which is what the LLM produced for you,

**[19:01]** but the processes and the targeting of origins is not perfected. It's very broad and it's based on that analog database. And I see, and it's just, we have more than 40 donors using Ocean inside their application. That means they're using the Ocean logic inside their application. And we see a massive wave going into those second generation agents. And that is where there's a much better understanding of the actual process that we actually have to support. And a process automating the whole flow when copy AI is a good example of it, where you can actually take straight out of a close one and do magic with it. I think this demo illustrated that.

**[19:57]** If it's an API supporting it, I see those processes now being starting to be interesting for people, because it's the first wow effect of, I'm speaking to a robot or it's a robot doing the whole sequence. I think we're moving into much more human interaction with an automated flow validating and moving on to the next automated flow. I don't believe we are there and for the foreseeable future, where unless it's a very, very target audience, I don't think we are there where we can actually fully automate it without any human interference, write the right copy, embed it into the right sequence, perfect target audience,

## 20:47 — Micro-targeting over mass targeting

**[20:47]** and make sure it reaches the right individuals in the inbox and in their LinkedIn profile with the right message. I think that needs finessing, but once it's finessed, it runs. And that's where I'm a big advocate for micro-targeting instead of mass targeting. What automation and AI really does well is micro-targeting. Overlay all this information with third-party data, intent everything else, and then you have a perfect audience for who will actually convert, not in the 0.1%, but actually in the 5 to 10%. Because what you send to them is highly relevant. And doing micro-targeting on scale without anything but an agent, I think that's where we are moving.

**[21:49]** Does it make sense? - It does make sense. And I had a follow-up question because I think this is something we worry a little bit about in the go-to-market space. I think these capabilities are being available to the masses now. And we're seeing just the sheer volume, even when it's micro-targeted, the sheer volume of communication and being targeted, really being amplified. When everybody has access to these capabilities and tools, and I think what you mentioned, keeping the human in the loop can help finesse this a little bit. But how do you stand out when the playing field gets to the point where everybody can kind of do this at scale,

## 22:36 — Standing out: the cold email that got read

**[22:36]** how do you stand out to still be competitive in that new environment? - I got an email the other day, and I think I get 20 to 30 cold emails a day. And most of them end up in my spam filter, but I actually got one I read. And it was very obvious that the script was run through any of the LLMs, chat GPT or whatever. But it was, that script had been run on my post on LinkedIn.

**[23:07]** And it was run on my text on description on LinkedIn

**[23:14]** because it was spot on. It was spot on what I stand for, it's spot on what ocean stands for. So the first five, six lines, which is unheard of, describe who I am and why they wanted to talk to me. You see, that contextual understanding of who I am

**[23:36]** and that communication to me with spot on, it was borderline flattering, but it's, anyway, exactly, of course. - That never hurts, I don't mind. - That's why I read it. Yeah, that's why I read it. No shame in saying that, no shame in saying that. But it was like, you know, that was interesting. That was an interesting, wow. Because intellectually I was stimulated, how did they do that? How did they get it so precise? And I decided to figure out the algorithm behind that, which is interesting. But those kind of, that's because that's really what we do with ocean. But it's, I think, when it gets to that level

**[24:23]** of contextual understanding of who you communicate with and what business I'm in, then it becomes relevant. And that's back to the micro-targeting aspect of this. You do not do this on 5,000 emails. You do this 5,000 emails. But the open rate they get, I don't know how the underlying data bike, I tell you it's more than 20%. - To all the people out there, spamming and trying to get around the hundreds of emails per day. I think, yeah, we're seeing that does not work anymore. So, you know, hopefully we'll save you some pain trying to get around that spam filter. But I completely agree with you and appreciate you showing us how people

## 25:11 — Where to find Ocean and what's next

**[25:11]** can actually do this in practice. I think you're allowing this to happen with ocean. And yeah, I mean, before we sign off here, if people are interested, reach out to you, reach out to the team. What's the best way to get in touch with you to learn more? - Yeah, you know, test us out. This is the best thing. You know, people, there's some people who prefer to communicate on a demo, others actually just wanna see how it works for them. There's no barrier here. There's no, you know, you don't have to, you know, just go in, test it out. And I actually meet a lot of people who have said, oh yeah, I went to your landing page, this is amazing.

**[25:51]** They might not be in the market now, but when they are in the market, they come back. That's what we see. Yeah, then obviously you get the, you know, LinkedIn operation, I just close to new meetings and all these kind of things. Thank you very much. I appreciate that. I obviously appreciate that, but it's down to targeting. The more precise you can do your targeting, the more precise you can do your messaging, the more precise you'll understand what a founder does for each of these company. If that is your target audience, the more precise you can write that copy. - Amazing, amazing. Well, sounds like easiest way. You can see it in Michael's background.

**[26:26]** You can see it on our big board back here. Ocean.io, go test out the product. Take a look for yourself. You've made it so intuitive and easy. And really just appreciate you being here with us, Michael. And can't wait to see what you guys do next because you've been pushing the limits so much since the beginning. I know there's more in the future too. - Yeah, there's a couple of things up to sleep. I tell you, there's a couple of things up to sleep, but there, that is for, you know, we can meet again in a half year. Then I have a couple of things to lay on top of this. - We'd love to have you back. - Thank you very much.

**[27:00]** I really appreciate this and thanks for the opportunity. Amazing. Thank you, Michael.