Omni Analytics raises $120M Series C at $1.5B valuation to build the all-in-one BI platform for the AI era

Speaker 12: the waiting room. Let's bring him into

Speaker 2: the TBPN Ultradome. Colin, how you doing?

Speaker 14: I'm great.

Speaker 2: Welcome to the show.

Speaker 14: I can do a two handed drink for you.

Speaker 15: There we

Speaker 2: go. We go. He's reading chat. This is great. Introduce yourself. Introduce the company. Tell us the news.

Speaker 14: Yeah. Colin Zimmer, CEO at Omni Analytics. We are building the next great data platform. So a product that can do everything from AI to spreadsheets to dashboards. We're trying to consolidate all of BI into one tool Okay. And we're doing it on top of a semantic layer. So a a layer that can explain how everything in the business works so that AI does stuff reliably.

Speaker 2: How generalizable is the semantic layer? Like, has got to be some trade off because the different businesses have different ontologies. How how are you thinking about creating something that's flexible but simultaneously uniform?

Speaker 14: So I mean, on the one hand, a semantic layer is actually pretty straightforward. It's just a bunch of text about the business. Mhmm. So if I'm gonna go write a a semantic layer for ChattyPT or Claude Sure. Right now, that is what the skills framework looks like. It's a document that has lists of stuff about the business, and that is completely unique to every single company. On the flip side, we've had semantic layers in data for fifty years at this point.

Speaker 2: Yeah.

Speaker 14: And it's the specifics of how all the tables in a warehouse fit together. And what we're doing is just bringing the like, bridging those two worlds together. It is completely specific to every business. Yes. Exactly. Like a Snowflake or a Databricks.

Speaker 2: Yeah. So are I mean, I imagine that there's still a world for databases in the future. How are you thinking about that? Do migrations get easier or changing schemas get easier in the future? Does this get harder because the data is just so much more? How how are you thinking about the evolution of databases and how you'll use them?

Speaker 14: Yeah. I mean, using sort of public trading as a proxy for this, it seems like the data warehouses have found, like, a nice middle ground in between the, like, the SaaS apocalypse

Speaker 16: and the

Speaker 14: pure play AI companies. Yeah. Like, we need somewhere to hold all of the data. Yeah. And so I I think the databases will probably be here for a long time. Yeah. I I think what has gotten interesting is that picking up your data infrastructure from one tool and moving it to another tool has gotten substantially easier. Like, SaaS used to have a good bit more lock in than it does today.

Speaker 2: Yeah.

Speaker 14: And you you still need a database that's holding all the stuff. Yeah. But the portability of it, think, is the new question.

Speaker 2: Yeah. Is is there a piece of this pitch that's, like, cost driven? Because with some of the advanced models, it feels like if I just threw a million dollars of compute at a problem, I can have it go and parse through every PDF and turn turn use an API to turn it into text and turn it into CSVs and write custom Python for it and just to answer the question of like what was revenue last month. Right? And that feels inefficient. It feels like this is a good solution. But is that is that the goal or is it something faster, speed, cost, everything?

Speaker 14: I don't think that we started there, but I think that to your point, like savvy people are sort of coming around to this. Because on one side, these models are smart enough that you can just sort of throw it at, you know, Google Drive and Slack in your warehouse Yeah. And you can actually answer every single question. So you can get 80% of the way there with literally no thought

Speaker 2: Yeah.

Speaker 14: Which on the one hand is a little bit scary to build your data company because it's sort of like, again, why don't you do this?

Speaker 2: Yeah.

Speaker 14: I think to your point, you probably don't want to reinvent your ARR metric every single time you query. Yeah. And ultimately, if it's a one second answer that you're gonna ask every single day, a dashboard is probably a pretty decent place to go get that. We don't need to rebuild the dashboard on the fly every single time. So people are starting to come around to realize that this is a cost performance optimization problem.

Speaker 1: Yeah.

Speaker 14: And it turns out that the analytics products that have existed for the last twenty years or fifty years or whatever it is actually do have some sort of good solutions to these things. I think at the same time, trying to fight the idea that you can just bring ChatTPT to the warehouse and get an answer is a naive version of building product now. Yeah. Every single person is gonna compare your SaaS product to sitting the thing on the warehouse or just setting it up yourself Yep. And doing it in flawed. Okay. And so figuring out that balance is, like, the goal of every SaaS company now. Yep. And cost is a part of it.

Speaker 2: Okay. That, like, bit of a bit of a hot take I want you to respond to. There there is like a muttering around like AI psychosis in the enterprise, like people vibe coding like things that don't actually create value yet. And there's a question about like where's the use what's the useful token spend versus just the explorative sort of performative token spend? I think there's lot of real stuff to be done. But there's also some crazy just some crazy internal tools that are just like Yep. You know, self perpetuating. And it reminds me of people had dashboard psychosis like a decade ago. I worked at a company where somebody was like oh we need all the every dashboard. We got to have every metric. And I'm like you kind of just oftentimes you have a question and you just need to go answer that question. You don't need to be checking like, what's the website traffic on my website every single minute? Like, sometimes that's not useful. Sometimes there's already a dashboard for it. So how do you think about rolling out, like, dashboards or analytic products that are actually effective, and, like, what coaching needs to happen versus just, like, raw consumption?

Speaker 14: It's it it really comes down to pragmatism. Mhmm. So the example I love to give is Guitar Center is a customer of ours.

Speaker 2: Okay.

Speaker 14: They have sent spreadsheets to every single store for the last thirty years that have every single SKU and, like, the week over week, day over day sales. Yep. That's not a process that needs reinventing. Yep. Like, they're gonna send the spreadsheets to the stores for the next twenty five years. You need products that can do that.

Speaker 2: Yeah.

Speaker 14: You're also gonna have a dashboard. Like, I I wanna know what our plan is, and I wanna go compare, like, our ARR on a weekly basis to where it is relative to plan. Yeah. You're also gonna just have a question that you need answered that you didn't predict in advance and need to go answer questions. And I think the challenge is that every vendor sort of has this point of view that's like, you know, dashboards are the way or Tableau was visualizations are the way to do everything. And then I was at Looker, and semantic layers were the way to do everything.

Speaker 2: Yep.

Speaker 14: And I think what you see is that there's a little bit of truth in all of these things. And and quite frankly, right now, it's AI is the way to do everything. Yeah. And we're into, you know, how many billion tokens can you spend to go answer your question. Yeah. We probably want a tool that does all of those things and actually makes them fit together.

Speaker 2: Yeah.

Speaker 14: And it's sort of like the it's not the sexiest pitch. You know, every single thing that you are doing is right, and you need a little bit of all of it.

Speaker 2: Yeah.

Speaker 14: But I I think that there is space for pragmatism in the world where we can invent things on the fly sometimes, and that is a great way of doing it, we don't need UI. And we have good UIs, and they do things, and, you know, websites are good, and we can go back to them again. Yeah. So it's both.

Speaker 2: It's not the sexiest pitch, but it was sexy enough to get the series c done. How much did you raise? Tell me about the round.

Speaker 14: Half we $120,000,005.01 plus valuation.

Speaker 1: Massive. Massive. Quick could could you could you give

Speaker 12: us a quick history of the company?

Speaker 1: When did you actually start the company?

Speaker 14: Yeah. February 22, so four years and a little bit old. Yeah. We raised about $27,000,000 as we got the company started. Did our b about twelve months ago at a $6,650,000,000 valuation. So Another we're building for about a year and a half, and we've been selling for two and a half.

Speaker 2: Yeah. So

Speaker 14: business has been pretty good.

Speaker 2: Fantastic.

Speaker 1: And what were you doing what were

Speaker 12: you doing before this?

Speaker 14: I was at a company called Looker that got acquired by Google. So

Speaker 1: Okay. Makes you feel

Speaker 14: a very direct competitor to my previous company.

Speaker 1: You know what

Speaker 2: you're doing. Well, congratulations on the progress.

Speaker 1: Love the the chat loves the pink by the way. I love the pink.

Speaker 2: It's great.

Speaker 1: Are you guys like fully just are you gonna own pink? Yeah.

Speaker 14: Embraced the pink. We we sort of fell into it.

Speaker 1: We I think it's

Speaker 11: so smart.

Speaker 1: We were

Speaker 14: going to a conference that was all blue and gray. Yeah. And one of our marketing leaders was like, hey, wear some pink. People will notice you.

Speaker 2: I'll stand out.

Speaker 14: And then we just turned into a pink company. So we embraced it.

Speaker 15: Fantastic. I love it.

Speaker 1: Very smart.

Speaker 2: Well, congratulations. Thanks so much for taking the time to come chat

Speaker 1: with us. Collin.

Speaker 2: We'll talk to you soon. Thanks for having

Speaker 1: me, guys.

Speaker 2: Have a good one. Goodbye. Up next.

Speaker 1: Interesting username. I didn't get to ask him about it.

Speaker 2: Oh, yeah?

Speaker 1: It's at drink zima. It sounds like some type of Interesting. Like cool caffeinated beverage.

Speaker 2: Yeah. That does is a Zico coconut water.

Speaker 1: Get a very cool caffeinated energy. Maybe

Speaker 2: that's the next job the the next thing he can buy Sobe, bring it back. Up next, we have Alex Epstein, the author of Fossil Future, live in the TBPN UltraDome, taking us through what's going on in the world, what's going on in energy markets, what's going on with OPEC. Great to see you again, Alex. Thank you so much for taking the time to come on down to the TBPN UltraDome. Please, get us up to speed.