 Welcome to this CUBE conversation. I'm Lisa Martin, excited about this conversation. It's combining my background in life sciences with technology. Please welcome Mike Tarcelli, the Chief Scientific Officer at Tetris Science. Mike, I'm so excited to talk to you today. Thank you, Lisa, and thank you very much to theCUBE for hosting us. Absolutely. So we talk about cloud and data all the time. This is going to be a very interesting conversation, especially because we've seen events of the last, literally on 14 months and counting have really accelerated the need for drug discovery and really everyone's kind of focused on that. But I want you to talk with our audience about Tetris Science, who you guys are, what you do. And you were founded in 2014, you just raised 80 million in Series B, but give us an idea of who you are and what you do. Got it, Tetris Science, what are we? We are digital plumbers, and that may seem funny, but it really, we are taking the world of data and we are trying to resolve it in such a way that people can actually pipe it from the data sources they have in a vendor agnostic way to the data targets in which they need to consume that data. So bringing that metaphor a little bit more to life sciences, let's say that you're a chemist and you have a mass spec and an NMR and some other piece of technology and you need all of those to speak the same language. Generally speaking, all of these are gonna be made by different vendors, they're all gonna have different control software and they're all gonna have slightly different ways of sending their data in. Tetris Science takes those all in, we bring them up to the cloud, we're a cloud native solution, we harmonize them, we extract the data first and then we actually put it into what we call our special sauce, our intermediate data schema to harmonize it. So you have sort of like a picture and a diagram of what the prototypic mass spec or HPLC or cell counting data should look like. And then we build pipelines to export that data over to where you need it. So if you need it to live in an ELN or a limb system or in a visualization tool like Spotify or Tableau, we got you covered. So again, we're trying to pipe things from left to right, from sources to targets and we're trying to do it with scientific context. That was an outstanding description, data plumbers who have secret sauce and never would have thought I would have heard that when I woke up this morning. But I wanna unpack this more because one of the things that I read in the press release that just went out just a few weeks ago announcing the series B funding, it said that Tetris Science is pioneering a $300 billion greenfield data market and operating, and this is what kept my attention without a direct cloud native and open platform competitor. Why is that? That's right. If you look at the way pharma data is handled today, even those that long tend to be either on-prem solutions with a sort of license model or a distribution into a company and therefore maintenance costs, professional services, et cetera. Or you're looking at somebody who is maybe cloud but they're cloud second. They started with their on-prem journey and they said we should go and build out some APIs and we should go to the cloud migrate. However, we're cloud first, cloud native. So that's one first strong point. And the second is that in terms of data harmonization and in terms of looking at data in a vendor agnostic way, many companies claim to do it, but the real hard test of this, the metal, what we'll say is when you can look at this with the scientific contextualization we offer. So yes, you can collect the data and put it on a cloud. Okay, great. Yes, you may be able to do an extract, transform and load and move it to somewhere else. Okay, but can you actually do that from front to back while retaining all the context of the data while keeping all of the metadata in the right place with veracity, with GXP readiness, with data fidelity? And when it gets over to the other side, can somebody say, oh yeah, that's all the data from all the HPLCs we control. I got it. I see where it is. I see where to go get it. I see who created it. I see the full data train and validation landscape and I can rebuild that back and I can look back to the old raw source files if I need to. I challenge someone to find another direct company that's doing that today. Will you talk about that context and the thing that sort of surprises me is with how incredibly important scientific discovery is and has been for since the beginning of time, why has nobody come out in the last seven years and tried to facilitate this for live sciences organizations? Right, I would say that people have tried and I would say that there are definitely strides being made in the open source community, in the data science community and inside pharma and biotechs themselves on the sort of build motif, right? If you are inside of a company and you understand your own ontologies and processes, well, you can probably design an application or a workflow using several different tools in order to get that data there. But will it be generally useful to the bioscience community? One thing we pride ourselves on is when we productize a connector we call or an integration, we actually do it with a many different companies generic cases in mind. So we say, okay, you have an HPLC problem over at this top pharma. You have an HPLC problem with this biotech and you have another one at this CRO. Okay, what are the common points between all of those? Can we actually distill that down to a workflow everyone's going to need? For example, a compliance workflow. So everybody needs compliance, right? So we can actually look into an empower or a unicorn operation and we can say, okay, did you sign off on that? Did it come through the right way? Was the data corrupted, et cetera? That's gonna be generically useful to everybody. And that's just one example of something we can do right now for anybody in biopharma. Let's talk about the events of the last 14 months or so mentioned 10x revenue growth in 2020. COVID really highlighted the need to accelerate drug discovery and we've seen that. But talk to me about some of the things that tetra science has seen and done to facilitate that. Yeah, these past 14 months, I mean, I will say that the global pandemic has been a challenge for everyone involved, right? Ourselves as well. We've basically gone to a full remote workforce. We have tried our very best to stay on top of it with remote collaboration tools, with Jira, with GitHub, with everything. However, I'll say that it's actually been some of the most successful time in our company's history. Because of that sort of lack of any kind of friction from the physical world, right? We've really been able to dig down and dig deep on our integrations, our connections, our business strategy. And because of that, we've actually been able to deliver a lot of value to customers because let's be honest, we don't actually have to be on-prem from what we're doing. Since we're not an on-prem solution and we're not an original equipment manufacturer, we don't have to say, okay, we're gonna go plug the thing in to the HPLC. We don't have to be there to tune the specific wireless protocols or your AWS protocols. It can all be done remotely. So it's about building good relationships, building trust with our colleagues and clients, and making sure we're delivering and over-delivering every time. And then people say, great, when I elect a Tetra solution, I know it's going right to the cloud. I know I can pick my hosting options. I know you're gonna keep delivering more value to me every month. Thanks. I like that. You make it sound simple and actually you bring up a great point though that one of the many things that was accelerated this last year plus is the need to be remote, the need to be able to still communicate, collaborate, but also the need to establish and really foster those relationships that you have with existing customers and partners as everybody was navigating very, very different challenges. I wanna talk now about how you're helping customers unlock the problem that is in every industry, data silos and point-to-point integrations where things can't talk to each other. Talk to me about how you're helping customers, like where do they start with Tetra? Where do you start that kind of journey to unlock data value? Sure, journey to unlock data value, great question. So first I'll say that customers tend to come to us. It's the oddest thing and we're very lucky and very grateful for this, but they tend to have heard about what we've done with other companies and they come to us. They say, listen, we've heard about a deployment you've done with Novo Nordisk. I can say that for example, because it's publicly known. So they'll say, we hear about what you've done, we understand that you have deep expertise in chromatography or in bioprocess and they'll say, here's my really sticky problem. What can you do here? And invariably they're gonna lay out a long list of instruments and software for us. We've seen lists that go up past 2,000 instruments and they'll say, yeah, they'll say, here's all the things we need connected. Here's four or five different use cases. We'll bring you start to finish, we'll give you 20 scientists in a room to talk through them and then we Tetra get somewhere between two and four weeks to think about that problem and come back and say, here's how we might solve that. Invariably, all of these problems are gonna have a data silo somewhere, right? There's going to be an org where the preclinical doesn't see the biology or the biology doesn't see the screening, et cetera. So we say, all right, give us one scientist from each of those, hence establishing trust, establishing input from everybody and collaboratively we'll work with you. We'll set up an architecture diagram. We'll set up a first version of a prototype connector. We'll set up all this stuff they need in order to get moving. We'll deliver value upfront before we've ever signed a contract and we'll say, is this a good way to go for you? And they'll say, either no, no, thank you. Or they'll say, yes, let's go forward. Let's do a pilot, a proof of concept or let's do a full production rollout. And invariably this data silos problem can usually be resolved by again, these genericized connectors are intermediate data schema which talks and moves things into a common format, right? And then also by organizationally, since we're already connecting all these groups in this problem statement, they tend to continue working together even when we're no longer front and center, right? They say, oh, we set up that thing together. Let's keep thinking about how to make our data more available to one another. Interesting, so culturally within the organization, it sounds like Tetra is having significant influences there, you know, the collaboration, but also data ownership. Sometimes that becomes a sticky situation where there are owners and they want to retain that control, right? You're laughing, I can, you've been through this before. I'd love to understand a little bit more though about the conversations, because typically, you know, we're talking about tech, but we're also talking about science. Are you having these technical conversations with scientists as well as IT? What does that actual team from the customer perspective look like? Oh, sure. So the technical conversation and the science conversation are going on, sometimes in parallel and sometimes in the same thread entirely. Oftentimes, the folks who reach out to us first tend to be the scientists. They say, I've got a problem, you know, and my research and IT will probably hear about this later, but let's go. And then we will invariably say, well, let's bring in your R&D IT counterparts because we need them to help solve it, right? But yes, we are usually having those conversations in parallel at first and then we unite them into one large discussion. And we have varied team members here on the Tetra side. We have me from science, along with multiple different other PhD holders and pharma lifers in our business who actually can look at the scientific use cases and recommend best practices for that and visualizations. We also have a lot of solutions architects and delivery engineers who can look at it from the, how should the platform assemble the solution and how can we carry it through? And those two groups or three groups really unite together to provide a unified front and to help the customer through. And the customer ends up providing the same thing as we do. So they'll give us on the one call, right? A technical expert, a data and QA person and a scientist all in one group. And they'll say, you guys work together to make sure that our org is best represented here. And I think that that's actually a really productive way to do this because we end up finding out things and going deeper into the connector than we would have otherwise. It's very collaborative, which is I bet those are such interesting conversations to be a part of it. So it's part of the conversation there, helping them understand how to establish a common vision for data across their organization. Yes, that tends to be a sort of further reaching conversation. I'll say in the initial sort of short-term conversation, we don't usually say you three scientists or engineers are going to change the fate of the entire org. That's maybe a little outside of our scope for now. But yes, that that first group tends to describe a limited solution. We help to solve that and then go one step past. And then they'll nudge somebody else in the org and say, do you see what Tetra did over here? Maybe you could use it over here in your process. And so in that way we sort of get this cultural buy-in and then increased collaboration inside a single company. Talk to me about some customers that you've worked with and I especially love to know some of the ones that you've helped in the last year where things have been so incredibly dynamic in the market, but give us an insight into maybe some specific customers that work with you guys. Sure, I'd love to. I'll speak to the ones that are already on our case studies. You can go anytime to tetrascience.com and read all of these. But we've worked with prelude therapeutics, for example. We looked at a high throughput screening cascade with them and we were able to take an instrument that was basically unloved in a corner, a T-CAN liquid handler, hook it up into their ELN and their screening application and bring in and incorporate data from an external party and do all of that together and merge it so that they could actually see out the other side a screening cascade and see their data in minutes as opposed to hours or days. We've also worked as you've seen the press release with Novo Nordisk. We worked on automating much of their background for their chromatography fleet. And finally, we've also worked with several smaller biotechs in looking at sort of instantiation. They say, well, we've just started. We don't have an ELN. We don't have a LIMS. We're about to buy these 50 instruments. What can you do with us? And we'll actually help them to scope what their initial data storage and harmonization strategy should even be. So we're really, man, we're everywhere from the enterprise where it's fleets of thousands of instruments and we're really giving data to a large amount of scientists worldwide all the way down to the small biotech with 50 people who were helping add value there. So big range there. In terms of the data conversation, I'm curious, have you seen it change in the last year plus with respect to elevating to the C-suite level or the board saying we've got to be able to figure this out because as we saw, the race for the COVID-19 vaccine, for example, time to value and to discovery is so critical. Is that C-suite or board involved in having conversations with you guys? It's funny because they are, but they are a little later. We tend to be a scientist and user-driven solution. So at the beginning, we get a power user, an engineer or a R&D IT person in who really has a problem to solve. And as they are going through and developing with us, eventually they're going to need either approval for the time, the resources or the budget. And then they'll go up to their VP or their CIO or someone else at the executive level and say, let's start having more of this conversation. As a tandem effort, we are starting to become involved in some thought leadership exercises with some larger firms. And we are looking at the strategic aspect through conferences, through white papers, et cetera, to speak more directly to that C-suite and to say, hey, we could fit your industry 4.0 motif. And then one other thing you said time to value. So I'll say that the Tetra Science Executive Team actually looks at that as a tract metric. So we are actually looking at driving that down every single week. That's outstanding. That's a hard one to measure, especially in a market that is so dynamic, but that time to value for your customers is critical. Again, COVID sort of surfaced a number of things and some silver linings, but that being able to get hands on the data, make sure that you can actually pull insights from it, accelerate, facilitate drug discovery that time to value there is absolutely critical. Yeah, I'll say, if you look at the companies that really went first and foremost, let's look at Moderna, right? Not our customer, by the way, but we'll look at Moderna quickly as an example, as an exemplar. Everything they do is automated, right? Everything they do is cloud first, everything they do is global collaboration networks with harmonized data, et cetera. That is the model we believe everyone's going to go to in the next three to five years, right? If you look at the fact that Moderna went from sequence to initial vaccine in what, 50, 60 days, that kind of delivery is what the market will become accustomed to. And so we are going to see many more pharmas and biotechs move to that cloud first, distributed model, all data has to go in somewhere centrally, everyone has to be able to benefit from it, and we are happy to help them get there. Well, that setting a new record for PACE is key there, but it's also one of those silver linings that has come out of this to show that not only was that critical to do, but it can be done. We have the technology, we have the brain power to be able to put those, I'll use your word, harmonize those together to drive this. So give me a last question, give me an insight into some of the things that are ahead for Tetra Science the rest of this year. Oh gosh, so many things. One of the nice parts about having funding in the bank and having a dedicated team is the ability to do more. So first, of course, our enterprise pharma and biopharma clients, there are plenty more use cases, workflows, instruments. We've just about scratched the surface, but we're gonna keep growing and growing our integrations and connectors, first of all, right? We want to be like a Netflix for connectors. We just want you to come and say, oh look, do they have the connector? No, well, don't worry, they're gonna have it in a month or two. So that we can be basically the almost the Swiss Army knife for every single connector you can imagine. Then we're going to be developing a lot more data apps, so things that you can use to derive value from your data out. And then again, we're going to be looking at helping to educate everybody. So how is cloud useful? Why go to this system with harmonization? How does this influence your compliance? How can you do bi-directional communication? There's lots of ways you can use once you have harmonized centralized data, you can do things with it to influence your org and drive times down again from days and weeks to minutes and seconds. So let's get there. And I think we're going to try doing that over the next year. That's awesome, never a dull moment. And you should partner with your marketing folks because you talked about data plumbing, the secret sauce, and becoming the Netflix of connectors. Those are three gems that you dropped on this morning, Mike. This has been awesome. Thank you for sharing with us what Tetriscience is doing, how you're really helping to fast-track a lot of the incredibly important research that we're all really dependent on and helping to heal the world through data. It's been a pleasure talking with you. Hey, Lisa, if I may real quickly, it's a team effort. The entire Tetriscience team deserves credit for this. I'm just lucky enough to be able to speak to you. So thank you very much for the opportunity. And shout out cheers to the whole Tetriscience team. Keep up the great work, guys. For Mike Tarselli, I'm Lisa Martin. You're watching this CUBE conversation.