In this theCUBE + NYSE Wired: Mixture of Experts segment from the New York Stock Exchange, theCUBE’s John Furrier sits down with Raj Verma, CEO of SingleStore, to unpack how the intersection of technology and finance is shaping enterprise strategy. Verma shares why SingleStore is “on course” for the public markets, reflects on brand-building through the company’s partnership with golf Hall of Famer Padraig Harrington and connects that ethos to how SingleStore helps organizations fix struggling data “swings.” The discussion zeroes in on what’s next as Wall Street watches the AI infrastructure buildout: after chips and systems, the software and data layers set the pace for value creation.
Verma outlines why enterprises must modernize “brown” data estates into “green” ones to safely bring corporate context, governance and compliance into LLM workflows via RAG – and why commoditized data-at-rest puts the advantage at the query layer that unifies data in motion with data at rest. He predicts agentic AI will gain reasoning capabilities in roughly 18 months, cites industry indicators like Google reporting ~25% of its software now built by AI and argues that high switching costs will give way to disruption as buyers reassess legacy vendors. The conversation closes with concrete momentum: ~33% YoY growth, ARR in the ~$135M range, gross dollar retention ~98%, cloud NDR ~130, ~50% of business now in the cloud, landing ~3 new customers per day, a path to cash-flow breakeven in the next two quarters and a teaser for AI-related announcements in the next two months. Listeners will find notable stats, real-world use cases and forward-looking views on how databases power reliable AI at enterprise scale.
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Brandon Rice, Weave
In this theCUBE + NYSE Wired: Mixture of Experts segment from the New York Stock Exchange, theCUBE’s John Furrier sits down with Raj Verma, CEO of SingleStore, to unpack how the intersection of technology and finance is shaping enterprise strategy. Verma shares why SingleStore is “on course” for the public markets, reflects on brand-building through the company’s partnership with golf Hall of Famer Padraig Harrington and connects that ethos to how SingleStore helps organizations fix struggling data “swings.” The discussion zeroes in on what’s next as Wall Street watches the AI infrastructure buildout: after chips and systems, the software and data layers set the pace for value creation.
Verma outlines why enterprises must modernize “brown” data estates into “green” ones to safely bring corporate context, governance and compliance into LLM workflows via RAG – and why commoditized data-at-rest puts the advantage at the query layer that unifies data in motion with data at rest. He predicts agentic AI will gain reasoning capabilities in roughly 18 months, cites industry indicators like Google reporting ~25% of its software now built by AI and argues that high switching costs will give way to disruption as buyers reassess legacy vendors. The conversation closes with concrete momentum: ~33% YoY growth, ARR in the ~$135M range, gross dollar retention ~98%, cloud NDR ~130, ~50% of business now in the cloud, landing ~3 new customers per day, a path to cash-flow breakeven in the next two quarters and a teaser for AI-related announcements in the next two months. Listeners will find notable stats, real-world use cases and forward-looking views on how databases power reliable AI at enterprise scale.
In this interview from theCUBE + NYSE Wired: Mixture of Experts, Brandon Rice, chief executive officer and co-founder of Weave.bio, joins theCUBE's John Furrier to discuss how AI agents are compressing the decade-long regulatory journey in drug development. Rice explains how Weave.bio was built from the ground up as an AI-native company, using large language models to automate the creation of FDA regulatory submissions — a process he describes as historically brutal, manual and time-consuming. He details how agents now extend across the full submission workfl...Read more
exploreKeep Exploring
How are agents being integrated into the regulatory submission workflow, and what additional value do they provide beyond the initial automated drafting of content?add
Can an AI fully handle the workflow and content for FDA submissions, or do humans still need to supervise and take responsibility?add
How does that factor into Weave's value proposition?add
What recent funding, team growth, office/location changes, and customer traction has the company experienced, and why was San Francisco chosen as the place to centralize?add
Has any customer used this product/service to submit a regulatory filing to the FDA and had that submission approved?add
>> Welcome back here into theCUBE Studio here at the New York Stock Exchange. I'm John Furrier, host of theCUBE. Of course, we have our Palo Alto studio connecting Silicon Valley and Wall Street. This is part of our mixture of experts here, is also part of our agent conference preview, growing community, Simon Chan, and builders and operators of agentic infrastructure, having an event in May here at Hilton in New York City. It's the must attend events growing into quite the community. As AI native developers swarm onto the scene, the agentic infrastructure is going to enable a lot more AI native apps. Of course, a lot of entrepreneurs and founders will be presenting. We have one here. We've got Brandon Rice, CEO and co-founder of Weave.bio. Brandon, thanks for coming in. Thanks for coming in and previewing the agent conference and being part of our mixture of expert series.
Brandon Rice
>> My pleasure.
John Furrier
>> Mixture of expert series, kind of a pun, but kind of intended, mixture of experts is the hottest part of AI. But this agent conference to me is a sign of the market. Simon Chan, the community, small... A year ago, a year and a half ago, not a huge number of innovators really kind of digging into the layer around some of the hard technical problems. What I would call infrastructure, I'm not sure there's actually an official persona yet for this category, but it's also not cloud native either, but it's kind of cloud native. It's kind of like, feels the same like cloud native, but it's a whole nother level of acceleration. You're part of that community. What are you working on and how do you fit into the agent conference?
Brandon Rice
>> So what we're working on at Weave is we are building a system to automate and unify the entire regulatory process in drug development. And we're initially focused just on drug development and getting drugs from the lab to patients as fast and as inexpensively as possible.
John Furrier
>> So you're like a poster child of compression.
Brandon Rice
>> That's right.
John Furrier
>> The acceleration that we're seeing. How are you playing into with the agent piece? What's the innovation angle there? How would you describe that?
Brandon Rice
>> So as a company, we're only three years old now. So we have grown up and been built around AI, this current generation of AI from the very beginning. Agents are relatively new within that. But our whole system is built around what should this process look like given this new technology? And it's a completely different lens with which to approach it from you already have software and you want to figure out how you can jam AI in there. That's very hard to do. And I would say impossible, or at least something's going to take a very long time.
John Furrier
>> It's a hard retrofit.
Brandon Rice
>> Absolutely.
John Furrier
>> I mean, that's like jamming a square into a round hole.
Brandon Rice
>> Because it's entirely different now. And the way you want to approach these types of workflows with this technology is entirely different. So you kind of have to build it, rebuild it from the ground up. It's moving very fast here. Drug development notoriously does not move fast. It's 10 years and billions of dollars to get a drug to market. And so we're trying to shrink that brand.
John Furrier
>> And the numbers are staggering when you look at the economics just on cost.
Brandon Rice
>> Yep.
John Furrier
>> I like how you said you guys were there from the beginning because born in the cloud was a term for all of us 17 years ago, Dropbox, theCUBE, Airbnb, Twitter was started in AWS. Back then it was just all startups. The alternative was build a data center or go to the cloud. You're living in the AI native era.
Brandon Rice
>> That's right.
John Furrier
>> Where the generative AI was hitting the scene, you saw immediate value. Because when I hear regulatory, my mind blows into like this mode of like grinding it out, complexity, paperwork, zillions of rules and regulations.
Brandon Rice
>> , yep.
John Furrier
>> It's a slog. So how did you guys attack that? What was the original thinking? We could apply AI to this, we can certainly ingest the content, get it to be vector embedded. How do you get reasoning in there? So take me through the process of how you guys saw this and what was your approach?
Brandon Rice
>> Yeah. So I have to give credit to my co-founder, Ari, who was the one who saw LLMs coming, this new generation of AI, and knew that it was going to be a game changer. And he was the one who brought that to me. I was coming from a background of... I'd worked in drug discovery, drug development, or sorry, drug manufacturing and diagnostics. And so by virtue of those experiences, and those are all startups that I worked in, I got to see a common denominator, a thread that ran through it all, which was the regulatory process. And it is exactly as you described it. It's a slog, it's brutal, it's manual. Nobody wants to do it, everybody has to do it. And the whole process is about transforming knowledge, managing knowledge, and then communicating it effectively. And that is an absolutely perfect .
John Furrier
>> How did you guys meet? I mean, because that is basically a living example of essentially what Jensen Huang said on stage two weeks ago or three weeks ago at GTC, horizontal scale, vertical domain knowledge. I mean, you have the domain knowledge. He saw the LLM as a liberator to go after this problem.
Brandon Rice
>> Yeah.
John Furrier
>> Was it serendipity? Was you guys hanging out drinking beers together? I mean, take me through, how did those two worlds come together?
Brandon Rice
>> It was complete serendipity and perfect timing. And I would much rather be lucky than good. And we've had a lot of those opportunities. We've been good too.
John Furrier
>> To make your own luck. If you're in the arena, stuff happens.
Brandon Rice
>> That's right. And so we actually met through VC. So I was leaving my last company, wanted to look for new opportunities more in the software side. I was working on drug manufacturing before this. So I wanted to get back to software. Ari was coming out of Tempus, which had just acquired his first company. And he has a deep background in life sciences as well. He was chief AI officer, or sorry, chief analytics officer at Memorial Sloan Kettering for 15 years. So he also had that domain-
John Furrier
>> He knew the data problem.
Brandon Rice
>> Yeah.
John Furrier
>> He saw the data opportunity.
Brandon Rice
>> Front to back.
John Furrier
>> Yeah. .
Brandon Rice
>> And so he had just raised a little bit of money, a pre-seed. We met through an investor friend of mine and friends at first sight, said let's do this and building things.
John Furrier
>> Yeah, yeah. We can see a path.
Brandon Rice
>> Yeah.
John Furrier
>> Saw the opportunity.
Brandon Rice
>> That's right.
John Furrier
>> All right. So talk me through the tech because I think agents are an extension of having that base requisite.
Brandon Rice
>> Yeah.
John Furrier
>> Okay, so you guys have done the work, you're in AI, you're engulfed in it, you're working through it, you probably saw some great results.
Brandon Rice
>> Yeah.
John Furrier
>> Then it's like, okay, wow, agents are here.
Brandon Rice
>> Yeah.
John Furrier
>> Now that's the conference that Simon has. So how are agents coming? Is that the automation piece? How are you thinking about extending the value creation with agents?
Brandon Rice
>> So where we started was with automated drafting of the content. So what we're doing is helping companies produce regulatory submissions for FDA. You got to do this when you want to go to clinical trials, when you want to progress through your clinical trials, when you want to take your drug to market. Each one of those has a stage gate where you have to go through FDA and get them to bless it. So you have to put together these mountains of paperwork to do that, that they then have to pick through and figure out. Where we started was generating that content, because it's all produced from source documents. You do a toxicology study over here. You figure out how to manufacture the drug over here. You got to pull all of that information together in one tidy, concise package. And that's where we started. And we're just using vanilla LLMs at that time. So this is before agents. And it's a perfect match for the LLMs. You give it a bunch of the source information, you ask it to produce an output, and then you get that in seconds. There is much more to the workflow though. After the content has been produced, you have to edit it and refine it. You have to review it, make sure that it's aligned to the guidance. You have to QC it and make sure that every sentence within that content is accurate. All of those jobs are very amenable to agents. And so it's all of these other ancillary parts of the workflow that we've begun to put agents into to support front to back that entire workflow.
John Furrier
>> It's interesting. It's nice that you guys got that complimentary background, but you just brought up workflows.
Brandon Rice
>> Yeah.
John Furrier
>> It seems to be the common thread of success in AI and agentic is workflows. Well, data, workflows, outcome. You agree?
Brandon Rice
>> Absolutely. I mean, the way that we talk about what we're doing to a lot of our prospects, and one of our big pharma customers was the first one to describe it this way to us, and we ripped it off, is the AI is the engine, we're building the car. You have all of this power, but it has to be wielded. It has to be pointed in the right direction. And in this industry, that has to be done by PhD scientists who know the regulatory landscape and the science, and they have to steer it.
John Furrier
>> My oldest daughter, Jacqueline, went to Berkeley Cal, and she ended up working for UCSF and doing FDA approvals, clinical trials, and all manual process.
Brandon Rice
>> 100%.
John Furrier
>> I got a little insight from hers at the slog. But turns out that the heart area and these devices, they're the highest tier of complexity. So take us through your thinking to the tiering of complexity, what that means, and how did the FDA respond to this AI workflow?
Brandon Rice
>> So far for our customers, it's been transparent to the FDA. And that actually is part of the answer to your question as well, is this content, this workflow is not something that you can just hand over to an AI right now. In the foreseeable future, maybe ever, we'll see. There still has to be a human driver of the process, and there has to be a human who's taking responsibility for whatever's going over to the FDA. And so we built our system as a cockpit. So the human drivers are still in there. The people at the drug companies with the expertise and the knowledge and the background, they're the ones directing the AI on what to do, and they're the ones ultimately taking responsibility for the content at the end. So that is how we're mediating that complexity. We're not asking the AI to do everything. We're giving it the stuff that is relatively self-contained and that it can do well. And for the higher order tasks of creating strategy, creating narrative, interpreting data, very complex data, we're still leaving those to the humans and having the two work in concert.
John Furrier
>> How has the acquisition of data changed? Because one of the concepts coming out of this modern era is the AI is only as good as what it can see. So data acquisition is a huge factor.
Brandon Rice
>> Yeah.
John Furrier
>> How does that play into Weave value?
Brandon Rice
>> It's half the challenge, especially as we're getting into larger and larger enterprises. So we have one top 20 pharma company as a customer right now. We've got a handful of others in pilots. We've got Parexel, which is a global CRO as a customer. There, the problems of immense amounts of data spread all over the place are at their peak and plugging into all the places where the data lives, aggregating it, organizing it, understanding it, and then getting it into a submission for FDA, it's fifty fifty.
John Furrier
>> This is where Ari, your co-founder comes in.
Brandon Rice
>> That's right, yeah. He's big on mustering the data.
John Furrier
>> Oh, yeah. He's lived that... Well, the analytics is interesting. Analytics industries like the regulated industries. I was just talking with a 38-year veteran at Procter & Gamble. They're analytics junkies. They love analytics.
Brandon Rice
>> Yep.
John Furrier
>> If you love analytics, you're going to love gen AI. That's what I say.
Brandon Rice
>> 100%.
John Furrier
>> You agree?
Brandon Rice
>> Yeah.
John Furrier
>> How does that transform for the human? I can imagine that the human in the loop here is seeing great value because the heavy lifting from the AI is helping them.
Brandon Rice
>> Yeah. Well, I'll give you an example from the perspective of FDA. So this whole process for us, it's a dance between two sides. You have the drug company here, the FDA here, and there's a lot of back and forth, and it's effectively a negotiation to get a drug from point A to the market to patients. FDA has to take all this content that the drug companies are producing, digest it, understand it, make decisions based on that. And historically, they're getting these very long narratives written by scientists who love to geek out on this stuff.
John Furrier
>> Yeah, they write 10 zillion pages.
Brandon Rice
>> That's right. And the reviewer is just like, I just want to know-
John Furrier
>> Bottom line means.
Brandon Rice
>> Yeah. And now that's possible.
John Furrier
>> It's a TLDR.
Brandon Rice
>> That's right. That's right. And you can put that in the hands of the reviewers. Same as we're currently equipping the sponsors, the drug companies.
John Furrier
>> But you guys can source it. So it's like a quick read.
Brandon Rice
>> Yeah.
John Furrier
>> So you have sourcing.
Brandon Rice
>> And just boil it down for what is your question and where is the information to answer your question and then we'll present it.
John Furrier
>> If they want to do a drill-down, there's a door available.
Brandon Rice
>> It's all there, yeah.
John Furrier
>> We have to click on. All right. Talk about the event. First of all, I love what you're doing. So I think it's a great win. I think that's going to speed up a lot. And this is a great example of where AI infrastructure's doing great work, healthcare, life sciences, booming categories.
Brandon Rice
>> Absolutely.
John Furrier
>> No doubt about it. So you're in a hot-
Brandon Rice
>> Very positive impact.
John Furrier
>> Positive impact, money making. I mean, it really lines up nicely. So I think you certainly have no problem getting your B round. Talk about the momentum you have with companies right now, your Series A.
Brandon Rice
>> Sure.
John Furrier
>> You got 40 employees.
Brandon Rice
>> Yeah.
John Furrier
>> What's your focus? Give some staffs, share some momentum.
Brandon Rice
>> Yeah. So we just raised our Series A back in September. That was $20 million. We are at 40 people now. We just opened our first office in San Francisco downtown. We were a distributed company before that, and now we're centralizing in the Bay where all the action is as far as AI goes.
John Furrier
>> And by the way, San Francisco, people might not know this, but from a university standpoint, you got Cal, UCSF, I mentioned that earlier, and you got Stanford and you got... And that's just in the Bay Area.
Brandon Rice
>> Yeah.
John Furrier
>> Tons of talent.
Brandon Rice
>> The combination of drugs and life sciences and AI there, it just can't be beat. And that's why we decided to centralize there, even though that because with its own challenges and pains and problems, but I'm feeling great about that decision so far. We at this point have a couple of dozen customers. The vast majority of them are drug companies. We have one top 20 pharma customer. We have a partnership and a customer in Parexel, which is one of the largest CROs in the world. They run clinical trials on behalf of drug companies, and they're using us exclusively to produce their early stage regulatory submissions, and we're gradually extending over more of the process. The kind of big question that we were always getting asked last year was, has anyone actually done a submission with this, sent it to FDA and had that be successful? And until Q4, we had to kind of dance around that. But in Q4 of last year, we had our first three customer submissions to FDA. They were all approved. All of those drugs are now in clinical trials.
John Furrier
>> They love you.
Brandon Rice
>> Yeah.
John Furrier
>> They love you because you make their life easier.
Brandon Rice
>> That's right.
John Furrier
>> I want to ask you a little bit off-topic, but kind of what you do, but I'm seeing a trend. I want to get your thoughts on it. The FDA process has always been kind of, I won't say regulated to a set of certain situations, drug companies, devices, implants, and whatnot, things that need review. We're seeing with the wearables, a lot more tech companies, whether it's an Aura ring or device on a human, they need FDA approval because now you have instrumentation. Just as a diagnostic guru expert, they've never done this before.
Brandon Rice
>> Right.
John Furrier
>> So do you see that as a trend or is that just a one-off? I mean, FDA must be getting pounded with volume.
Brandon Rice
>> I mean, I think that that's where the world is going. And my wife agrees, she's a data scientist who works in health tech and was at Apple. And that's absolutely the way the world is going. The amount of data that you have to amass to be able to answer meaningful health questions at this point, all the low hanging fruit has been plucked. So all that's left is very complex answers that require a lot of data to successfully address them. There's no other way forward except for these devices, more advanced diagnostics, things like that. The future is all data.
John Furrier
>> So instrumentation, having access to that data is going to be the collective intelligence for the harder...
Brandon Rice
>> And figuring out how to drive insights from that, which again, is something that is now beyond the capacity of human minds to do. The simple sort of statistical analyses or even the sophisticated statistical analyses that we used to do, it's not enough anymore. And that's why people are increasingly applying AI to that problem as well, the data analytics and trying to derive insights in that way. And it's AI there on the analytics part. It's AI here creating the communication channels. It's all-
John Furrier
>> It's a fun time to be in the market, just because the breakthroughs are there. I mean, it's like in my entire career, 30 years doing this plus years, I've never seen a time where the north stars of these big ideas, especially in life sciences and healthcare, they are gettable on a speed level that's years to months, months to weeks.
Brandon Rice
>> Yeah. I mean, when we started, the first year we were in business, everyone was just trying to wait out the latest hype cycle, which was this AI, and then it didn't go away, and then people started to use it and prove it out. And we are now at a point where we have customers who are using our system to produce FDA submissions. They've done it successfully. They're saving half the time at least, and some of them up to 90% of the time in this process. It's real. It's not painless to incorporate into your organization. There's a lot of evolution that has to happen.
John Furrier
>> But the rewards are there.
Brandon Rice
>> That's right.
John Furrier
>> And I'll talk about the... You mentioned this earlier, I want to get this, it's this important point. You can't just throw AI at stuff. You mentioned that.
Brandon Rice
>> Yeah.
John Furrier
>> Expand on that because this is where I think the signal from the noise becomes an interesting thing, whether you're a builder, operator, or an investor, understanding some of the work that has to get done to put yourself in a position to be successful for whatever that serendipity is, whatever that moment is. It's not just throw some magic dust on a problem.
Brandon Rice
>> And that's why there's so much excitement around verticals right now, and that's why we are going after the vertical that we're going after because there's a lot of complexity and nuance and collaboration and interactions in these workflows. And it's not like a Google search or a ChatGPT thing where you're just asking for an answer to a simple question, then you get it and you're done. These are multifactorial years long processes that are happening. You have to understand those deeply, and then you have to understand the technology deeply. You have to put the two together, get creative, and figure out what should the new workflow look like, and it's painful. And the big barrier that we're hitting right now is adoption, mindset change, skillset change.
John Furrier
>> Well, Brandon, great to have you on obviously the agent conferences coming up in May here at the Hilton in New York City. What's your plans for that event? What are you going to be talking about? What's on your agenda? What's the big story?
Brandon Rice
>> So by that time, we will... And we're just about to announce this in another week or two, but I'll give you the preview that we are going to be launching our support for market applications for drugs. The whole journey of a drug, the very first submission you do is an IND. The very last one to get approval to go to market is an NDA. We're going to have that whole journey covered in the next couple of weeks. And so that's what we're going to be talking about.
John Furrier
>> With timetables, with the innovation.
Brandon Rice
>> All the content that you need to create to get through that journey. And I'm talking about the 10-year span from your animal studies, through your clinical trials, into your market application. We can support the creation of all of the content along that journey. Just after that, we're going global. So we've been very focused on FDA and US right now. That's half the market for drugs in the world. And then there's the other half. And every country you want to take a drug into, you have to go through the same process with all the idiosyncrasies in that country.
John Furrier
>> Is that a case study or a case study and a template or both?
Brandon Rice
>> It's both. There are non-trivial differences to go from US FDA to EMA in Europe. And so you have to accommodate those. It's not entirely different, but there's a million little paper cuts you got to-
John Furrier
>> I mean, think about the upside for people getting an answer to a drug that could save their life, make their life better, and the money making, don't poo-poo that either. I mean, there's a lot of money being spent and wasted in the process, time, cost. Thanks for coming on theCUBE. Really appreciate it.
Brandon Rice
>> My pleasure. Thanks for having me.
John Furrier
>> Again, mixture of expert series here. It actually kind of crosses over in our life sciences health tech show, but it's going to be really part of the feature of what we think is the big wave, which is the extension of the generative AI to agentic, which is essentially just the work behind all the investment and energy of intelligence in data. Co-founder Ari and Brandon, really strong startup. Weave in San Francisco, we're here in New York. Of course, we have our Bay Area studio in Palo Alto, connecting Silicon Valley and Wall Street. I'm John Furrier, your host of theCUBE. Thanks for watching.
John Furrier