Fastest Growing AI Company Ever? CEO Reveals Revenue

Siva Surendra, CEO of Lyzr, ended 2024 with $0 in revenue. Eighteen months later his AI agent company closed Q2 2026 at $12M ARR, is adding $1–3M of new ARR every month, runs at 95% gross margin, and is on track for $30M by year-end. Potentially $50M if one federal government deal lands. In this interview Siva reveals how Lyzr went from $650K ARR in September 2025 to $12M in nine months (20x), why investors paid a 76x ARR multiple on his $8M Series A led by Rocketship with Accenture Ventures, and how his valuation jumped from $50M to $250M in just four months. He also explains why he built an AI SDR to $4M in contracted ARR in three months and then shut it down, how he beat Palantir on a $2M federal contract with a 10-day prototype, and why he says a $400M acquisition offer would be too low. You'll hear the numbers behind the enterprise AI playbook: 32 enterprise customers paying $250K–$1M each, 80,000 builders on the platform, 400 inbound leads a month converting at 60%, and Lyzr Optimus (a sovereign, on-premise AI appliance priced from $10K to $1M that supports 10,000 concurrent users and costs 95% less than frontier APIs like OpenAI and Anthropic). Siva is now raising a $100M Series B.

• 0:00Speaker 00

  Why is Lizer growing really fast? We are a sovereign, full-stack agent platform that enterprises could run locally within their own cloud or even on-premise system. We kind of replaced twenty different individual technologies under one single homogeneous platform. We closed Q2 this 2026 at 12 million ARR. So we grew by 20x in the last nine months. We are projected to get to 30 million minimum by end of the year. And Nathan, we are operating at 95% gross margin.

• 0:36Speaker 01

  Hey folks, my guest today is Seva Sarandira. He's the CEO of Lizer, a two-time founder, previously built PowerUp Cloud, an AWS consulting firm, which was acquired in 2019, where he then ran the AWS business group globally. Today, Lizer builds enterprise infrastructure for deploying and governing AI agents, pursuing a vision it calls organizational general intelligence. Seva, you ready to take us to the top?

• 1:00Speaker 00

  Absolutely.

• 1:01Speaker 01

  All right. It's good to have you on, and we were talking before the show. You know what to expect. It sounds like you caught some early episodes back in the day, huh?

• 1:08Speaker 00

  Absolutely. I mean, I've been, like I mentioned earlier, I'm a big fan and a lot of, during the early stages of building Lizer, a lot of the videos that you did was extremely helpful, kind of reaffirmed the confidence that a founder needs, to be honest.

• 1:24Speaker 01

  Where did you come up with the idea for this?

• 1:26Speaker 00

  So after I sold my previous startup to this large global system integrator.

• 1:31Speaker 01

  Which one?

• 1:32Speaker 00

  I sold PowerUp Cloud. This is the company that I built and ran, and it was an all-cash deal, which is good for me and the whole team, and we sold it. And I moved to the US as part of the sale in 2020. And I was leading this global AWS business unit at LTI,

• 1:52Speaker 00

  which was doing 65 million when I took over the whole business unit. And I did about, I took it to about 220 million in under two and a half years. So did about 400 million for the business unit in three years. And during that time, I used to go into this QBRs with Excel sheets, spreadsheets and pivot tables, and only for the leaders to ask a lot of questions, which I'm not prepared at all. I mean, I know my business really well, but the line of questioning was very different from the management team. And so when my time was up at LTI, which is when my golden handcuffs came off, I wanted to come back building startups.

• 2:33Speaker 00

  And the timing was perfect because that's when we had the GPT 3.5 KPI come out. So I, obviously as a big data engineer, and I realized, okay, what if we address the problem that I faced? What if we build an AI data analyst that can not only analyze your spreadsheets, but also can

• 2:53Speaker 00

  prepare you with all these questions and ideas that you may have, so that you can understand the data really well? That was the whole thought process. And when I spoke to a lot of these vice presidents in large services firms and banks, I realized that all of them, all these folks are on the same boat. None of them have business analysts or dedicated EAs who does the work for them, which is exactly where AI can play a massive role. While building it, I realized that there is a bigger problem to solve because even as a solo developer, I found it very hard to deploy and manage all these open source packages. And for enterprise, it would definitely be a nightmare. So that insight led to pivot

• 3:39Speaker 00

  in the first three months of starting Lizer to becoming an enterprise framework and platform, allowing enterprises to build AI systems and agents.

• 3:49Speaker 01

  What year did you write the first line of code for the platform?

• 3:53Speaker 00

  April 2023.

• 3:54Speaker 01

  When founders come to, you know, Founder Path to raise from our fund, I go, okay, if it's a solo founder, great. They can move fast, they can break things, they don't have to get seven people to agree. I'm reading here from my research team, co-founders include Jithin, Anki, Anuradha, and then you. Is there four of you? And how do you agree? How'd you split equity on day one? This is a lot of people.

• 4:13Speaker 00

  I start alone, then I start building something. And I have some interns who might probably, I might hire to build something. I get to a stage where I am convinced that, okay, there is some potential here. And then I start building the team together. So Jimmy and, this is Jithin and Ani, were introduced to me. Ani, I knew Ani earlier as well. And Jimmy was introduced to me and we started speaking with one another. And they came on board in, I don't know, Jan 2024, almost six months into the system. And, and then we had Shreyas and Kush, who also are founding architects who came on board. So this is the core team, five of us, of which four of them can code. Ani is our growth expert.

• 4:56Speaker 00

  So that is how the whole team came together. At least that's my style. It's not that I need a co-founder to start something. You just need an idea.

• 5:05Speaker 01

  What did you finish at the end of 2024 with in terms of revenue? Do you remember ARR?

• 5:11Speaker 00

  We finished 2024 at zero revenue, right? But this is what happened. 2023 is when we started building something. 2024 is when we started doing some early experiments. And you won't believe, during that experiment, around July 2024, we built an AISDR as an open source agent on our framework. The idea was to show that, hey, you can have AISDR completely open source. And during that time, 11x and Artisan were two popular AISDR companies that I think Andreessen Horowitz invested 80 million or something in 11x, right? So, so my thesis was that, okay, why should you pay so much for an AISDR

• 5:56Speaker 00

  if you could build it as an open source agent on Lizer framework? And interestingly, we started getting a lot of demand for what we built.

• 6:05Speaker 01

  What was the name of the open source project?

• 6:07Speaker 00

  Jazon, J-A-Z-O-N.

• 6:10Speaker 01

  A lot of people say, Nathan, where, where's the crop of the fastest-growing startups coming from? And I just think if you put a scraper together to track GitHub stars on these open source products, that's a great leading indicator if something's got legs or not.

• 6:21Speaker 00

  We went from zero to 4 million in contracted ARR in three months. Just three months. Now, why contracted? Because we told our customers that, very honestly, that, hey, we don't know whether it'll work, but sign up with us and we'll work with AWS to even fund the internal, the pilot. And AWS has a plan to fund these pilots. We said, we will try and do this for you. And if it works, you convert into a paying customer. So that is why it's a contracted era. They started with a contract. The first three months was covered by AWS and the rest, nine months by the customer. This is the business model that allowed us to show the product really well. And, and then we were able to convert a lot of customers who came on board.

• 7:07Speaker 00

  This was the bull run that AISDR companies had.

• 7:10Speaker 01

  How did you get that initial tranche of like 10, 15 customers? Did it come from the ones that were like starring the open, like the, the open source project or somewhere else?

• 7:19Speaker 00

  So we started seeding on Twitter and LinkedIn and Reddit, highlighting that you have an open source version that is available, that you could try. You don't have to necessarily spend, I think 11x was back then, charging 70,000 to 100,000 dollars per year for our plans. And then Ani, who's my co-founder and head of growth, he's fantastic. His team was able to then

• 7:41Speaker 00

  get the SEO in play. And we started getting 400 inbound leads a month. And we had a 60% demo conversion rate. So that is the bull run that we had in 2024, right? And we went to 4 million ARR, contracted ARR in, by September. Only in October, we said, we are not going to do this anymore. And we stopped doing it because we realized that AISDRs don't work. The promise of AISDRs, if, and Nathan, you would know this really well. The promise was that it is supposed to book meetings for you. It is supposed to write emails, automatically book meetings for you. The moment that promise was broken, customers felt that, okay, you know what?

• 8:26Speaker 00

  This tech is not working. That's exactly what we saw from close quarters. The other SaaS companies were not admitting it, but we saw that, okay, you know what? AISDRs can probably do mass emailing, but can it book a meeting for you? That depends on many other things. What we did, we gave the open source option to all our customers who had already signed up. And we just discontinued the AISDR to go back to the core product, which is the agent framework itself.

• 8:54Speaker 01

  So Seva, what is your prediction for all these AISDR agents? Since you built your own, grew it to 4 million in revenue, then shut it down. I mean, does 11x and Victor and Finn, do they have, you know, Artisan, do they eventually have churn problems and shut down? Or what's your prediction?

• 9:06Speaker 00

  Massive churn problem. It works if you are trying to just send mass emails. The moment you start expecting AISDRs to book meetings for you, that's when it falls flat.

• 9:17Speaker 01

  Even the mass emailing? How are people setting up their email architecture to make sure they have warm inboxes, to make sure they show up in the primary inbox of the Fortune 200 brand that they're targeting? I mean, even getting that to work is not easy.

• 9:28Speaker 00

  It's not easy. Options are like buy 30 to 40 domains on Lemp List or instantly, then start scheduling, use the API to integrate an agent or cloud directly. It's, it's probably the lowest cost when it comes to marketing, which is landing in an inbox. If your agent could write a really good opening email, I think you would have caught the attention of the, of the ICP. Over a period of time, what we realized was, do not try and do the seven email drip campaign stuff, et cetera. Try two emails, hit the point, give them the links and screenshots to decide. If they don't decide in two emails, they're not going to decide in seven emails.

• 10:12Speaker 00

  October 2024 is when we shut down the AISDR product. We went back to a drawing board, which is, hey, we wanted to be an enterprise agent company, let's do that. And we took three more months to build out the developer studio. And Jan 2025 is when we launched the Lizer Agent Studio, which is a low-code, no-code builder platform for developers. That eventually turned out to be a good decision for us.

• 10:36Speaker 01

  Okay, so new launch in January of 2025? How'd you get your first 10 customers? What were you charging?

• 10:42Speaker 00

  Again, good question. So we started with a $10,000 annual plan. Uh. Q one, we we onboarded twenty five customers, mostly startups and SMBs. And we went to two fifty K in ARR by Q one, right? And then by Q three, we were like, we went, we launched a fifty thousand dollar plan and a hundred thousand dollar plan. By Q three, we went to six fifty K in ARR. That is when we signed our term sheet with Rocketship, who led our Series A. Uh. Accenture Ventures participated. But what happened after that is the growth story, right? Q three, twenty twenty five, six fifty K ARR. And uh we closed Q two this season, uh this twenty twenty six at twelve million ARR.

• 11:26Speaker 00

  So we grew by twenty X in the last nine months. We are projected to get to thirty million minimum by end of the year. Uh. But there is one large government deal, and if that comes in, we'll get to fifty million by the end of the year.

• 11:37Speaker 01

  What's your largest customer pay you today?

• 11:40Speaker 00

  It is a federal agency, close to two million dollar. Why is Lizer growing really fast? We are a sovereign, full-stack agent platform that enterprises could run locally within their own cloud or even on-premise system. That is what we are. So today, companies are understanding what OpenAI and Anthropic are doing. They are obviously charging you for tokens, but eventually they're able to take all the intelligence away from you. The if you're an insurance firm, the moment you upload your underwriting handbook to OpenAI or Anthropic, it's gone, the intelligence is now with them. They can share the same decision-making to every other insurance firm in the world.

• 12:25Speaker 00

  Your secret sauce just went out. So this is something that enterprises are reeling very realizing very quickly. So they are looking at full-stack sovereign platforms like us, which they could deploy within their own cloud or even on-prem systems so that they can really own the intelligence. And that is what is resulting in our growth. Lizer is full-stack, which means all the necessary building blocks that you need for a complex agentic system are available within our single platform. We kind of replaced twenty different individual technologies uh under one single homogeneous platform. Which is why uh we had Accenture invest in us twice in six months, and they are the world's largest IT services provider.

• 13:09Speaker 01

  As someone watching this as a founder of a ten million dollar ARR company, I'm going, yeah, I want to keep my alpha and my intelligence with me. But how do I, like, do I have to? If I think back to like the the rack space days, do I have to have a closet on my business with a bunch of HP two hundred, you know, H two hundreds running or Blackwell's running? And then put your application layer on top of it? Like, what's the infrastructure under it? How do I get the compute power if I use your application?

• 13:32Speaker 00

  It is going to be a combination, Nathan. It is going to be an on-prem appliance which has H two hundreds, H one hundreds or AMD chips. So that is your on-prem appliance, completely cut off from internet. But you can, your CFO offers your R and D team, your uh say biotech uh team could use it for local intelligence to build agents and automate a lot of work. Which is why just last week, we launched Lizer Optimus, which is an on-prem appliance that you can buy from us. It can support up to ten thousand concurrent users. Uh. You won't find that on the platform yet because we just started pre-orders, pre-orders for our existing clients. So twenty percentage of any enterprise workload will run with local hardware.

• 14:14Speaker 00

  And about next fifty percent will be on one of their hyperscaler platforms like Amazon or Azure or Google. And they will be using models that these hyperscaler platforms provide. Or they will be using open source models behind bedrocks of the world, which is semi sense uh data sensitive. And the rest twenty percentages where or thirty percentages where they will be open to sharing with OpenAI or Anthropic directly.

• 14:38Speaker 01

  Well, so let me, let me ask you a follow-up on this because my audience is a combination of tech people that are following everything you're saying and people that are not tech and they might get lost. And that's sort of me. So if they think literally about, okay, I want to own my intelligence, are they going on eBay and buying for forty grand, you know, ten H two hundreds? And then putting in a closet and then putting your app on top of it and running it? Or are they renting, are they renting GPU space from a middleman somewhere? And then putting your intelligence on top of your application layer on top of it?

• 15:03Speaker 00

  Three options, right? The option that is on fire right now is the inference providers. Together AI, Fireworks and Bastion. These companies are inference providers. They allow, they give you open source models and you can fine tune, so you don't have to buy hardware. And these companies ensure data privacy. They are not the big labs like OpenAI or Anthropic who use, who uses your data to retrain something. Their job is straightforward. They will host an open source model like a DeepSeek, for example, or a GLM or a Quen. Uh. And then they give you inference capability.

• 15:39Speaker 01

  When what makes their business model hard is or is it easy to copy? You just have to go rent GPU space from the the hyperscalers and then rent it out at a higher rate.

• 15:47Speaker 00

  Uh. Sometimes they rent some and mostly they buy, so it's all about demand. So two things. One, do you have enough hardware capacity? And number two, do you have a really uh I would say optimized software stack? The reason why Together, Bastion and Fireworks are good is because their software stack is very strong. Which is why if you are an individual, you don't have to buy these uh Nvidia H two hundreds yourself. You can rent it out from these inference providers. Option number two, big hyperscalers like AWS, Azure and Google, they also have some capacity. Corevive, all the Neo cloud, right? Corevive, Nebius, they also have these options.

• 16:28Speaker 00

  Uh. Then option number three is buying hardware appliance from companies like us. This is Lizer Optimus, an on-prem agent factory for enterprises and teams who wants to own their intelligence, right? It comes with uh, it can support up to ten thousand concurrent users, which means even a large bank can adopt this. Ninety-five percent lower cost compared to a frontier API usage like GPT five point fives of the world. It just takes thirty minutes to set up and get started.

• 16:56Speaker 01

  What are you going to sell this for?

• 16:58Speaker 00

  So there are four configurations that we are launching. Uh. Optimus Personal is for the CXOs. Imagine a CEO or a CFO who wants to have a device on their desk. So that is Optimus Personal, starts at ten thousand dollars. Optimus Max uh is one million dollars. It comes with eight uh small models, two medium models, two large models, one OCR model and one voice model. It can support up to ten thousand users.

• 17:23Speaker 01

  What's the difference between your Optimus Personal plan at ten K versus like what most people are doing now, which is like stacking Mac Studios for three K a pop?

• 17:30Speaker 00

  It is not just the hardware that you get. What you also get is the software, right? This is what you get, the whole studio.

• 17:38Speaker 01

  Wait, wait, tell, okay, so this is great. I love a visualize. So just to be clear, we're, this is like what a paying customer would see. We're now logged into your platform.

• 17:44Speaker 00

  Yes. You get an end-to-end studio where all the agents that you are building across are, sorry, are registered here. You have an option to create a task agent or a voice agent or even a workflow or even bring third-party agents via proxy agent. Um. You are able to build complex multi-agent systems through the studio.

• 18:06Speaker 01

  How many customers are you serving today?

• 18:08Speaker 00

  Today, thirty-two very large enterprise customers. Uh. Like I said, the deal values are between two fifty thousand dollars to one million dollars and one customer obviously closer to two million dollars. And we have eighty thousand builders on the platform. You can go to studio today, create a free account, you get five hundred credits and you can start building agency yourself.

• 18:29Speaker 01

  Why would they build on you versus going directly to Zapier, N A N or all these other agent building tools?

• 18:35Speaker 00

  Lizer has fifty percent more agent building blocks or modules compared to what even some of the best uh top agent platforms in the market have today. So it is a comprehensiveness of what we bring to the table allows us to uh obviously help our enterprise customers build really complex systems. This is developer studio, right? Built for developers. We then wanted to build something for business users, which is why we built Architect dot new. So this is something that I would encourage the users to try. This is purely built for business users. It is not just an app builder, but an agent builder primarily. So Architect goes out and plans in detail the kind of agent that you want to build. It then visually builds the agent for you, writing the prompt, selecting the models automatically.

• 19:20Speaker 00

  Then it also builds the app for you, all three in one shot.

• 19:24Speaker 01

  The argument uh that you're making is, hey, when you build the agents inside of here, it's your own data, it's private, you're not passing your business intelligence to the foundation models who then might distribute it to every other law firm on earth.

• 19:34Speaker 00

  That's correct.

• 19:35Speaker 01

  September twenty twenty five, you hit six hundred and fifty thousand dollars of ARR and you did it eight million Series A, right?

• 19:40Speaker 00

  That's correct.

• 19:41Speaker 01

  Are you comfortable sharing the valuation?

• 19:43Speaker 00

  Uh. Yeah, we did it fifty million valuation.

• 19:46Speaker 01

  So I mean, how did you think about the multiple then? I mean, look, I mean, obviously you want to get the highest multiple possible, but you've been through this before. You know, raising at a high multiple can sometimes hurt you.

• 19:53Speaker 00

  Yeah, yeah. And also, uh, I'm not a Bay Area founder, I'm a New York founder and I'm an immigrant. Um. So I didn't have any network. So raising the first round was pretty hard, to be honest. In spite of my previous success that I had, we had to raise purely on our ARR growth. So it became just a multiple of whatever ARR was.

• 20:11Speaker 01

  That's a seventy-six X ARR multiple, right? On six hundred and fifty K of ARR?

• 20:15Speaker 00

  Because we had a good pipeline. But he he here is what interesting thing, right? This is October. In Feb, we did our Series A plus at two fifty billion valuation. Our valuation jumped five times in a matter of four months.

• 20:29Speaker 01

  What was revenue in February this year?

• 20:32Speaker 00

  Uh. In Feb, when we raised that round, it was three and a half million.

• 20:37Speaker 01

  So you've gone from three point five million of ARR in Feb of twenty twenty six to now to June, twelve million of ARR. So in three, four months, you more than almost trip more than tripled revenue.

• 20:47Speaker 00

  Yeah, and in July, we will be at fifteen and we are targeting thirty by the end of the year.

• 20:51Speaker 01

  You're still trading in a seventy X revenue multiple. I mean, but your growth, you'd argue the growth justifies it.

• 20:56Speaker 00

  That's correct. And and Nathan, we are operating at ninety-five percent gross margin.

• 21:01Speaker 01

  What about net, net margin? Are you profitable or burning today?

• 21:05Speaker 00

  We will break even in 30 days.

• 21:07Speaker 01

  Okay, if you wanted to, I imagine you're not. You're going to keep burning to grow. That hardware you just showed me, the most expensive one for a million bucks, what will your cost of goods sold be on that based off the manufacturer, the chips you're putting inside it, et cetera?

• 21:20Speaker 00

  I think just the hardware bomb alone is 50%, 48 to 50%.

• 21:24Speaker 01

  So you're, you're reverse engineering to get like a 48% hardware margin and then you'll upsell obviously 95% margin, software and application layer on top of it.

• 21:32Speaker 00

  Exactly, because the real value is the software, right?

• 21:34Speaker 01

  So how much total have you raised to date?

• 21:36Speaker 00

  14.5 and we are now in talks to do our series B. It's 100 million round.

• 21:40Speaker 01

  So you're like growth at all costs, basically. What's the fastest way to do? Do you go through inorganic growth, buy a bunch of companies in your space just to buy your way to 100 million of ARR? Or just keep doing what you're doing, grow organically?

• 21:50Speaker 00

  I have a different take on inorganic growth. We have several other system integrators investing in Lizer and also partnering with us because we help them ship agents that can go to production for their end customer. So that is inorganic growth for us, right? If you make your partner win, they will take care of building the pipeline for you, bringing in the demand.

• 22:11Speaker 01

  What I'm trying to wrap my head around is when you're going head to head with Palantir on a deal and it's a, and the government, the US government is the buyer, how are you winning these $2 million contracts, right? People are thinking Palantir, they raised a bunch, they're worth billions of dollars. Why would they give, you know, respectfully, right? Your little startup a shot?

• 22:29Speaker 00

  Exactly. I think that is the underdog tag that gives us the, the advantage both in two deals, both Palantir and the other large enterprise, which is a critical infrastructure for the US, who I cannot name. And the other, the first one is the US government themselves. Both deals were Palantir compete. We were able to win number one, because of the product superiority, because the product is agent first, right? We, we didn't have a baggage of ontology and all the other previous softwares to sell. We just had one focus, which is how do I build agentic systems that can go production? So that is the focus, that is, I think that product superiority helped us win. The second one is we were able to ship a prototype in 10 days to our customer. It's always been a dogfight, Nathan, to be honest.

• 23:14Speaker 00

  Like almost all the large deals that we won, we were picked against either a Palantir or an agent bricks, or one of these are Lang chains all over the world. It is always one of these dogfights that we have to win.

• 23:26Speaker 01

  If one of those firms approached you today and offered to buy the whole business for $400 million all cash upfront, do you take the deal?

• 23:33Speaker 00

  They can't afford us. But yeah, we are currently raising $100 million round and you, you could probably guess the valuation that we are moving towards. So, so which is why.

• 23:42Speaker 01

  No, I can't. What, what valuation are you targeting?

• 23:45Speaker 00

  Let the market sets too early right now, so.

• 23:48Speaker 01

  How much revenue ARR do you think you'll add in this month?

• 23:51Speaker 00

  We have a Q3 target of 20 million. Q4 and Q1, we grew 300% quarter on quarter. Q2, we grew 200% quarter on quarter. And obviously as the number grows up, the percentage comes down. So this quarter we will probably grow 80% so.

• 24:06Speaker 01

  So is it fair to say you're adding 1 to 3 million of new ARR per month?

• 24:10Speaker 00

  Yes.

• 24:11Speaker 01

  Listen, on that note, if people want to follow your story, Siva, where can they find you online?

• 24:15Speaker 00

  I'm, LinkedIn is the best place. You can find me Siva Surendra Lizer. I'm on LinkedIn.

• 24:22Speaker 01

  Guys, there you have it. Started in 2008 as a big data engineer at Tesco in Bangalore and look at him now. Siva, thank you for taking us to the top.

• 24:30Speaker 00

  Thanks, Nathan. Thanks for having me.

• 24:32Speaker 01

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