Questions Every Founder Must Answer Before Taking an Acquisition Offer | Shashank Saxena, VNDLY & Pantomath

Most AI failures won't come from a bad model. They'll come from bad data.

Shashank Saxena spent most of his career on the buying side of enterprise technology before founding VNDLY which was acquired by Workday for $510 million. He then joined Sierra as a Managing Partner before going full time as Co-founder and CEO of Pantomath, a data operations center for enterprises that are betting their future on AI agents.

We discuss why data quality is becoming one of the biggest challenges in enterprise AI. 

An AI agent fed bad data for 12 hours doesn't go rogue. It just makes 12 hours of wrong decisions: rejecting insurance claims, issuing credit cards, or drilling in the wrong location. 
As more business decisions are delegated to AI systems, companies will need far greater visibility into what is happening across their data infrastructure.

Shashank also shares the decisions that led to VNDLY's acquisition, the advice he'd give founders evaluating acquisition offers today, and why a Michael Jordan analogy continues to motivate him as a second-time founder.

If you're building enterprise software, selling to large companies, or trying to figure out whether experience is an asset or a liability in the AI era, this episode is for you.

0:00 - Trailer
01:00 - How Shashank became a second-time founder
07:20 - Where Pantomath sits in the data stack
10:55 - How a broken Tableau report turns mission-critical with AI
12:55 - Who Pantomath sells to
15:35 - Solving for a problem that doesn't exist yet
19:03 - How have founder expectations changed today?
20:31 - Series B companies pre- and post-AI
21:26 - The Michael Jordan example
23:57 - How a repeat founder chooses investors
25:10 - What value Snowflake adds as a strategic investor
27:05 - Data is not an open category today
28:34 - The astounding Databricks outcome
29:08 - The reality of the $100 million ARR number
31:48 - Will non-human workers 100x in the next few years?
36:00 - How to protect data in motion
37:26 - How comfortable are we giving full access to agents?
39:47 - Where is automation fastest today?
42:09 - Why entrepreneurs tend to like uncertainty
43:28 - Why Shashank chose to be a founder
45:48 - A customer-driven $510M acquisition
48:32 - Employees vs contractors in any organization
51:22 - Building from Ohio vs the Bay Area
53:14 - Learnings from selling to enterprises
56:31 - How Shashank raised from Tier 1 US VCs
59:19 - Heads down or network as a founder?
1:02:47 - First-time vs second-time founder edge in AI
1:06:22 - Hiring as a repeat founder
1:08:08 - How enterprise sales has changed
1:10:52 - How do you sell for a problem that isn't visible today?
1:12:58 - Best piece of advice
1:16:27 - The only advice for a founder considering M&A
1:21:06 - Position yourself to be capable of taking risks
1:24:51 - What matters to an enterprise buyer?

-------------

India’s talent has built the world’s tech—now it’s time to lead it.
This mission goes beyond startups. It’s about shifting the center of gravity in global tech to include the brilliance rising from India.

What is Neon Fund?
We invest in seed and early-stage founders from India and the diaspora building world-class Enterprise AI companies. We bring capital, conviction, and a community that’s done it before.

Subscribe for real founder stories, investor perspectives, economist breakdowns, and a behind-the-scenes look at how we’re doing it all at Neon.

-------------

Check us out on:
Website: https://neon.fund/
Instagram: https://www.instagram.com/theneonshoww/
LinkedIn: https://www.linkedin.com/company/beneon/
Twitter: https://x.com/TheNeonShoww

Connect with Siddhartha on:
LinkedIn: https://www.linkedin.com/in/siddharthaahluwalia/
Twitter: https://x.com/siddharthaa7

-------------

This video is for informational purposes only. The views expressed are those of the individuals quoted and do not constitute professional advice.

Transcript

SPEAKER_03 0:00

It's not that the AI agent went rope. It's for 12 to 15 hours the AI agent would spend bad data. And that's why I made a bad decision. We would have started seeing front page headlines saying this credit card company issued 1 million credit cards, and now they need to withdraw those back because they could have never issued that. When we raised our CDP for this company, revenue-wise, we're almost identically the same number as when we raised our CDSP for my last company. At my last company, we were 75 employees, and we probably paid for like five, seven, eight software products. This time, for that same revenue, we were probably 35 people, but we licensed and used 50 to 70 products. Doing has gone 10x, and people have gone 50 to 70. It's almost not the same game. When I talk to other founders who have been around the block and seen it, we end up discussing. It's like the Michael Jordan example.

SPEAKER_00 0:51

In the data category, what are the remaining opportunities for entrepreneurs?

SPEAKER_03 0:54

I don't know if that's the category I'd look at today, mainly because.

SPEAKER_00 1:02

Hi, this is Sudhahat Al Uwalia, your host at Neon Show and managing partner at Neon Fund, a fund that has invested in some of the best enterprise AI companies that's starting from India and building for the globe, like Atomic Work, Spot Draft, CloudSec, and many others. Today I have with me Shoshank Saksena, founder of Pantomath. Sashank, welcome on the Neon Show.

SPEAKER_03 1:25

Thanks, Siddhartha. Thanks for having me. I'm excited to be here with you.

SPEAKER_00 1:28

You co-founded Pantomath with your brother, which is very exciting, right? Yeah. Knowing your co-founder for your entire life.

SPEAKER_03 1:34

That's right. We've been roommates actually for most of our life until we got married. So yeah.

SPEAKER_00 1:39

That's amazing. And uh you both moved to US together?

SPEAKER_03 1:43

That's right. Uh we completed 20 years in the US in towards the end of March. So uh very recently. Yeah, we both have been in the US together.

SPEAKER_00 1:51

Would would love your journey growing up in India, moving to the US, uh, right? Uh and then starting, you know, possibly your first company, Windley, and then Pantomath.

SPEAKER_03 2:02

Sounds good, yeah. Maybe I'll cover that a little bit as part of the intro. Uh, so yeah, born and raised in India, in Mumbai, lived there my entire life. In fact, our family has been settled in Mumbai for the last, I would say, three generations in pretty much that same house. So uh a lot of cousins around, a lot of family around, and uh growing up in Mumbai was fun. I still have some of the most fondest memories of my life there. Uh and then, yeah, so then I moved to the US in 2006, right after undergrad. I had an undergrad in computer science, and then uh here I moved to the University of Cincinnati because they gave me a scholarship for grad school. So I graduated in 2008 in the heart of what was very well known now as the global recession. And uh of all places, actually, I started off at Citibank. Uh so as the bailout and all of that stuff was happening, I was starting my career at Citibank. So got to learn a lot of interesting lessons. Uh, I've only had actually three main companies that I've worked for in my career, uh, predominantly speaking, like 90% of my career has been those three, where I started at Citibank, I worked there for a few years, got into digital and digital transformation. From there I went to Kroger, I led digital transformation there, and then I left to start a company called Wendley, uh, which was acquired by Workday, and then I was at Workday. So those were the three big pillars of my career. And I had a brief stint at Sierra where I'm still a partner now. I was a managing partner, and then now I'm doing Pantomath full-time, so that's why I'm co-founder and CEO of Pantomath now.

SPEAKER_00 3:32

And you know, before we dive into deeply into the journey of Wendley, I would love to start. How did you and your brother ended up starting a company together?

SPEAKER_03 3:40

So my previous company, Wendley, had exited and sold to work day. We were celebrating that at one of my friend's house. He was hosting a dinner. This was our college roommate. Uh, so my brother came over as well. My brother ran data operations at GE Aviation. And one of the migrations was not doing too well. He came in and he was like, hey, this is he he looked as if he had not slept the entire weekend because he actually hadn't slept the entire weekend, right? Uh and most people who have run data operations will resonate with that experience where they know when things fail and go bad, things can go really bad really quick. And then the ability to have visibility and know all the different moving pieces uh was not that easily available. So uh one of the things he thought about was there should be tooling that can help and show the entire picture of what's connected to what, all the moving pieces that does observability, alerts, incidents, management, all of those things. And he was toying with the idea of should he build a product like that.

SPEAKER_01 4:46

Yeah.

SPEAKER_03 4:47

And given that I had just been on the entrepreneurial journey myself, I very strongly encouraged him to take the leap of faith and get started and not just solve the problem for GE, but for every company around the world by making this a SaaS product and getting started on that journey. So he resigned, quit his job at GE, gave some thought, and went out and raised some money, and that's how Pantamath got started. So I was not actively running the company until recently, until March of last year. Uh I was always on the board and a shadow co-founder. Uh, but now I'm full-time running Pantomath.

SPEAKER_00 5:25

So what what made you join? Like the company started in 2022 and you joined in full-time in 2025. What made you take that leap?

SPEAKER_03 5:33

Um, the opportunity is really exciting, right? Because between 2022 and 2025, like this whole thing with AI happened, and AI got more and more real because ever since that first, I would say, Chat GPT moment in 2022, uh, everyone thought, oh, like we've had seen this bubble before, it's a temporary bubble, it's going to pop. And today we are like almost what four years in, and there is no signs of anything slowing down anytime soon. In fact, it's getting even more and more aggressive, the arms race for AI. Uh, so it's a very exciting time because for Pantamath, when we started, we were just focused on data observability, one thing only. Today, what we've done is we've transitioned into forming a full-blown data operation center. Right? So, just like in cybersecurity, every single CISO uses something called a SOC, a security operation center. It's one of their most critical pieces of software and tools that they use to understand the security posture of the entire enterprise. And we're taking that same concept and bringing it to the world of data, where we're building a data operation center where people, CDOs and CTOs and CIOs can see the end-to-end movement of data enterprise-wide. So, again, the nature of the problem statement has changed because now we're taking on a bigger and more ambitious challenge. Because it's not just observability, incident detection, it's data operation center plus agent tech AI-based resolution. Because I believe, and this is one of the theses we have, is customers won't just want to know what happened, they're like fix it and then tell me what happened, right? Uh, so the ability to fix it is uh going to be the key and most critical piece of this whole equation. So now it's again the nature of the or the scope of the problem statement has changed.

SPEAKER_00 7:20

God. And how did you move, let's say, uh over a period of time from data observability to a complete data operation center?

SPEAKER_03 7:28

Uh, this is where when we think about moving, right? A lot of it comes down to listening to your customers. Because early observability is like, okay, you see issues and say, now what? Like, sure, you sent me an alert, you told me there's an issue, what do I do with it? Or you showed me a lineage graph, what do I do with it? But now with AI agents, we're uniquely positioned where it's like you can take action on things, right? You can I you can write a whole resolution use case, you can write a whole root cause analysis paper on it. You can show how the agent has thought through the root cause analysis and why it's proposing the solution it is. So all of those building blocks and underlying components are there and getting better very quickly. So that part is really exciting because we couldn't, we could have not done that four years ago, right? So it was partially lack of vision and but more importantly, lack of the underlying tooling, which is there today, thanks to Anthropic and OpenAI and Gemini and all these other products that that provide the tooling where you can go do these things.

SPEAKER_00 8:33

Got it. And where in the the data stack do you sit? And what are the other tools that sit in the data stack usually in your customers?

SPEAKER_03 8:41

Yeah, data stack has gotten very interesting, right? So back in the days, people just used to have like a Terrarata data warehouse and a couple of things around it, and it was really, really simple. Today, if you look at most Fortune 500 companies, because we deal with large enterprise Fortune 500 customers, today, if you see how data is organized across them, on the extreme left, you have your core ERP systems, your source system, right? Your Salesforce, your SAP, your Oracle, your workday, your Coupa or Reba, depending on whatever the system of record is for what category, you have those. Data flows from there through ingestion tooling, which is FiveTran and Informatica and ADF, and there's Talon and Click, like there's a dozen players in that category through ingestion tooling into a Databricks Lake House, into a Snowflake warehouse, through DVT labs or whatever else that they're using for there, or MLOps with Data IQ, to Tableau and Power BI for visualization. And in between, you'll have an orchestrator like either Airflow Astronomer or uh Control M or Atomic that's owned by Broadcom, like one of those. And through all of this, because you have so many different hops and so many different verticals, what's the horizontal where you can see it all? Right? When you only had a couple of verticals, you didn't need that horizontal cross-platform product to be able to bring it all together, right? As we call it the glue. And now, when data has to move through these many hops, when the tableau report breaks, the first thing people do is open a ticket in Service Now and say, there's a Tableau report issue, or 20 Tableau reports are broken. And in that ServiceNow ticket, the person in India, generally offshore, who gets assigned that ticket, what's the first thing they do? They open a triage call and a support call and they ping the tableau admin saying, get on the call, this tableau report or Power BI dashboard is not working fine. And very rarely is it a Tableau or Power BI issue. It's something upstream somewhere that has failed, but no one knows what. And the admin that knows Snowflake is generally different from the admin that's dealing with Tableau. So, like, who knows where the issue is, right? And that's where we bring it all together. Uh, so that way issues can be resolved in real time. Because the thing is, if you think of not just today, but like let's fast forward two years from now. Today, if a tableau report is broken and it broke at midnight last night when the job failed, uh someone catches it at 9 a.m., 10 a.m. the next morning, they have it resolved by noon or 2 p.m., 12, 14 hours. It's just a bad broken report. You can live with that, right? That's not as mission critical. But as AI is getting more real in the enterprise, that same broken tableau report today is a broken data feed that feeds into an LLM and an AI agent tomorrow. So at some point, you're gonna start seeing front page headlines in the Wall Street Journal saying this healthcare company or insurance company rejected 1 million claims, which it shouldn't have, because the AI agent went rogue, or this credit card company issued 1 million credit cards, and now they need to withdraw those back because they should have never issued that. And an AI agent went wrote. It's like it's not that the AI agent went rogue, right? It's for 12 to 15 hours the AI agent was fed bad data, and that's why it made a bad decision. So as AI agents and automation and these use cases become mainstream in the enterprise, the ability or the cost to avoid an incident will go up pretty drastically. And that's really one of the key fundamental bets we are making is there's going to be a lot more discipline and rigor. So cybersecurity today is run with a lot of discipline and rigor, right? Millions of dollars are spent to avoid that one incident to keep you off the front page of a Wall Street Journal article, right? We feel that data is going to start running with that same amount of rigor because that one incident puts you in bad light on the front page of a publication and it's not a good look for the company. Yeah. So that's really the core underlying hypothesis that we're building on.

SPEAKER_00 12:55

Got it. And what are the existing tools uh in the stack that you have replaced?

SPEAKER_03 13:01

Uh we generally don't replace. We help bring it all together. Uh, so that's why, even like if you look at let's take data catalogs, right? Uh, we are partners with Athlane Inalation and all these companies because uh we we believe like it's BYOC, bring your own data catalog. Like that's not the business we want to be in. We want to be in the business where we bring it all together. It's that centralized command center view where everything that you have, you still have your own tooling, but we show it all in one place. And if there's an issue, because we run monitoring, we can go ahead and resolve the issue because you see it all in one place.

SPEAKER_00 13:36

And who is the enterprise uh uh you know ICP that you sell to?

SPEAKER_03 13:41

Uh upper mid-market and then above, right? So generally Fortune 500s or global 5000, I would say, falls in that category. Uh SMB is just not our target area. Lower end of mid-market becomes a little too small for us, right? Because if you don't have too many of these tools in the mix, then it's harder. Like 30% of our customers use Snowflake and Databricks both.

SPEAKER_01 14:02

Okay.

SPEAKER_03 14:02

Right. So they have a Databricks Lake House and Snowflake warehouse. So that's the volume that we deal with.

SPEAKER_00 14:08

Got it. And do you sell to the CIO or the chief data officer?

SPEAKER_03 14:12

Uh generally, it's three different personas that are part of every buy cycle. And it varies in terms of who's the primary versus who's the secondary and who owns the budget center versus not. Uh, the three personas is office of the CIO CTO, right? Because they own the platforms and supporting the platforms, which is uh they're the ones who pay for Snowflake or Databricks or whatever else. Second persona is the CDO, the chief data. Now the new title is CD AIO, right? So chief data and AI officer. Uh they are responsible for the quality of the outcomes and deliverables of the metrics, which is like, hey, my tablet report and my analytics stack, is it producing value? Uh so that's the second persona that's involved in the equation almost always. Uh, and the third persona is generally someone who owns operations and support. So that could be like an SRE team, and generally that falls in the CIO, CTO org, but sometimes it sits outside where they've outsourced operations or they've given it to one of your offshore providers. Uh, so like the the vendor management, because a lot of the offshore providers they log into our tool to do the job because a lot of companies have outsourced that, offshore that. Uh, so they are generally involved in the process as well. They're not the budget holders, but they're the users. Uh, so that's generally these three personas are involved in most of our decision-making cycles.

SPEAKER_00 15:35

Got it. So, so what I try to understand is this is a new completely new category, data operations center. Did you coin the category, like the the pantomath founders?

SPEAKER_03 15:45

Uh we are the only ones we know of that are in this category. Okay. Right? So uh data operation center, I don't know of other players in the category, but yes, that's that's how we think about it. And uh I don't think the term is essentially new, right? Because knock and sock have been around for so long that it is only a matter of time that someone would come up with a talk. Uh, but the way we think about our product vision and strategy, like we think that's pretty valuable.

SPEAKER_00 16:11

So so when you're going to your customers, you are setting a new category.

SPEAKER_03 16:14

Yeah. Which is a pretty hard thing to do. That's very hard to do. That's very, very hard to do.

SPEAKER_00 16:19

Because you are asking for a budget that doesn't exist.

SPEAKER_03 16:21

That's right. That's right. And we are also, yes, and we are also trying to solve a problem that doesn't exist yet, right? Because it's not like AI agents are all over the enterprise going rogue and making bad decisions. We are saying you need to be proactive before you put these agents in production. You need to have guardrails to make sure the data feeds getting into these agents that where the models are being trained on are pristine and you have your act together. So your agents end up making good decisions and actually end up hitting productivity benchmarks that you hope and keep you off the front page news. So, so yes, not only is it a category that it doesn't exist, it also is uh a problem that is not mainstream today.

SPEAKER_00 17:05

So, so but you have achieved a significant scale, you have raised a series B from Channel Catalyst. Yeah, right. So uh without a category that is fully baked in, how did you manage to achieve all these things, right?

SPEAKER_03 17:18

Because it's it's that customer curve, right? There's always forward-thinking customers, there's always early adopters, there's always customers. Like the good news is AI today is a board level conversation at pretty much every global 5000 company. There is no company out there that's not thinking about AI or discussing it at the board level and CEO level. So when the mandate comes from the top saying we need AI and agent tech productivity, then it becomes okay, what are the tools I need to be able to push AI into production? And what are the guardrails we're going to put to make sure that we don't fall into trouble by doing it? So that latter part of the sentence, like what are the guardrails we put, we fall very squarely into that category. Right. And that's a very hot topic of discussion right now in the enterprise. So it's not like we're spending millions of dollars on marketing trying to do category creation and that. It's falling into that there are tailwinds in this business where everyone's thinking about what guardrails to put in so that my AI agents don't go rogue and we just happen to be playing in that space.

SPEAKER_00 18:25

So right now you're selling directly to the enterprise or through partners?

SPEAKER_03 18:29

Uh today it's all direct to the enterprise. Uh we're just starting to go through channel now, but that's very new for us.

SPEAKER_00 18:36

What helped you open the first 10 enterprise doors?

SPEAKER_03 18:40

So observability is how we started. And a lot of customers that bought in the first, I would say, 10, 15, 20 customers were all data observability customers. Uh and now we are through that journey evolving beyond data observability into a data operation center, which is a lot more robust feature set, which pulls in a lot more of the cross-platform stuff that we end up helping the customer with.

SPEAKER_00 19:04

Understood. And you know, on your journey, because you're a second-time founder, this time I believe there was more VC inbound than the previous journey. Is that true?

SPEAKER_03 19:14

Yeah, yes, that is true. But that's yeah, I mean, once you've had a decent exit, then that gets easier. But having said that, uh, the expectations have changed, right? Because uh during my last journey, like it was always about how do you get to 100 million ARR? Is there a pathway to get there? And you're you're a unicorn, right? Today with AI, we are seeing companies, it's it's not it's not impossible to see a company that gets to 100 million ARR in two years with less than 50 people. Right? And that just was unheard of. And back then, a repeat mature founder or a seasoned entrepreneur or someone who has deep enterprise experience, the value was there. Today, in this bucket that I mentioned, is a company that gets 200 million ARR in two years with less than 50 people, that founder persona is generally not a 15, 20 years experienced veteran in the enterprise, it's generally a much, much younger persona of a founder that is often fresh out of college and comes in with lateral thinking and is rethinking the problem statement and how they're gonna build a company ground up using all sorts of tooling. Uh so that has changed drastically. Uh, the other thing is when we raised our series B for this company, revenue-wise, we were almost the same, almost identically the same number as when we raised our series B for my last company.

SPEAKER_01 20:42

Yeah.

SPEAKER_03 20:43

At my last company, at that stage, we were 75, 80 employees, let's call it 85, maybe on the upper end. Uh, and we probably paid for like five, seven, eight, like a single-digit number of software products that we licensed and used, right? Like HubSpot CRM or this or that, like payrolling HRs. Like that we had the bare basics. This time, for that same revenue, we were probably 35 people, so like less than half, or right, about half the headcount. But we license and use 50 to 70 products.

SPEAKER_00 21:18

So, tooling has gone 10x.

SPEAKER_03 21:19

Exactly. Tooling has gone 10x and people have gone 50%. So it's almost not the same game, right? And as a repeat founder, when I talk to other founders who have been around the block and seen it, very often we end up discussing, it's like it's a little bit like the Michael Jordan example, right? My Michael Jordan was a great, great, great basketball player. And uh at some point he decided to take a break in his career to go try baseball.

SPEAKER_01 21:48

Yeah.

SPEAKER_03 21:48

And he didn't do that well in baseball. In basketball, he is great. And for us, and this is true for me, where I think about this all the time what do I need to do to retrain, redo, then reskill? Myself to be relevant in today's day and time because things are changing just so, so, so quickly that I don't want to be, and I'm not saying I'm Michael Jordan or anything, but I don't want to be stuck in that the metaphor where it's like I'm trying to try my hand at baseball and not doing as well, right? So uh we we just have to constantly be learning and evolving, and I'm trying to spend a lot more time with a lot of younger people trying to see how they think, how they are solving problems. Uh we're trying to bring that that energy into our company too. Uh my last company, we only hired super senior developers, people with 15, 17, 18 years experience. Now we're blending that too because different people have different ways of doing it. So yeah, I mean that's our way of staying relevant, or at least trying to.

SPEAKER_00 22:47

So, did do you see 23 years old selling to the larger and like the largest of enterprise today? Which was not possible.

SPEAKER_03 22:54

Like, yeah, it happens all the time. You see some of these growth benchmarks and growth numbers at these companies, I think. Now, it's a different kind of revenue, right? A lot of like in my last company, we we built an ERP system. That's like locked-in, sticky revenue with close to zero churn. And the customers I signed in 2017 and 2018 are still customers using the platform today, right? Uh these days, like this persona of the company you're talking about that get to 100 million ARR real quick, it's not necessarily the same stickiness of revenue and time will tell. So the question is what multiple should be associated with the revenue, what's the true valuation of that company? Should you value it more as a traditional software SaaS business or should you value it more as a services business, where it's a one-time revenue and profitability matters more than uh like so CAC LTV ratios, all of those things. That's all under question because I just don't know where the puck settles then. It's it's a little too early to tell or we have mid-cycle to tell. Uh, so we'll only know that in hindsight.

SPEAKER_00 23:57

Yeah. And and this time, how did you choose your investors at every round? Because I believe uh being a repeat founder, you have the luxury of doing that, right? When there are multiple offers on the table.

SPEAKER_03 24:08

Uh yeah, but this one for all the way up to series B. So series B is the first round I raced at this company because Seed and A was done by Somage. So I just made a bunch of intros for him and then it played out that way. Uh so but we've been very happy. Uh, Epic and Bowry did the seed round. They were investors in my last company too. Sierra did the A, and I ended up I like the team so much that I ended up joining Sierra as a GP myself. Uh Quintin at General Catalyst is legendary. He's really, really good. He's involved in a lot of meaningful companies uh that are becoming very real businesses. Uh so we were very, very fortunate to have him on board. Uh Snowflake Ventures came in as a check recently. Uh so yeah, overall it's been a very, very good roster and very good, uh, very good people around the table. So in terms of board meetings to give us feedback, uh, Hitachi Ventures and Investor from a strategic standpoint. So yeah, we've we've we've got a good group around us.

SPEAKER_00 25:06

And how do you get uh let's say uh strategic ventures like Snowflake, Hitachi, or the founders listening to it? Because I believe they are tremendous value, not in terms of contacts, but what is enterprise thinking like?

SPEAKER_03 25:19

Yeah, uh with Snowflake, uh the partnership has been phenomenal because the value goes both ways, right? So we've launched an app on Snowflake Marketplace, and Snowflake customers can use us, and Snowflake teams can help with distribution. Uh but the flip side is we've also invested in a product experience with Snowflake that'll make the customer's life valuable, right? So a few different examples on that. Like Snowflake knows one hop to the left and right of Snowflake. Yeah. Pantomath sees it all.

SPEAKER_01 25:52

Yeah.

SPEAKER_03 25:52

So we have gone ahead and we dump all of that in Snowflake and we call it Snowflake plus infinity lineage, which means you see the end-to-end movement of data in your entire data stack all within your Snowflake experience without ever having to log out a Snowflake. And that's valuable for customers, right? So we've done that. We've built stuff with Snowflake's DQ framework around whenever there's a data quality issue, how do you do diagnostics? How do you see that in Pantomath? Because we have observability built in. So, how do you go ahead and blend the view together to intercept all those things to then give the customer a better understanding of what went wrong and why this happened? Uh, because a data quality issue is not just someone fat-fingered a field, like we've got enough checks for that already. Uh, it's very often like something broke while data was in motion, right? So, we that's part of our core observability product, anyway. So, we integrate a Snowflake's DQ framework. Uh, so there's a lot of like product innovation that we've done together to make the customer's life easier. So, like any good partnership, right? Like we're helping with with product innovation and like and because we're live in the marketplace, we're getting help on distribution.

SPEAKER_00 27:05

Got it. And today in the data category, we are discussing it offline. What are the the things that are already done and what are the remaining opportunities for entrepreneurs?

SPEAKER_03 27:14

Uh, data is a very crowded category. I don't know if that's the category. If someone's thinking of starting a seed raising a seed round or pre-seed round to start a company, I don't know if that's the category I'd look at today, mainly because right now, over the last five, seven, eight, ten years, there's just been so much that has already happened in that area that now I think the industry will go through more of a consolidation cycle. And you're starting to see that, right? Like you look at FiveTran and DBT merger, you look at like there are these transactions starting to happen where it's like, okay, some of these, like when you go to these conferences and you see like 250 different vendors exhibiting, like, not everyone's going to make it in the long term. Uh, and there has to be some consolidation. And I'm not saying that more from a competitor standpoint, I'm saying this more from an advocate of the customer standpoint. It's just way, way, way too hard for the customer to understand the differences between all these products, what each one does, which one's better, and pick eight different products and stitch it together and try to make it work. It's just very hard for the customer. So I do think the industry will start to consolidate at some point. A customer should have three, four, five solid options in every category. A customer does not need 15 options in every category, uh, more than half of which are mediocre, right? Like that's just not healthy industry dynamics.

SPEAKER_00 28:36

Got it. And uh so you're saying that it's harder to create outcomes like Snowflake or Databricks in data.

SPEAKER_03 28:43

Oh, Databricks size outcomes in data, yeah, that's going to be pretty hard. Uh Databricks is an incredible, incredible company, right? And they've done incredibly well. And if you look at especially the growth rate in the last five, seven years, it's just been an incredible journey. So in I I would not just say in data, like building a Databricks style outcome in anything, in any category, is an incredible thing. It's an incredible feat. And yeah, it's just those those things are just never easy to begin with.

SPEAKER_00 29:09

Yeah, but but as an entrepreneur, the bar has got much higher. Like as you said in Windley's case, I believe the the outcome you would have had in mind is let's reach 100 mil ARR. Right today, uh, though reaching 100 mil ARR is more tougher, I believe. Maybe you can uh share your perspective uh on that because the categories have become uh crowded, right? Uh and there's this blurring of categories today also. But but still at 100 mil ARR, the benchmarks have become so high. Yeah.

SPEAKER_03 29:40

Because it's not just so there used to be like Battery and Neeraj had mentioned whatever the three triples, two doubles kind of a thing, uh, triple, triple, double, double, double, or whatever that what whatever the uh norm used to be. Uh their playbooks out of the window, right? Like right now with these AI companies, as I said, like you see companies in like six months, one year, two years getting to 100 million ARR. Uh well, is that true ARR? I don't know, but the point is they're getting to those numbers. Uh so it's going from a little bit, I mean we see N startups as never a normal distribution, but theoretically speaking, just for illustrating the point, the point is there used to be companies that kind of would fit the mold uh that are roughly doubling, tripling every year. Uh now it's kind of becoming more bimodal in terms of like the haves and have nots, and that divide is uh increasing a lot more. So if a company is just doubling or tripling revenue, they're kind of not that relevant unless you have a plan to really, really expedite growth really quick.

SPEAKER_00 30:43

Or or owning a category or the narrative is like that how do you own a category?

SPEAKER_03 30:48

Yeah, and that's harder because every category is more crowded now.

SPEAKER_00 30:52

Yeah. And especially let's say if we look at data. So data within an enterprise is exploding. You don't now only have you know the data that you need to train your own LLMs on. You are creating synthetic data also, right? Uh so is it uh fair to say that a data in an enterprise has gone up 10x in the or 5x in the last few years?

SPEAKER_03 31:15

Yeah, and that's why when you look at people's bills with like Snowflake or Databricks or any of these, and all yeah, that that is like you you are seeing that, right, in terms of explosion of data. Uh not just human-created data, machine data. Like you're seeing all these adjacent categories emerge, like how do you secure AI agents in the enterprise, non-human identity, all these other different, I would say, secondary categories that are emerging as byproduct of managing the data and like and the access for the data that an agent has, right? So all these other categories are also emerging and creating and will continue to grow.

SPEAKER_00 31:48

Yeah, because let's say uh uh fail to generalize that the human identities in an enterprise might become half over short or long period of time, but the but the number of non-human identities would be 10x or in some cases 50x, 100x or something.

SPEAKER_03 32:06

Possibly, possibly uh I don't know how much of the the the the second half I feel more confident about in terms of like non-human identities will explode, that I feel confident about. The human identities like in terms of the headcount of most companies will become half that I don't know. I don't think it'll be that drastic, but that there are multiple different opinions and point of views on that topic. So uh yeah, I I don't know, I don't know the first half of the sentence, but the second half is yeah very likely to be true.

SPEAKER_00 32:36

Yeah. But let's say uh you said across both your companies to achieve a similar outcome, yeah, you you required half as the amount of team to do that.

SPEAKER_03 32:46

But then you're starting off from scratch and you're an AI native company, it's easier to set up things in a way that you don't need to bring on humans to do that. Whereas if you're a much larger company that has tens of thousands of employees, cutting down, cutting down 10-15% of your workforce, I can see that. Cutting your workforce down into half, like the promise of AI has to really come true for that, right? Because then that's that's a much, much more significant cut, that point. And then the other thing is, or we're also assuming the amount of work the company will have to do will be flat in that equation, right? So to achieve the same amount of work, you need half the people. But what if the amount of work goes up? And we've seen this like in terms of multiple different tech evolutions. Actually, if you look at staffing and recruiting, right? In the late 90s, when monster.com and job boards came about, everyone assumed recruiters will grow go away. Like, why do you need staffing firms and recruiters? And most of these job boards made money from staffing firms and recruiters. And then in the second evolution of this, in the mid to late 2000s, when LinkedIn came about, the answer was you're never going to need recruiters. This time staffing firms are gone for sure. Right. And we are connected to each other, everyone knows who's good and who's not. You can see the past history, you can see the background, who's done what. It's very transparent because it's on social media. So, why do you need recruiters? I can just search like product manager with this experience, and boom, it should show me the answer. It's like, where does LinkedIn make most of his money from? Recruiters and staffing companies. So they make money from people they were trying to disrupt, right? And similar examples can be given from like Zillow and real estate agents or from brokerage and trading platforms. Like, so if you look at brokerage trading platforms and uh wealth managers, right? Your financial advisors. Just because, like, if you think of 25 years ago, you would call your financial advisor and a broker to execute a transaction, buy X units of this stock, sell Y units of that other stock, right? Today, when you sit with your financial advisor, all the work is about retirement planning and goals, not execute A, B, C, or XYZ transactions, buy these many units or sell these many units. That's not the main conversation.

unknown 35:06

Right.

SPEAKER_03 35:06

And 25 years ago, that was the conversation you had. And as a result, now that you're discussing more strategic goals, the amount of money people have put in 401ks, the amount of money that's in the equities market, all of that has gone up exponentially. Right. So similarly, when you think about enterprises and humans, assuming that, oh, companies will just replace this work with AI agents, not move to a more strategic tier of work and just get rid of half the humans. I don't know if that's true because generally then you just end up moving work up the value chain and the humans get repurposed to do the more value-added task and not the tactical execution of the transaction task, right? So that's where I think just the nature of the work will continue to evolve up the intellectual spectrum.

SPEAKER_00 35:54

So what you are pointing us towards, like Jemon's paradox, if work becomes cheap, then more work stay done.

SPEAKER_02 35:59

Exactly. Exactly, yeah.

SPEAKER_00 36:01

Yeah. And uh you're not only uh providing data operations center for data at rest, you're also providing for that a data in motion.

SPEAKER_03 36:10

That's right.

SPEAKER_00 36:10

How do you do that?

SPEAKER_03 36:11

Uh, and that's actually one of the core pieces of technology we've written, right? Where we install an agent or a VM on the client's infra behind their firewall. And what we do is we scan through and understand all the movement of data, we do all the metadata mapping, we put in the knowledge graph, we show it to the client end-to-end. And because we see everything and we show everything to the customer, uh, we then go ahead and understand all the movement patterns of that data. So there are machine learning models that run behind the scenes that understand all the movement of data that, oh, uh this table has 50,000 rows every day, today it has 5,000, there's an issue, or this job takes one hour to run every day, today's taking two hours because of that extra hour, it's going to break these eight other things downstream, right? So we understand these things, sometimes even before they happen. And because we understand that, we can then go ahead and do things about it. We don't need to wait for everything to break or come to a standstill. Uh, because data doesn't break or go bad while sitting at rest, it breaks in motion, right? And that's why we go ahead and we've built that. That's the core fundamental piece of technology we've built, which is uh understanding all the movement of data enterprise-wide while keeping the data secure behind the customer's firewall.

SPEAKER_00 37:28

And also um the read and write permissions that the agents are getting, they are getting more and more every day, right? So the number of data that agents are writing.

SPEAKER_03 37:37

Yeah, that is different. Customers have they are all like customers are all over the spectrum on that one. Uh some say that humans screw up more things, so as long as they can put guardrails and set a certain set of rules engines of tasks to do and task not to do, that the agent is allowed to do versus not allowed to do. They're like agents obey better than humans, kind of a thing, right? So, versus there's a contradictory point of view where it's like, nope, I'm gonna give you read-only least privilege access, no write access, human in the loop, human has to review everything. And so customers are on different ends of the spectrum on this one. Uh, and that's okay because like even when the cloud wave happened, right? There were some customers that were all in on the cloud, and some were like, nope, we're gonna run our own data centers, or we'll and there were late adopters to the cloud, and that's okay. And that's always gonna happen with any transformational technology. But today, I would say it's more ambiguous and more of a mixed bag, and that that'll play out over the next couple of years, where I think customers will have a better framework to manage these permissions and will be more comfortable giving AI agents more permissions to do. Uh, so today, when we say we'll resolve issues, today we do it human in the loop, where the AI agent gives the resolution plan, it tells you what to do, but the human has to click approve and actually give permission to execute the task. At some point, I bet you 12 months, 18 months, two years from now, the customer will come and say, Shashank, this is very dumb. I'm unhappy. Why? Why? Because you've already done the whole resolution plan. Why do I have to come in and manually hit approve? Just do it for me. Like, don't make me do the extra work. So, what today is a guardrail? Tomorrow will feel like a bottleneck and inefficiency saying, why do I have to wake up at 2 a.m. when there's an outage to hit that approve button, just go ahead and resolve it. You've done it 10 times before, just do it the 11 times, it's okay, I'm okay with it. Right? But then that customer's mindset changes, but we'll have to wait till we get there because today, proactively, if I say we're going to autonomously resolve it, the customer just isn't comfortable enough yet.

SPEAKER_00 39:48

Yeah, I believe the world is changing so fast right now. Yeah. Did in your journey did you see the world change so fast?

SPEAKER_03 39:55

Uh last two years have been absolutely crazy. I just have not. Because the other thing is, change, the world is changing fast has been a statement said from my parents' generation onwards, right? And that has been true for everything, be it the internet cycle, be it the mobile wave and social and local, and be it cloud, be it that that's been true for everything, and with AI it is. But a lot of counterintuitive, contradictory things are happening. Like the traditional wisdom here was software companies are meant to be software companies and don't worry about services, right? So once a software company starts sailing, it's like, oh, go stand up with Accenture and Deloitte and KPMG or practice so that they can do the implementation work. We don't want to be in the services business, we'll be in the product and software business, high gross margin business, services is low gross margin, right? So that was traditional prevailing wisdom until look at what we've seen in the last one week anthropic and open AI, and everyone wants to get into the services business. And it's just areas that you didn't think they'd get into.

SPEAKER_01 41:00

Yeah.

SPEAKER_03 41:00

Uh so a lot of the traditional norms are being rechallenged, and that's why the change feels even uh faster because yes, it is changing faster, but also it's challenging traditional wisdom and traditional norms in ways that we had just not thought they would be challenged.

SPEAKER_00 41:18

What are the other things that you are seeing?

SPEAKER_03 41:20

The pace of change is so fast, uh pace of change for white-collar work. Like you would assume. So every time we speak automation, at least for the last 25 years, it's been true where we think of it as blue-collar workers, mundane tasks, and those are the ones to automate. Right now, automation is moving faster for white-collar work and office work and knowledge work than it is for blue-collar work in terms of robotics. I understand physical AI is coming and robotics is a real thing and it's coming this time. But if you see just the pace of change, like this is what I'm saying, everything that is intuitive, where, oh, the mundane task that a lower wage worker does will is the first one to get automated, and these high value tasks will take longer. It's like being completely the opposite. So all sorts of conventional wisdom right now is just being challenged, and that's what that's what makes this time way more exciting.

SPEAKER_00 42:11

How do you cope up with so much unknown as an entrepreneur?

SPEAKER_03 42:15

Um it just comes down to entrepreneur personalities, right? Like generally, the people who love dealing with ambiguity naturally get attracted more towards entrepreneurship. Whereas people who like certainty or they like the steady paycheck and they like the big company job are not the ones that generally get attracted to entrepreneurship. So it's just the selection bias that comes into like people who decide to be entrepreneurs who enjoy this. Uh so that's that's why I'm also doing my second startup, right? Like because like you enjoy this, like you you you don't want to be sitting on the sidelines. That was my main thing. I didn't want to be sitting on the sidelines while the AI wave happened, right? You see this wave, you jump in because it's like it's it's a time to figure out while where the whole world is changing. It's like, okay, where is your place in this new world? And that's the fun, exciting part of this.

SPEAKER_00 43:15

Yeah, because when you would have joined Sierra as a full-time investor, you wouldn't have imagined like within 12 months.

SPEAKER_02 43:21

And that's exactly right.

SPEAKER_00 43:22

So the pace of change of the world couldn't hold you back.

SPEAKER_02 43:28

Yeah, exactly. Exactly.

SPEAKER_00 43:31

And let's just go back to your first journey, you know, before diving deep into uh, you know, uh the data uh and pantom math. Uh what made you start Wendley?

SPEAKER_03 43:43

So prior to Wendley, I had done this thing called entrepreneurship instead of entrepreneurship. Right, which is how do we set up a startup within a big company? And at Citibank it was a digital payments team because digital payments was relatively New and digital transformation was new, and mobile had just happened and all of that stuff. And in Kroger, it was starting the e-commerce business within Kroger, which is similar to what Walmart has done here with Walmart Labs in the Bay Area. And both of those were like relatively successful endeavors. But entrepreneurship was one of those things where I was like, listen, once you have a big company backing and a whole platform and there's already distribution in place, like all you're trying to do is cut through political bureaucracy to do what you need to do to accomplish, right? Whereas if you're doing a startup, the number of variables goes up pretty drastically. So it was more a personal challenge to myself saying, okay, can I deal with the extra variables? So that's where I wanted to try doing a startup. And the whole goal was also the personal life stage I was in, where it was like, okay, I'm still relatively young. I'll try it. If it works out, great. If it doesn't, then it's not going to be as bad, and I can always go back and do something different. Uh, so a lot of the motivation to do a startup was more personal rather than professional accomplishment driven. Uh so that's that's why I started Wendley uh as an experiment to see it's like, okay, can this work? And then we also luckily stumbled upon a very good category uh that had not been extremely innovative for over, I would say, a decade, decade and a half. Uh so that's why things worked really well at Wendley because it was a lot of good things that happened and just triangulated and came together at the same time. Uh so yeah, it was, I would say that journey was very personal motivation driven rather than anything professional.

SPEAKER_00 45:51

Can you share the Wendley journey in detail?

SPEAKER_03 45:54

Yeah, we started the company almost mid-year 2017, we launched the product early 2018, uh 2019 in January or February, Battery came in that uh series A round. In October that year, we signed a term sheet with Insight for a Series B round because between those six months we had grown quite a bit. Uh so they pre-C Insight preempted us. Uh 2020 COVID happened, so we had a really bad quarter in between, but then things again went back on track once once people understood COVID is here to stay and it's not just a two-week thing where you just put the whole world on hold. And 2021, we got acquired by workday.

SPEAKER_00 46:41

And how did the workday acquisition happen?

SPEAKER_03 46:43

Like uh Workday already was looking at this category, and Workday Ventures had invested in one of our competitors in this category as well. So they were very aware of the category. In all honesty, it was very like, yes, Workday drove the acquisition, but it was very customer driven in the sense that we fell into a wave where customers were going through a legacy application modernization exercise. Right. If you think about what was happening in the enterprise in that timeframe, uh Workday was replacing PeopleSoft and Koopa was going after Ariba and ServiceNow is replacing Remedy and like this whole next gen come replace the legacy predecessor. 76% of our revenue came from replacing the other older VMS. Right? So we fell very squarely into that category. So a lot of it is right place, right time. Uh so that's where like most of our customers would modernize their HCM with workday and then come to modernize their VMS with Vendly. And you could see a bunch of your workforce in workday, and then Vendly had was a workday partner, so we built that connective, and now you can see your whole workforce, employees and contractors within workday. So it was really, really valuable for the customer. So the customer feedback was resoundingly positive, saying, yes, this is like peanut, butter, and jelly, they belong together. So that's where the workday acquisition happened. And kudos to Anil and the whole team there, and you know, they're very in touch with the customer sentiment as well. Uh, if you've met Anil and know him, like he's just a very sharp product mind uh in terms of knowing product strategy, where to go, where to take the product, and it just made a lot of sense. So uh overall, I think it was it worked out really well for everyone involved, uh, including the customers, including Workday, including us, yeah.

SPEAKER_00 48:35

And at Wendley, you had mapped the entire contractor onboarding to contractor exit process.

SPEAKER_03 48:41

Yeah. Uh and that's where we were partners. So Okta was an investor in Wenley, ServiceNow was an investor in Wendley, Salesforce was an investor in Wendley, and Salesforce the tie was because they had launched a platform called Work.com during COVID. Uh so yeah, I mean, a lot of those partnerships helped too because with Okta we built a proprietary integration that helped with like you know the SSO and onboarding and all of that checklist. Yeah.

SPEAKER_00 49:05

So yeah, it is said that building a startup in SF versus anywhere else in the US is very different. But it seems like at Wendley you you are having a great place in terms of customers, investors, right? So can you share your experience building in two different locations?

SPEAKER_03 49:25

Yeah, uh that pendulum swings depending on timing too. So during the Wendy time, I never felt compelled that I need to move to the Bay Area to build this. Wendley could have been built, like Wendy was built in Ohio, like in Cincinnati, the whole journey, start to end. Uh, and that worked really well because at that time there was not information asymmetry in terms of tools and usage.

SPEAKER_01 49:50

Yeah.

SPEAKER_03 49:51

Right? Wendley also happened when I call it a little bit of a the dull wave of technology time. So 2017 to 2021, that journey, the cloud had already been there for almost 10 years. AI hadn't happened yet, right? So a lot of the tech was known and it was an apps layer company, it was not an infra-layer company. So a company like Wendley, built in Ohio, perfect, made perfect, perfect sense. I never felt compelled that I need to go hire a Bay Area team. A company like Pantomath, infra company playing in the AI wave, has to be built in the Bay Area. Like, yes, we have a team in Cincinnati still, but the point is it's just a completely different ball game and a completely different time given how quick AI is changing. And there are there are people that are developers, there are people that are AI native developers, and they're not one and the same. Right? There is a transition happening, but they're still not one and the same. So it's just a different mindset that we are seeing right now. And here in the Bay Area, it's very it's a very exciting time to be here and build here.

SPEAKER_00 50:56

And today, how many Fortune 500 logos do you have?

SPEAKER_03 50:59

Uh that's a good question. I don't think I know the exact count. Five, seven? Okay. Somewhere in that range.

SPEAKER_00 51:07

For for an early stage company, it's still two and a half years.

SPEAKER_03 51:10

Yeah. Uh it's quite commendable. Because we we solve the enterprise problem, right? It's meant for it's made for big enterprise.

SPEAKER_00 51:18

What what is uh your learnings from now you have been selling to enterprise for almost like a decade as an entrepreneur. Your learnings on selling to enterprise, like how do you discover problems? How do you because it's it's very hard to get to the right POC, build champions, build a series of champions to win an enterprise deal.

SPEAKER_03 51:39

Yeah, that is true, but a lot of it also comes down to how real is the pain point. Uh because if you catch a real, real pain point, those things tend to start getting simpler. Of course, there's always human dynamics or political dynamics or financial and budget dynamics in the enterprise, which are constants. Uh, but the big variability of companies and categories that take off or categories in which companies hit significant revenue momentum versus categories in which they don't, a lot of it comes down to how big is the enterprise problem or how relevant from a timing standpoint it is, right? So the timeliness of this really, really matters. And that's true for Pantomath. That was true for Wendley. If I had started Wendy five years sooner, we would it wouldn't work out. If I started Wendley five years later in the AI wave, it wouldn't work out, right? The timing, which is where people talk call call it luck or destiny, because that's not something you market dynamics are not something that you influence with hard work. Uh the timing was right. Same thing for Pantomath right now, right? We're building, and we believe like AI agents and giving them rich data to feed off of that's high quality, is going to be is starting to be more of a mainstream problem, but will be a mainstream problem two years from now, and you're building towards that time frame. So you have a bunch of early adopters, it's a as I said earlier, it's a bold conversation of how do we get AI right? Uh so the timing of these things really, really matter, uh, more so than the standard enterprise sales cycle.

SPEAKER_00 53:12

Got it. So in your case, in both the both the journeys, you have been able to to figure out timing right. How did you do that?

SPEAKER_03 53:22

Uh no, Pandam is still early in the journey, right? So I don't know. Like all these things you can only look back in hindsight and then reflect on them while you're living through it. Like every day, building Wendley was like, I don't know if you're doing the right thing, I don't know if it's going to work out. How's this? And you feel high and you feel low and you feel all the emotions in between. And uh so it when you're doing it, it never feels like right because you you focus more on not things that are working well because you barely think about those, you focus on all the things that are not working well, and that's all you do your entire day. Yeah, that's right. So you spend your entire day in that. Whereas uh, and so the only time you really sit back and reflect is after the journey is done, which is like, oh yeah, we actually you know got these three things right, and I wish we did these three things better, and I wish so. All of that is in hindsight and when you reflect.

SPEAKER_00 54:14

Yeah. So I I would say uh Wendley in the HR category would have been one of the largest exits at that time. We sold it for 500 million plus, uh, right. Uh uh, you got uh tier one investors during that journey. Uh uh if you can share this with a lot of entrepreneurs, we get 5 million views a month on the podcast. Like 80% are from India, but 20% are from California. So, what was your uh you know, strengths that you raised in your first journey starting in Ohio from tier one investors?

SPEAKER_03 54:49

Um so it's very kind of you to say, but there have been way bigger exits in the HR text, so I don't consider Wendley as one of the biggest. Uh but coming back to more of the sentiment of your question, uh I don't know. Uh I don't know how to answer that. Because I feel for Wendley, what really, really worked out very well for me was naivety and optimism. I just didn't know any better, right? And every time you're a first-time founder, there is a certain level of optimism, there's brashness, there's borderline arrogance, there's borderline uh, you know, you want to push really, really hard and go really, really fast. Uh so yeah, it's it's that that kept us going. And as I said, like in that wave, we were starting to see like Chicago's startup ecosystem was doing really well. Atlanta had started to thrive, uh, LA, we were starting to see good activity. So today in the AI wave, I feel it area is the epicenter, but I feel like when we did Wendley, like you were starting to see all over like the rise of the rest of America and the startup ecosystems there were starting to happen. So overall, like listen, investors and capital follows where entrepreneurs or companies are doing well. So if you build a meaningful company and you're doing well, you can build that anywhere in the world. And as long as you're building it well, like investors will follow. So I don't people people you don't need to pick a location based on thinking which location attracts tier one capital or not, because there are pros and cons, right? Like in the Bay Area, like, yes, there's plenty and plenty of good investors that invest, but there's plenty and plenty of great companies too, right? Whereas in the Midwest, it'll be a smaller pool of startups, but at the same time, it's not the area that's where investors are zooming in and focusing on either. So it it varies, and these things again, as I said, like it's not it's not a decision criteria that you think about like when you start, it just you pick it up and you learn and go with the flow.

SPEAKER_00 56:59

But but as an entrepreneur, you know, as you said, right, when you're living day in, day out, you never know whether you are sitting in the right category or or not, because investors also look at which which are the markets, yeah. Uh is it a market that can create multiple $10 billion companies? Because that's the outcome that at least series A and Series B investors want to. So, how do you figure out that?

SPEAKER_03 57:23

So I I honestly feel like I didn't pick the problems, the problems picked me. Like Pantomath so much just happened to be in data operations. So it's not like I was out looking and analyzing and studying categories or building a category-based thesis, how VCs do it. It just didn't happen that way. Uh, same thing in terms of VMS, like my co-founder then Orion, like he was already living in these systems, he knew these systems well. Uh, so he already was very keen on that category. Starting in that category. Exactly. So that's my point, is like I didn't pick the problems, the problems picked me. So that just as happenstance ended up happening.

SPEAKER_00 57:58

Got it. That's also being around the right people.

SPEAKER_03 58:02

Yeah, that matters a lot, actually. That is a very, very big factor.

SPEAKER_00 58:06

So I think Bay Area influences that if you can just surround yourself with I think the right set of people.

SPEAKER_03 58:13

Yeah, I agree. And that's why like Wendley, I I feel that company was the right one to be built in Ohio and not in the Bay Area. Uh, because I feel I was isolated and in a little bit of my own shell there, uh, which was good because I was heads down and focused. Uh, but the other thing is also your earlier question that you had asked, which is how is it different this time? One of the biggest differences, right, is when I built my first company, our all I knew was if I'm heads down, focused, executing, things will go well. And pretty much that's how it played out. You had to be heads down, you had to be focused, you had to be executing. Uh now, in the AI wave, I feel if you're heads down and you're focused, you don't know what's going to come hit you from the left or right. You are one anthropic press release away from not having a business ahead of you.

SPEAKER_00 59:10

Can you share an example?

SPEAKER_03 59:12

Look at what happened to cybersecurity in the last few weeks, right? And anthropic. And then so my point being, like now, yes, you have to execute. There is no two ways about that, right? There's no business without execution. At the same time, you have to understand all the different moving pieces in the industry that we've discussed over the last half an hour, 45 minutes, like how quickly things are changing. You have to have to have to stay abreast and afloat with all those changes to then think like, what does each of these things mean with your business? Today you're selling a product and the customer's buying it. Tomorrow you walk in and sell, try to sell the product, and the customer's like, oh yeah, the whatever, name your LLM, Gemini guys, GCP guys, anthropic guys, open AI guys, they were here and they said this is on their roadmap and they're doing it, so I'm just gonna wait for them.

SPEAKER_00 59:57

Yeah, like like what you shared in terms of how easy it became to send sell Wendley within work day. You just became a line item. That's right. That's right. So as an entrepreneur, you don't have to become a line item on somebody's else.

SPEAKER_03 1:00:09

Exactly.

SPEAKER_02 1:00:10

Purchase order.

SPEAKER_03 1:00:11

Exactly. So that's the thing, is like right now things are just moving so quick, you you never know who owns what category and who's playing in what. So basically the traditional like swim lanes have all been abandoned, and everyone is just all over the place, and you never know who's going to enter in your category.

SPEAKER_00 1:00:28

So we were discussing offline about you know, as a second time founder, what's just easier versus still remains hard.

SPEAKER_03 1:00:38

So I'll answer that question in two different buckets, right? So let's say I done my first company in like 2006 to 2010, 12 time frame, and my second one was 2014 to 2018 or 2020, right? Like in in similar-ish timeframes, 80-90% of learnings, or pretty actually close to 100% of learnings, would would would reapply, right? Because if you look at the move from on-prem to cloud, that was a rinse and repeat playbook. The only thing that changed was the cost structure and how product releases are done on multi-tenant cloud architecture. Right? The entire go-to-market game and all of that stuff, pretty much like sales, quota carrying, field sales, all of those things were pretty consistent. So, as a repeat founder, in most normal times, 90% of stuff, how you build the product and how you sell the product are identical. However, and now is the second bucket, which I said I'll answer it in two buckets. However, right now, I think just this like two, three, four-year window is just a drastically different time in the game because how you build the product is changing drastically, and how you sell the product is also changing drastically. Right? So, how you build the product using AI tooling, how many developers you need, how product management should be done, how your release notes are written, uh product manuals, updates, all of that stuff, all of that you can use AI for. So how you build the product has changed completely. How you sell the product, like look at these companies we spoke about getting to 100 million ARR. It's no longer just numbers, how many qualified leads, how many sales reps do you have, what is the quota per rep? Like, yes, you know all of those things, so it should be easier. But then the question is, just because you know it that way, is that the right way to do it? Right? So that becomes a very big question. So overall, like I would say everyone who has learned everything over the last 20 years, now during this AI phase, has to like unlearn and relearn. Uh, and that's why when I said AI native, like we need more AI native people overall, not just at Pantomat. Yes, we need them at Pantomat, but overall, because a lot of these things are getting unlearned and relearned in terms of because the traditional model of like enterprise reps doesn't get you to 100 million ARR in two years. You have to do something drastically different for that, right? In fact, a lot of these hot AI startups aren't doing the traditional model, be it forward deploy engineers, be it selling services contracts, be it outcome-based contracts, be it pricing based on usage, be it all of these things are just completely drastically different. So, like that's where like there are certain things that are the same, like running operations at a company, financials, uh fundraising, your cap table, like all of those things you know. So, yes, there's so instead of like 80% of the playbook being replicable, now in the AI world, only 20% of the playbook is replicable. So I don't know if being a repeat founder has that much of an advantage in this narrow time frame where it's AI. Once things settle down, then it might come back and you might see a higher percentage. But because the two most important things, how you build the product and how you sell the product, have just changed drastically.

SPEAKER_00 1:04:02

But but two things remain easier uh as compared to a first-time founder, which is first is building the team because you know all the top performers in your past team would want to join you again. Yeah, I assume that. And second is fundraising.

SPEAKER_03 1:04:18

Fundraising is definitely easier, no two ways about that. Uh, the top performers joining now, of course, if you're doing a company in the same category, then yes, you would want that. But if you're playing a whole different game, then you need the right horse for the right track. So that may or may not be completely identical, right? Because this is also where repeat founders, and I'm speaking on behalf of myself and other friends too, that are in the same board. Uh the key is not to have familiarity bias. Just because I like someone, I have a personal relationship with them, I get along well with them, I've been on a journey with them, does not make them the right person for this journey, too. Right? So, my head of engineering, instead of going to my previous company and hiring, which I absolutely love them, so it's not a thing on them, I hired someone from Databricks and GCP. Yeah. Because they understand data. I'm building in the data category, right? They have a network of people that have worked at these companies and understand data. So it's just a completely different ball game, different geography, different category, different layer of the stack, like apps versus infra. I just need a different person. So the trap that repeat founders can always fall in is like, oh, we're getting the group back together because now it's going to be fun this time again. It's like, yeah, you're not doing this to just hang out with the group, right? Like, that's not the goal. Like, who is the right person for the set of problems that you're trying to solve for? So that's why it's it's it's different and it's not that easy.

SPEAKER_00 1:05:50

And you mentioned that the enterprise sales have also completely changed. Can you throw some light on it? How are the sales done right now? And how are the companies that you know? Are growing the ARR because it's not the usual contracts.

SPEAKER_03 1:06:04

Yeah. So a few things. One is and especially in data, it's true where most companies want to do a POC. Right. Uh in your traditional enterprise sales, like take workday or SAP or Oracle. They don't do POCs. Right? That's the first step of the process. Second step of the process is when you start thinking about uh infra companies, you're selling more to a technical audience than to a stakeholder. Koopa sells to a chief product officer, workday sells to a CHRO, right? Salesforce sells to a CRO, right? So each of those, like it's a very business persona or a business leader persona that you're selling to top-down. Here, you're selling to a technical persona bottom-up, completely different model. Then if you start thinking about pricing, a lot of companies are moving towards usage-based pricing. And they're getting rid of the per seat or per license model. For Pantomat 2, like we have a connector-based pricing model, we're not tethered to any like seat count, whether two people use it or 200 people use it, it's still the same price. So that's change, uh which is good and bad, right? Like we hear it both sides from the customer. The customer says, I used to like the SaaS pricing model because when I'm building my budget, I know exactly how many dollars to bake in for the contract for next year. And in data, that's very, very hard to do. Your Snowflake bill, your Databricks bill, like your pre-buying credit, you don't know how much you're going to burn. Like you bought 10 million of credit and you use six, or did you buy six million of credits and use 10? Right? Like all of that usage-based pricing is changing, including your bill for your LLMs. So when we look at our internally, we use Gemini and Anthropic and Cloud Code. We use OpenAI for a few things, but like a lot of the cloud code stuff, when I look at usage numbers and pricing trends, uh the bill is it varies and it's going up drastically. So how much do you budget? How much do you factor in? Like at R scale, it's okay, but for if you're a large enterprise, like 50,000 employees, like that's a massive amount of variability you're signing up for.

SPEAKER_00 1:08:33

So you mentioned also that you are selling on a problem that can happen at scale tomorrow within enterprises. But today, to show the ROI, what dollar ROI or what uh cost ROI are you able to do.

SPEAKER_03 1:08:46

We make your services team more efficient, right? The amount of time it takes to detect an issue and resolve an issue. Today you have a certain amount of headcount tied to supporting your data platforms, right? Whether it's eight people or eighty people that are in your support org, it depends, it varies by company. But the point is mean time to acknowledgement, mean time to root cause analysis, mean time to resolution. Those are three metrics, and there's tangible man hours assigned or associated with each issue and how many man hours were spent trying to resolve the issue. We really move the needle on that, right? So if it takes you six hours to figure out what happened and we turn that down to 60 seconds, that's that's meaningful, right? Six hours at even an offshore rate of $30, $40, $50 an hour, right? Or if it's in the US, like developers aren't cheap, like $80, $100, $120 an hour, uh and six hours of savings right there, times X amount of tickets per week times Y amount of tickets in the year, like all of that stuff adds up and the software kind of pays for itself then. And that that's excluding any like business impact in terms of like, oh, you use this bad Tableau dashboard to make a bad decision. If you're an oil and gas company and you drill for the wrong locations because a tableau dashboard didn't update, that's like millions of dollars of fines. If you're a payments company and you'll you report wrong numbers, like there's millions of dollars of fines and liabilities that get accrued. And I'm not factoring that stuff in, right? Like I'm factoring like hard dollars ROI in terms of how quickly can you solve a support issue?

SPEAKER_00 1:10:36

That's quite powerful. Yeah, and uh across both these companies, you know, uh if you have to, you know, I know it will be hard to recall, but what's been the best piece of advice or advice that you have received that change the trajectories of these companies?

SPEAKER_03 1:10:53

Uh overall, in terms of advice, it's it's less to do with company. Like if you think of the best mentorship or board member relationships I've had, yeah. So one of my board members in at my last company was Steve Singh, the founder of Concurr. And just the human being that he is, the way you care about people, the way you think about talent management and retention, the way you think about how people should do well and thrive in the enterprise. Like if you look at his legacy, and like I consider him a father figure in my life, and I I I love him deeply and dearly from the bottom of my heart, and I will be forever grateful to him. Like a lot of the advice and feedback is not about a SaaS metric or a go-to-market playbook or something along those lines. It's more around the framework of thinking, around how, as a human, you go approach the problem of company building and product building and solving and how do you want to treat people and customers along the way. That's the part like that resonated with me the most. And those are the things I take advice on and I learn and I seek to learn more of uh along those angles. And that's why actually he's an angel investor in Pantomat, too, for that reason, right? So anything I ever do in life, like I'd always want him around the table. Uh, so those are the kind of things I've have that have resonated and stayed with me along the way.

SPEAKER_00 1:12:18

So, what are the some of the principles or frameworks that you built along the way?

SPEAKER_03 1:12:23

Uh, I don't have like a framework, but in terms of like principles, right? Like a lot of it comes down to how well have people done, how much of their lives changed as a byproduct of me starting a company or raising money or distributing a certain amount of equity. Like at Wendley, I think I think the first 17, 18, 19 people that joined us all became like multimillionaires when the exit happened. Uh, and the conversations then you have with people, right? It's like a mortgage for a house that's paid for, a kids' college thing that's paid for, money set aside for retirement, financial freedom, writing a check for charity that they can start doing, like just the conversations afterwards had become so like emotionally entrenched and meaningful. Uh it's it just is very heart-wrenching and heartwarming uh in terms of the impact that you feel on people's lives, right? Uh Steve once said this to me. He's like, listen, Shashank, the the difference between having I don't talk about exact numbers you he used, but let's say I'll I'll just use hypothetical numbers, but it was something along these lines. The difference between having 100 versus 150 million is virtually zero. The difference between having zero million and five million for a software developer that joins you in your journey is life-changing for them, right? So it's like how many people can have, and one of the things he measured was during his Conquer journeys, how many people that became millionaires along that journey with him, right? Uh, that those kind of metrics, when you start measuring your life in, those things become very, very meaningful uh around the impact that you have on other people's lives because you were bold enough and courageous enough to go start a company and build a company along the way.

SPEAKER_00 1:14:14

I want to touch upon this because many founders today are going through their MA's journeys and MAs are happening faster. We discussed that. Today, uh $0 to $200 million MA happens in the period of 30 to 45 days.

SPEAKER_03 1:14:32

Yeah, that's what I'm saying. I I don't know how sustainable this is, what's going on right now, but yes, it is. I've I've seen it more even as a VC, even as a fellow founder, I have friends whose companies are getting acquired, uh, or even, yeah.

SPEAKER_00 1:14:46

What's what's your advice to founders on MA? The journey that you had been through. I believe during the process it might have been a little stressful for you. How did you cope up with it? How do you ask for the right price?

SPEAKER_03 1:14:58

Uh I don't know if I'm one to give a lot of advice there because that is so, so, so deeply personal.

SPEAKER_01 1:15:05

Yeah.

SPEAKER_03 1:15:06

Right? In terms of founder level ambition, founder level motivation, what's going on in your family life, like are you what mental state of mind you are in? Uh generally, like whenever I get a phone call for advice, the first question I ask is not about the deal, the terms, the money, how much will you make? Actually, I don't we get to that later in the conversation, but that's not where I start off. The first line of questioning that I start off with is tell me more about you personally, right? What personal headspace are you in? Are you burnt out by this journey? Are you excited about what you can build ahead? Are you excited about what you can build ahead independently if you don't sell? Are you more excited about what you can build once you get acquired within the company because you get distribution, your product can become number one category king, right? That's one easy way to win against competition. Tell me about what's going on in your personal life, right? If you're going through a divorce and having mental health issues, versus if your spouse is like very, very supportive at that phase and like you're ready to build for the next five, seven years. Because listen, if you turn down a lucrative offer and then later on things don't work out, you're gonna have to live with that for the rest of your life, right? Whereas if you sell too quick and then the market changes, you're gonna have to live with that too for the rest of your life. It's like shit, I could have been the category king. Uh and a great example is uh Jyoti Punsal, right? Like if you see App Dynamics, App Dynamics was the flagship product in the category. And it sold to Cisco for what 3.7 billion. Datadog was a much, much smaller company when App Dynamics was out there killing it. And Datadog today has what a 50, 60, 70 billion market cap. I don't even know, I haven't tracked it. There could have been App Dynamics. So $3.7 billion exit, fantastic by every measure.

SPEAKER_04 1:16:59

Yeah.

SPEAKER_03 1:17:00

But if you talk to him, does he regret selling? Is he happy about selling? It's so convoluted, it's so personal, there's so many mixed emotions that you feel as a founder that you just don't know. So that's why I start off with that first line of questioning is which decision will you regret more versus less? Right? And that comes down to personally like, do you really have it in you to go at this? For the because you have to assume if you don't take this offer, don't assume the next offer is six months out.

SPEAKER_01 1:17:28

Yeah.

SPEAKER_03 1:17:29

Assume for the next five years, seven years, you're basically signing up for the next five, seven years of this journey, saying, if I don't sell right now, assuming it's a very good and lucrative offer, then and if you're not taking it, then you're committing yourself for the next five to seven years, because you may not get a next offer six months out.

SPEAKER_00 1:17:47

That's a great framework to think from for founders.

SPEAKER_03 1:17:51

Thank you. Yeah. So a lot of it is personally driven. Like in fact, and you asked me like, why did I start Wendy and all of this, right? Like consistently, or even like advice I've been giving. More of most of my decisions come at it from a personal lifestyle standpoint rather than a professional life standpoint. My goals are never like, oh, I want to be, I want to make X amount of money, I want to be the CEO of this company, or I want to like having a CEO title is good, or like none of that stuff factors in in my decisions or like a personal career path objective, none of that factors in. A lot of it comes from the personal side of things, saying, personally, given the circumstances, what's right for my family and me, given this stage and this time in life, and a lot of my decisions are driven based on that.

SPEAKER_00 1:18:37

Which makes sense because if if you're personally aligned, a lot of professional part will take care of itself.

SPEAKER_03 1:18:44

Yeah, and you do right things for the right reasons, and the rest of it just takes care of itself.

SPEAKER_00 1:18:48

What has been your strength as a founder during both the journeys?

SPEAKER_03 1:18:53

Uh I don't know. Uh generally, I like people. I genuinely like people. So if I was not a founder and if I just had to hang out with people, I could do that all day, every day. Right? So once you like people, your employees are people, your customers are people, your investors are people. When you have to go have hang out with your employees at a company event, or you have to go recruit the next person and have a coffee meeting with them, or you have to go fundraise and have a coffee meeting with an investor, or you have to go do a sales deal and have a lunch or dinner with a customer and chat about the product. Is that burdensome? Or do you feel good about it? And if you feel good about it, the journey automatically becomes easier as compared to feeling the burden of it, right? So that's why a lot of times people feel they have to be co-founder and CEO. It's like, no, you can be CTO if you don't enjoy those things. Or you can be engineering lead, or you could be something else, depending on what the right role for you is. So don't chase the title when your personality is not suited to the title, right? So for me, it just happens to be that things that I enjoy doing are also things that my job needs me to do. So those things when they align, then that's when the journey, the journey is incredibly hard. So I wouldn't say it becomes easier, but at least I focus on the hard parts of the hard journey. It's not an additional layer of hardness that's added of me not enjoying the things I have to do.

SPEAKER_00 1:20:25

What has been, let's say, if certain, I know you shared that you don't believe in some frameworks, but what have been some of the things in your life that have helped you grow, that have helped you reach here?

SPEAKER_03 1:20:37

Uh taking risks. And more importantly, than taking risks is from a personal life standpoint, putting yourself in a position where you can take risks and you don't have to live the guilt of it. So let me give you an example. When my wife and I were both working, we used to uh as our salaries and income went up, we didn't increase the cost of living to a point where, so we were living off of one person's paycheck and the others was all savings. So essentially, when I went from being a well-compensated executive at Kroger to doing my startup, it actually didn't change our life much. We still lived in the same house, we still drove the same cars, we still ate at the same restaurant, we still, because we had never we were never living beyond our means, anyways, to begin with. So I didn't feel guilty that, oh, because I have chosen to take a pay cart and do a startup enough my personal ambition and putting my family through a ton of sacrifice, I didn't feel that because nothing changed. Because to begin with, we were never living beyond our means. So mentally, that put me at ease to go do what I needed to do. And I didn't have to cope with constant guilt. So, how you set up your personal life in a way that when the opportunity comes or when the feeling comes that I want to go take this job at the startup or do my own startup, mentally I'm just at ease, right? Because you're not constantly battling it. Like that really matters. So then to take the risk, you have to have the foundation that enables you to then take the risk. And again, I don't blame people because life can get hard and things are out of your control, but as much as you can control it, like I would recommend people like in terms of put yourself in a position where that when the opportunity comes to take a bigger risk, you are capable, you you are in a position where you can do that. You've set up the framework in such a way that you can do that.

SPEAKER_00 1:22:32

For first-time founders, uh, my experience has been those who have never sold to enterprises before, knowing what an enterprise needs. I'm talking about let's say Fortune 10,000 to simplify, uh, is is tough because uh you don't get a direct answer sometimes, and people are not usually working to solve a problem, maybe. But let's say uh individual is is working towards optimizing their work, aiming for their promotion. That's where they say, right? Uh if your tool works well, uh your champion gets promoted, and that's what you should optimize for. That's exactly right. That's exactly right. So so how did you learn to know the uh enterprise like selling is the the later part, but uh understanding the enterprise well.

SPEAKER_03 1:23:24

So for me, I actually came from enterprise, right? So I was not fresh out of college trying to do a startup, I didn't work at startups, I didn't work in Silicon Valley. So a lot of people, if you're if you start your career at a SAP Oracle, Salesforce Service Now Work Day, like you've seen this side of the equation, but you've never been a customer. For me, my career was Citibank, Kroger, Large Enterprise Fortune 500, working in that machinery to see how the machinery works. So for me, when I went to the startup side, like I already knew that side because I belonged there. And you were responsible for transformation. Exactly. So I was the buyer.

SPEAKER_01 1:24:01

Yeah.

SPEAKER_03 1:24:01

So for a lot of these products, when I'm I was the buyer, I know how to how the buyers think. Because now when I move to the other side, that's why like a lot of my sales strategies was at least for Wendley, was not very conventional. Like when we sold the company, we had like four apps, right? Because then we we we didn't focus on the the whole machinery of sales, which is may not be very buyer-centric. We were always like very buyer-centric because we understood like what what they care about, what matters to them, because we came from that side. We were the buyer.

SPEAKER_00 1:24:33

Yeah. And and when you were the buyer, what mattered to you?

SPEAKER_03 1:24:38

Uh okay, I'll give a very tangible example. Right. And anyone who's worked with me on the sales side knows this about me very clearly. Actually, I'll give you two examples. The first one is traditionally they say open a sales meeting by listening to the customer. Ask questions. So everyone does a round of intros. What are we trying to accomplish today? What are the pain points we're trying to solve? Blah, blah, blah. It's the list of questions, right? 20 minutes into the meeting, 25 minutes into the meeting, you've told me nothing about you and your product.

SPEAKER_01 1:25:12

Yeah.

SPEAKER_03 1:25:13

And you're asking me questions. And I'm talking about me as a buyer sitting in the buyer seat. You're asking me questions, and everything I tell you, you're going to rehash and pad it to me and package it back to me. I'm smart enough to see through that crap. Like, why are you here? If you're here, just tell me what your product does and let me decide is it a fit or is it not? Don't make me tell you so that you can rehash those same words back to me and wrap it around your product wrapper and then tell me the same message back. I see through that crap. Buyers see through that. Second thing. If the client's asking for a demo, give them a damn demo. Or we'll do a pre-call. Or we'll do an evaluation call. We'll ask them 10 questions. Do you have budget? Are you the stakeholder? Are you going to run an RFP? Who's the buyer? Who's going to pay for this? Who are the personas that are going to be on the demo? Send me the profile. It's like they want a demo, give them a damn demo. Right? So at Benly, I used to do this all the time. First call I get in, we do intros in the first five minutes. I was like, listen, I don't know much about you. I'm going to talk to you about product capabilities because I don't want to waste 30 minutes of your time asking you questions and then rehashing what you've told me back to you just to make you feel good. At that point, I've told you nothing about my product. It's not going to be valuable to you. For customers, that was a refreshing change. They're like, oh, thank God, it's not another salesperson that's trying to like ask me questions and rehash the same information back to me. In the first 10 minutes, I'm demoing it. Throughout the demo, it's engaged because they're like, oh yeah, this is relevant to us, in which case I spend more time. Oh, this is not relevant to us, it's not a problem, we don't care. Great. On the fly, my demo is changing. So each demo is custom. Yeah. It's conversational with the customer. They've not spent 30 minutes giving me a download context. I'm not making them do three calls before I give them a demo.

SPEAKER_01 1:27:02

Yeah.

SPEAKER_03 1:27:03

It's just run different. And customers see through that. Customers are very smart and very savvy and they appreciate that. So this is where like that conventional playbook I've not been a fan of like having six prep calls before the demo to and making them jump through hoops to see a demo and just rehashing same questions and having forcing customers to talk because they don't know what your product does, but you're asking them what their top priorities are. It's like you don't care about my priorities, you have nothing to do with that. You're here to sell me a product, so just tell me about the product you're here to sell me. Let's just keep it that simple. So I don't know, it's just very basic in that regard. Uh, I know it is counter to most traditional sales cycles and processes, but it's worked well for me, so I keep doing it that way. And every founder should be able to do a sales demo themselves. I will never back a founder who can't do a sales demo themselves. If you have to rely on your AE and SE, To do a demo, I wouldn't, as a VC, I wouldn't fund you. Like, yes, you have to have salespeople, and those roles are important. But if you if you if you're not confident enough to demo your own product, you're just not going to make it as a founder. The first few deals you have to sell yourself, anyways.

SPEAKER_00 1:28:20

And what would you advise to founders? I say founders are doing demo, and founders try to wing it that if customers are asking for integrations, when the integrations are not built, they say it'll be built or it is already built. And then they try to go back to the product teams to that.

SPEAKER_03 1:28:39

I mean that can work sometimes, some bit, but see the difference between a visionary and a con man is execution.

SPEAKER_01 1:28:46

Yeah.

SPEAKER_03 1:28:46

Right? If you commit to something and you show up and deliver it and it works really well, great. Like you'll come across as a visionary. But if it doesn't, then you're a con man, right? So you have to that's a very tricky boundary to uh tackle. Uh so yeah, you have to be very, very careful playing those games. Uh for the most part, listen, you can do that in seed stage when you don't have when you're trying to pretend as if you're bigger than you are, because otherwise you'll spook out large fortune fivers and they want to work with you. So seed stage that gets by, once your series A, B, or C, like you better have that shit figured out, right? Like there are things like today, we disclose in the beginning we had no connectors. So we would talk to the customer and be like, okay, what connectors do you need? And those are the ones we built, right? Like it was very simple as that. Uh here today we display a list of connectors and we say, This is what we have and this is what we don't have. And if there's one or two that they don't have, then we say, Okay, we'll build this for you. And by the way, it'll take three weeks to build, which by the way, your legal contract takes three weeks to sign at least at the minimum. So by the time you sign the MSA, that's agree on price, that's handshake, we'll have it ready by the time even the contract gets signed. So there's always that trade-off that you can have with the customer and have that conversation.

SPEAKER_00 1:29:56

So at Wendley, let's say when you were at seed stage and you were asked by customers or connectors, how transparently would you tell this is not built versus this is built?

SPEAKER_03 1:30:06

Pretty transparently. Like our workday connector, we needed uh so the other thing is not all connectors are integrations are equal, right? So, like we had it, but we had an SFTP integration. Now, if you need to have native workday integration with native workday APIs, when workday they click stuff and automatically those things happen in Wendley. For that, we needed workday access. So we actually told the customer we want to build it, you'll be a lighthouse customer. I won't charge you for it, but you need to go tell workday to give us access to those APIs. So once 10 customers pick workday, workday made us a partner and gave us access to those APIs. We built a phenomenal experience with Workday. The first three customers were Lighthouse customers that they needed to certify our integration for Workday to certify it and approve it. Uh once Workday approved it, like I think three months after Workday approved it, they made an offer to acquire us.

SPEAKER_00 1:30:54

Wow.

SPEAKER_03 1:30:55

So we got acquired mainly because of the connectors that we built with Workday and how good they were. Uh, so that part worked really well.

SPEAKER_00 1:31:04

Uh so yeah, I mean that yeah, the radical transparency has really worked.

SPEAKER_03 1:31:09

Yeah.

SPEAKER_00 1:31:10

Well, thank you so much, Hashant. Uh it's been an amazing conversation. Thank you being for so candid.

SPEAKER_03 1:31:16

No problem. Thanks for having me here, and I enjoyed the conversation. Good luck on your podcast.

SPEAKER_00 1:31:20

Thank you so much. It's it's been amazing discussing your life journey, your lessons, and what you discovered from this journey.

SPEAKER_04 1:31:27

Thank you.