Siddhartha Ahluwalia 1:03
Hi, this is Siddhartha Ahluwalia, your host at Neon Show and Managing Partner at Neon Fund, a firm that has invested in some of the best enterprise AI companies that are starting from India and building for the globe like Atomicwork, SpotDraft, CloudSEK and many others. Today I have with me Shashank Saxena, founder of Pantomath. Shashank, welcome on the Neon Show.

Shashank Saxena 1:26
Thanks Siddhartha. Thanks for having me. I’m excited to be here with you.

Siddhartha Ahluwalia 1:28
You co-founded Pantomath with your brother, which is very exciting, right?

Shashank Saxena 1:32
Yeah.

Siddhartha Ahluwalia 1:32
Knowing your co-founder for your entire life.

Shashank Saxena 1:34
That’s right. We’ve been roommates actually for most of our life until we got married. So yeah.

Siddhartha Ahluwalia 1:40
That’s amazing. And you both moved to US together?

Shashank Saxena 1:43
That’s right. We completed 20 years in the US in towards the end of March. So very recently.

Yeah. We both have been in the US together.

Siddhartha Ahluwalia 1:51
Would love your journey growing up in India, moving to the US, right? And then starting, you know, possibly your first company, Vndly and then Pantomath.

Shashank Saxena 2:03
Sounds good. Yeah. Maybe I’ll cover that a little bit as part of the intro.

So yeah, born and raised in India and 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 a lot of cousins around, a lot of family around and growing up in Mumbai was fun.

I still have some of the most fondest memories of my life there. And then, yeah, so then I moved to the US in 2006, right after undergrad, I had an undergrad in computer science. And then here I moved to 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 as the global recession. And of all places, actually, I started off at Citibank. So as the bailout and all of that stuff was happening, I was starting my career at Citibank.

So I got to learn a lot of interesting lessons. I’ve only had actually three main companies that I’ve worked for in my career, 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 VNDLY, which was acquired by Workday. And then I was at Workday.

So those are 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.

Siddhartha Ahluwalia 3:33
And you know, before we dive into deeply into the journey of VNDLY, would love to start how did you and your brother ended up starting a company together?

Shashank Saxena 3:41
So my previous company VNDLY had exited and sold to Workday. We were celebrating that at one of my friend’s house, he was hosting a dinner, this was our college roommate. 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 looked as if he had not slept the entire weekend because he actually hadn’t slept the entire weekend, right?

And most people who have run data operations 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 was not that easily available. So 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. 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 Pantomath got started. So I was not actively running the company until recently, until March of last year. I was always on the board and a shadow co-founder.

But now I’m full time running Pantomath.

Siddhartha Ahluwalia 5:25
So what made you join? The company started in 2022 and you joined full time in 2025. What made you take that leap?

Shashank Saxena 5:33
The opportunity is really exciting, right? Because between 2022 and 2025, 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, everyone thought, oh, we’ve seen this bubble before. It’s a temporary bubble.

It’s going to pop. Today, we are almost four years in and there is no signs of anything slowing down in time. Soon, in fact, it’s getting even more and more aggressive, the arms race for AI.

So it’s a very exciting time because for Pantomath, 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. 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 and 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. So the ability to fix it is going to be the key and most critical piece of this whole equation. So now it’s, again, the nature of the scope of the problem statement has changed.

Siddhartha Ahluwalia 7:20
And how did you move, let’s say, over a period of time from data observability to a complete data operation center?

Shashank Saxena 7:29
This is where, when we think about moving, a lot of it comes down to listening to your customers because early observability said, OK, you see issues and said, 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 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 could have not done that four years ago, right? So it was partially lack of vision, 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 provide the tooling where you can go do these things.

Siddhartha Ahluwalia 8:34
Got it. And where in the data stack do you sit? And what are the other tools that sit in the data stack usually in your customer?

Shashank Saxena 8:41
Yeah. Data stack has gotten very interesting, right? So back in the days, people just used to have like a Teradata 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 system, your source system, right?

Your Salesforce, your SAP, your Oracle, your Workday, your Coupa or Reba, depending on whatever the system or 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 Talend and Click. There’s a dozen players in that category through ingestion tooling into a Databricks lake house, into a Snowflake warehouse, through DBT labs or whatever else that they’re using for their MLOps or Dataiku, to Tableau and Power BI for visualization.

And in between, you’d have an orchestrator like either Airflow Astronomer or ControlM 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 ServiceNow 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 bring the Tableau admin saying, get on the call, this Tableau report of Power BI dashboard is not working fine. And very rarely is it a Tableau 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 who knows where the issue is, right? And that’s where we bring it all together.

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, someone catches it 9am, 10am the next morning, they have it resolved by noon or 2pm, 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 going to 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 AI agent went rogue. 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’re 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. So that’s really the core underlying hypothesis that we’re building on.

Siddhartha Ahluwalia 12:56
Cool. And what are the existing tools in the stack that you have replaced?

Shashank Saxena 13:02
We generally don’t replace. We help bring it all together. So that’s why even like, if you look at, let’s take data catalog.

We are partners with Atlin and Alation and all these companies, because we believe that it’s BYOC, bring your own data catalog. 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’ve done monitoring, we can go ahead and resolve the issue because you see it all in one place.

Siddhartha Ahluwalia 13:37
And who is the enterprise ICP that you sell to?

Shashank Saxena 13:42
Upper mid-market and then above, right? So generally, Fortune 500 or Global 5000, I would say, falls in that category. 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. 30% of our customers use Snowflake and Databricks both, right?

So there are Databricks Lakehouse and Snowflake Warehouse. So that’s the volume that we deal with.

Siddhartha Ahluwalia 14:08
Got it. And do you sell to the CIO or the chief data officer?

Shashank Saxena 14:12
Generally, it’s three different personas that are part of every bicycle. And it varies in terms of who’s the primary versus the secondary and who owns the budget center versus not. The three personas is office of the CIO, CTO, right? Because they own the platforms and supporting the platform, which is 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 CDAIO, right?

So chief data and AI officer. They are responsible for the quality of the outcomes and deliverables in the metrics, which is like, hey, my tabular report and my analytics stack, is it producing value? So that’s the second persona that’s involved in the equation almost always.

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 or CTO. But sometimes it sits outside where they’ve outsourced operations or they’ve given it to one of your offshore providers. So like 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 back. So they are generally involved in the process as well. They’re not the budget holders, but they’re the users. So that’s generally these three personas are involved in most of our decision making cycles.

Siddhartha Ahluwalia 15:35
Got it. So what I try to understand is this is a completely new category, data operations center. Did you coin the category like the Pantom as founders?

Shashank Saxena 15:46
We are the only ones we know of that are in this category, right? So data operations center, I don’t know of other players in the category, but yes, that’s how we think about it. And I don’t think that term is essentially new, right?

Because NOC and SOC have been around for so long that it was only a matter of time that someone would come up with a DOC. But the way we think about our product vision and strategy, like we think that’s pretty valuable.

Siddhartha Ahluwalia 16:11
So when you’re going to your customers, you’re setting a new category.

Shashank Saxena 16:14
Yeah.

Siddhartha Ahluwalia 16:15
That’s a pretty hard thing to do.

Shashank Saxena 16:16
That’s very hard to do. That’s very, very hard to do.

Siddhartha Ahluwalia 16:19
Because you’re asking for a budget that doesn’t exist.

Shashank Saxena 16:22
That’s right. That’s right. And we also, yes, and we also try 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 these agents that 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 yes, not only is it a category that it doesn’t exist, it also is a problem that is not mainstream today.

Siddhartha Ahluwalia 17:06
So, but you have achieved a significant scale. You have made your series B from generate that list, right? So without a category that is fully built in, how did you manage to achieve all these things, right?

Shashank Saxena 17:18
Because 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 5,000 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.

Siddhartha Ahluwalia 18:26
So right now you’re selling directly to the enterprise or through partners?

Shashank Saxena 18:29
Today, it’s all direct to enterprise. We’re just starting to go through channel now, but that’s very new for us.

Siddhartha Ahluwalia 18:36
What helped you open the first 10 enterprise doors?

Shashank Saxena 18:41
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. 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 customers.

Siddhartha Ahluwalia 19:05
And 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?

Shashank Saxena 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, the expectations have changed, right? Because during my last journey, it was always about how do you get to 100 million ARR? Is there a pathway to get there? And you’re a unicorn, right? Today with AI, we are seeing companies, 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 to 100 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 is 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 going to build a company ground up using all sorts of tooling. So that has changed drastically. 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. At my last company, at that stage, we were 75-80 employees, let’s call it 85 maybe on the upper end. 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 payroll and HR, like we had a bare basis. This time for that same revenue, we were probably 35 people, so like less than half or right about half the headcount, but we licensed and use 50 to 70 products.

Siddhartha Ahluwalia 21:18
Tooling has gone 10x.

Shashank Saxena 21:20
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? Michael Jordan was a great, great, great basketball player. And at some point, he decided to take a break in his career to go try baseball.

Siddhartha Ahluwalia 21:48
Yeah.

Shashank Saxena 21:49
And he didn’t do that well in baseball and 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, retool, and 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, but I don’t want to be stuck in the metaphor where it’s like, I’m trying my hand at baseball and not doing as well, right? So, we just have to constantly be learning and evolving. And I’m trying to spend a lot more time with a lot younger people, trying to see how they think, how they are solving problems. We’re trying to bring that energy into our company too. 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.

Siddhartha Ahluwalia 22:47
So do you see 23 years old selling to the largest of enterprise today, which was not possible like.

Shashank Saxena 22:55
Yeah, happens all the time. You see some of these growth benchmarks and growth numbers of these companies, I think. Now, it’s a different kind of revenue, right? A lot of like, in my last company, 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, right? These days, 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 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? That’s a one time revenue and profitability matters more than like, so CAC, LTV ratios, all of those things. That’s all under question, because I just don’t know where the puck settles there. And it’s a little too early to tell or mid cycle to tell. So we only know that in hindsight.

Siddhartha Ahluwalia 23:58
Yeah. And this time, how will you choose your investors at every round? Because I believe, being a repeat founder, you have the luxury of doing that, right? When there are multiple offers on the table.

Shashank Saxena 24:09
Yeah, but this one for all the way up to series B. So series B is the first round I raised at this company, because C then A was done by Somesh. So I just made a bunch of intros for him and it played out that way. So we’ve been very happy. Epic and Bowery did the seed round. They were investors in my last company too.

Sierra did the A and I ended up, I liked the team so much that I ended up joining Sierra as a GP myself. Quinton at General Catalyst is legendary. He’s really, really good. He’s involved in a lot of meaningful companies that are becoming very real businesses. So we are very, very fortunate to have him on board. Snowflake Ventures came in as a check recently. So yeah, overall, it’s been a very, very good roster and very good people around the table. So in terms of board meetings to give us feedback, Hitachi Ventures is an investor from a strategic standpoint. So yeah, we’ve got a good group around us.

Siddhartha Ahluwalia 25:06
How do you get, let’s say, 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?

Shashank Saxena 25:19
Yeah, with Snowflake, the partnership has been phenomenal because the value goes both ways, right? So we’ve launched an app on Snowflake Marketplace. So Snowflake customers can use us and Snowflake teams can help with distribution. But the flip side is we’ve also invested in a product experience with Snowflake that’ll make the customers’ life valuable, right? So a few different examples on that, like Snowflake knows one hop to the left and right of Snowflake. Pantomath sees it all. 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 of 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? Because a data quality issue is not just someone fat fingered a field, like we’ve got enough checks for that already. It’s very often like something broke while data was in motion, right? So that’s part of our core observability product anyway. So we integrated Snowflake’s DQ framework. So there’s a lot of 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 product innovation and because we’re live in the marketplace, we’re getting help on distribution.

Siddhartha Ahluwalia 27:05
Got it. And today in the data category, we are discussing it offline. What are the things that are already done? And what are the remaining opportunities for entrepreneurs?

Shashank Saxena 27:15
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 5, 7, 8, 10 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’ve got 5TRAN and BBT merger. You look at, 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. And there has to be some consolidation. And I’m not saying that more from a competitive 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 trying 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 3, 4, 5 solid options in every category. A customer does not need 15 options in every category, more than half of which are mediocre, right? Like that’s just not healthy industry dynamics.

Siddhartha Ahluwalia 28:36
Got it. And so you’re saying that it’s harder to create outcomes like Snowflake or Databricks in data?

Shashank Saxena 28:44
Oh, Databricks size outcomes in data. That’s going to be pretty hard. 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 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 things are just never easy to begin with.

Siddhartha Ahluwalia 29:09
Yeah, but as an entrepreneur, the bar has got much higher, as you said in Mingly’s case, I believe the outcome you would have had in mind is let’s reach 100 mil ARR, right? Today, though reaching 100 mil ARR is more tougher. I believe maybe you can share your perspective on that because the categories have become crowded, right? And there’s this blurring of categories today also. But still at 100 mil ARR, the benchmarks have become so high.

Shashank Saxena 29:39
Yeah, 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, triple, triple, double, double, double, or whatever the norm used to be. That playbook’s 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 mil ARR. Is that true ARR? I don’t know. But the point is they’re getting to those numbers. So it’s going from a little bit… We see in startups there’s never a normal distribution, but theoretically speaking, just illustrating the point.

The point is there used to be companies that kind of would fit the mold that are roughly doubling, tripling every year. Now it’s kind of becoming more bimodal in terms of like the haves and have-nots and that divide is 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.

Siddhartha Ahluwalia 30:43
Or owning a category or the narrative is like that. How do you own a category?

Shashank Saxena 30:48
Yeah. And that’s harder because every category is more crowded now.

Siddhartha Ahluwalia 30:52
Yeah. And especially, let’s say if we look at data, the data within the 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? So, is it fair to say that our data in an enterprise has gone up 10x or 5x in the last few years?

Shashank Saxena 31:16
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 is like, you are seeing that right in terms of explosion of data, not just human created data, machine data, like you are seeing all these adjacent categories emerge, like how do you secure AI agents and enterprise non-human identity, all these other different, I would say secondary categories that are emerging as byproduct of managing the data and like 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.

Siddhartha Ahluwalia 31:49
Yeah, because let’s say, fair to generalize that the human identities in an enterprise might become half over short or long period of time, but the number of non-human identities would be 10x or in some cases 50x, 100x.

Shashank Saxena 32:06
Possibly, possibly. I don’t know how much of 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 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 there are multiple different opinions and point of views on that topic. So, yeah, I don’t know, I don’t know the first half of the sentence, but the second half is very likely to be true.

Siddhartha Ahluwalia 32:37
But let’s say, you said across both your companies to achieve a similar outcome, you require half as the amount of team to do that.

Shashank Saxena 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 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 a much, much more significant cut at that point. And then the other thing is, are we 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 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 will 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. So, if you look at brokerage trading platforms and 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 the 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 ABC or XYZ transactions, buy these many units or sell these many units. That’s not the main conversation, right? And 25 years ago, that was a conversation you had. And as a result, now that you’re discussing more strategic goals, the amount of money people have put in 401k is 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 all 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.

Siddhartha Ahluwalia 35:54
So, what you’re pointing towards like Jermon’s paradox, if work becomes cheap, then more works get done.

Shashank Saxena 35:59
Exactly, exactly, yeah.

Siddhartha Ahluwalia 36:02
And you’re not only providing data operations center for data at rest, you’re also providing for that data in motion.

Shashank Saxena 36:10
That’s right.

Siddhartha Ahluwalia 36:10
How do you do that?

Shashank Saxena 36:12
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, 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, this table has 50,000 rows every day, today it has 5,000, or this job takes one hour to run every day, today it’s taking two hours because that extra hour is 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 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 understanding all the movement of data enterprise-wide while keeping the data secure behind a customer’s firewall.

Siddhartha Ahluwalia 37:28
And also, 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.

Shashank Saxena 37:37
Yeah, that is different. Customers are all over the spectrum on that one. Some say that humans screw up more things as long as they can put guardrails and set a certain set of rules and genes of tasks to do and tasks 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 going to 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. And that’s okay, because 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 going to run our own data centers and they were late adopters to the cloud. And that’s okay. And that’s always going to happen with any transformation of technology. But today, I would say it’s more ambiguous and more of a mixed bag and 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. So, today when we say we’ll resolve issues, today we do it human in the loop, where the AI agent gives a 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 2am 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 11 times. It’s okay. I’m okay with it. 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.

Siddhartha Ahluwalia 39:48
The world is changing so fast right now. In your journey, did you see the world change so fast?

Shashank Saxena 39:56
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. 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. That’s been true for everything and AI it is. But a lot of counterintuitive contradictory things are happening. The traditional wisdom here was software companies are meant to be software companies and don’t worry about services. So as a software company starts sailing, it’s like, oh, go stand up with Accenture and Deloitte and KPMG of practice so that they can do their implementation work. We don’t want to be in the services business. We’d be in the product and software business, high gross margin business, services is low gross margin. So that was traditional prevailing wisdom. Until look at what we’ve seen in the last one week, Anthropic and OpenAI and everyone wants to get into the services business. And it’s just areas that you didn’t think they’d get into. So a lot of the traditional norms are being re-challenged. And that’s why the change feels even 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.

Siddhartha Ahluwalia 41:19
What are the other things that you are seeing the pace of change so fast?

Shashank Saxena 41:24
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 physically 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 or the mundane task that a lower wage worker does 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 makes this time way more exciting.

Siddhartha Ahluwalia 42:11
How do you cope up with so much unknown as an entrepreneur?

Shashank Saxena 42:17
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. So that’s why I’m also doing my second startup, right? Like because like you enjoy this, like 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 a time to figure out 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.

Siddhartha Ahluwalia 43:15
Because when you would have joined Sierra as a full-time investor, you wouldn’t have imagined like within 12 months.

Shashank Saxena 43:21
That’s exactly right.

Siddhartha Ahluwalia 43:23
So the pace of change of the world couldn’t hold you back.

Shashank Saxena 43:28
Yeah, exactly.

Siddhartha Ahluwalia 43:31
And let’s just go back to your first journey, you know, before diving deep into, you know, the data and Pantomath. What made you start VNDLY?

Shashank Saxena 43:43
So prior to VNDLY, 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, we’re 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 as 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. So a lot of the motivation to do a startup was more personal rather than professional accomplishment driven. So that’s why I started VNDLY as an experiment to see, it’s like, okay, can this work?

And then we also luckily stumbled upon a very good category that had not been extremely innovative for over, I would say, a decade, decade and a half. So that’s where things worked really well at VNDLY because it was a lot of good things that happened and just triangulated and came together at the same time. So yeah, it was, I would say that journey was very personal motivation driven rather than anything professional.

Siddhartha Ahluwalia 45:52
Can you share the VNDLY journey in detail?

Shashank Saxena 45:55
Yeah, we started the company almost mid-year 2017. We launched the product early 2018. 2019, in January or February, battery came and led our 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. So Insight preempted us. 2020, COVID happened. So we had a really bad quarter in between but then things again went back on track 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.

Siddhartha Ahluwalia 46:42
And how did the Workday acquisition happen?

Shashank Saxena 46:47
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. If you think about what was happening in the enterprise in that time frame, Workday was replacing PeopleSoft and Coupa was going after Alibaba and ServiceNow was replacing Remedy and this whole next-gen come replace legacy predecessor, 76% of our revenue came from replacing the other older VMSs. So we fell very squarely into that category. So a lot of it is right place, right time. So that’s where most of our customers would modernize their HCM with Workday and then come to modernize their VMS with VNDLY. And you could see a bunch of your workforce in Workday and then VNDLY was a Workday partner. So we built that connector and now you can see your whole workforce, employees and contractors within Workday.

So it was really, really valuable for the customers. 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 they’re very in touch with the customer sentiment as well. If you met Anil, he’s just a very sharp product mind in terms of knowing product strategy, where to go, where to take the product and it’s just made a lot of sense. So overall, I think it was, it worked out really well for everyone involved, including the customer, including Workday, including us here.

Siddhartha Ahluwalia 48:36
At VNDLY, you had mapped the entire contractor onboarding to contractor exit process.

Shashank Saxena 48:41
Yeah. And that’s where we were partners. So Okta was an investor in VNDLY, ServiceNow was an investor in VNDLY, Salesforce was an investor in VNDLY and Salesforce at the time was because they launched a platform called Work.com during COVID. So yeah, I mean, a lot of those partnerships helped too, because with Okta we built a proprietary integration that helped with the SSO and onboarding and all of that checklist.

Siddhartha Ahluwalia 49:05
So it is said that building a startup in SF versus anywhere else in the US is very different, but it seems like at VNDLY you were having a great pace in terms of customers, investors, right? So can you share your experience building in two different locations?

Shashank Saxena 49:26
Yeah, that pendulum swings depending on timing too. So during the VNDLY time, I never felt compelled that I need to move to the Bay Area to build this. VNDLY could have been built, like VNDLY was built in Ohio, like in Cincinnati, the whole journey start to end. And that worked really well, because at that time, there was not information asymmetry in terms of tools and usage, right? VNDLY also happened when, I call it a little bit of 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 app-layer company, it was not an infra-layer company. So a company like VNDLY 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 ballgame in a completely different time given how quick AI is changing. And 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’re seeing right now. And here in the Bay Area, it’s a very exciting time to be here and build here.

Siddhartha Ahluwalia 50:56
Got it. And today, how many Fortune 500 logos you have?

Shashank Saxena 51:00
That’s a good question. I don’t think I know the exact count, five, seven.

Siddhartha Ahluwalia 51:04
Okay.

Shashank Saxena 51:00
Somewhere in that range.

Siddhartha Ahluwalia 51:05
Wow. But for an early stage company, it’s still two and a half years.

Shashank Saxena 51:11
Yeah.

Siddhartha Ahluwalia 51:12
Quite commendable.

Shashank Saxena 51:13
Because we solve the enterprise problem, right? It’s meant for, it’s made for big enterprise.

Siddhartha Ahluwalia 51:18
What is your learnings from now, you have been selling to enterprise for almost like a decade as an entrepreneur, your learnings on selling to enterprise, right? How do you discover problems? How do you, because it’s very hard to get to the right POC, build champions, build a series of champions to win an enterprise deal.

Shashank Saxena 51:39
Yeah, that is true. But a lot of it also comes down to how real is the pain point? 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 of financial and budget dynamics in the enterprise, which are constants. 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 is true for VNDLY. If I started VNDLY five years sooner, it wouldn’t work out. If I started VNDLY five years later and the AI was wave, it wouldn’t work out, right? The timing, which is where people call it luck or destiny, because that’s not something you, market dynamics are not something that you influence with hard work. The timing was right. Same thing for Pantomath right now, right? We’re building and we believe that 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 timeframe. So, you have a bunch of early adopters. As I said earlier, it’s a bold conversation of how do we get AI right? So, the timing of these things really, really matter, more so than the standard enterprise sales cycle.

Siddhartha Ahluwalia 53:13
Got it. So, in your case, in both the journeys, you have been able to figure out timing, right? How do you do that?

Shashank Saxena 53:22
No, Pantomath 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. Every day building VNDLY was like, I don’t know if you’re doing the right thing. I don’t know if this is going to work out. How’s this? And you feel high and you feel low and you feel all the emotions in between. And so, when you’re doing it, it never feels right because 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, and so, the only time you really sit back and reflect is after the journey is done, which is like, oh yeah, we actually got these three things right and I wish we did these three things better. So, all of that is in hindsight and when you reflect.

Siddhartha Ahluwalia 54:15
So, I would say VNDLY in the HR category would have been one of the largest exits at that time. You sold it for 500 million plus. You got tier one investors during that journey. If you can share this with a lot of entrepreneurs, we get 5 million views a month on the podcast. 80% are from India, but 20% are from California. So, what was your strengths that you raised in your first journey, starting in Ohio from tier one investors?

Shashank Saxena 54:52
So, it’s very kind of you to say, but there have been way bigger exits in the HR tech. So, I don’t consider VNDLY as one of the biggest, but coming back to more of the sentiment of your question, I don’t know. I don’t know how to answer that because I feel for VNDLY, 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, you want to push really, really hard and go really, really fast. So, yeah, it’s that that kept us going. And as I said, in that wave, we were starting to see Chicago startup ecosystem was doing really well. Atlanta started to thrive. LA, we were starting to see good activity.

So, today in the AI wave, I feel it behaves as the epicenter, but I feel when we did VNDLY, you were starting to see all over the rise of the rest of America and the startup ecosystems there were starting to happen. So, overall, 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, investors will follow. So, 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? In the Bay Area, yes, there’s plenty and plenty of good investors in the West, but there’s plenty and plenty of great companies too, right?

Whereas in the Midwest, it’ll be a smaller pool of startups. At the same time, it’s not the area that’s where investors are zooming in and focusing on either. So, it varies and these things, again, as I said, like 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.

Siddhartha Ahluwalia 56:59
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 not because investors also look at which are the markets, is it a market that can create multiple 10 billion dollar companies because that’s the outcome at least series A and series B investors want. So, how do you figure out that?

Shashank Saxena 57:24
So, I honestly feel like I didn’t pick the problems, the problems picked me. It’s 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 we see is do, it just didn’t happen that way.

Same thing in terms of VMS, like my co-founder there Narayan, like he was already living in these systems, he knew these systems well. So, he already was very keen on that category.

Siddhartha Ahluwalia 57:51
Starting in that category.

Shashank Saxena 57:52
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.

Siddhartha Ahluwalia 57:59
It’s also being around the right people.

Shashank Saxena 58:02
Yeah, that matters a lot. Actually, that is a very very big factor.

Siddhartha Ahluwalia 58:07
So, I think Bay Area influences that if you can just surround yourself with, I think, the right set of people.

Shashank Saxena 58:13
Yeah, I agree and that’s why I’m like VNDLY, I feel that company was the right one to be built in Ohio and not in the Bay Area because I feel I was isolated and in a little bit of my own shell there, which is good because I was heads down and focused. 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, 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. 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.

Siddhartha Ahluwalia 59:11
Can you share an example?

Shashank Saxena 59:13
Look at what happened to cyber security in the last few weeks, right, and Anthropic and 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 is buying it. Tomorrow you walk in and try to sell the product and the customer is like, oh yeah, the whatever, name your LLM, Gemini guys, GCP guys, anthropic guys, OpenAI guys, they were here and they said it’s on their roadmap and they’re doing it, so I’m just going to wait for them.

Siddhartha Ahluwalia 59:57
Yeah, like what you shared in terms of how easy it became to sell when you leave within one 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 else’s purchase order.

Shashank Saxena 1:00:11
Exactly, so that’s the thing is like right now things are just moving so quick, 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.

Siddhartha Ahluwalia 1:00:28
So we were discussing offline about you know as a second time founder, what’s gets easier versus still remains hard.

Shashank Saxena 1:00:40
So I’ll answer that question in two different buckets, right. So let’s say I’d done my first company in like 2006 to 2010 time frame and my second one was 2014 to 2018 or 2020, right, like in similar-ish time frames. 80-90% of learnings, or pretty actually close to 100% of learning 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 that was 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 a product using AI tooling, how many developers you need, how product management should be done, how your release notes are written, 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 the 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? 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, I would say everyone who has learned everything over the last 20 years, now during this AI phase, has to like unlearn and relearn. And that’s why when I said like AI native, like we need more AI native people overall, not just at Pantomath. Yes, we need them at Pantomath, 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 same like running operations at a company, financials, fundraising, your cap table, like all of those things you know. So yes, 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 timeframe 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.

Siddhartha Ahluwalia 1:04:02
But two things remain easier as compared to a first-time founder, which is first is building the team because all the top performers in your past team would want to join you again. I assume that. And second is fundraising.

Shashank Saxena 1:04:19
Fundraising is definitely easier. No two ways about that. 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 the right horse for the right track. So that may or may not be completely identical. Because this is also where repeat founders, and I’m speaking on behalf of myself and other friends too that I end up saying both, 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. So my head of engineering, instead of going to my previous company and hiring, which I absolutely love them, so something on them, I hired someone from Databricks and GCP. Because they understand data, I’m building in the data category.

They have a network of people that have worked at these companies and understand data. So it’s just a completely different ballgame, 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. That’s not the goal. Who is the right person for the set of problems that you’re trying to solve for? So that’s why it’s different. And it’s not that easy.

Siddhartha Ahluwalia 1:05:51
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?

Shashank Saxena 1:06:04
Yeah. So a few things. One is, especially in data, it’s true where most companies want to do a POC, right? In your traditional enterprise sales, like take Workday or SAP or Oracle, they don’t do POC. That’s the first step of the process. Second step of the process is when you start thinking about infra companies, you’re selling more to a technical audience than to a stakeholder.

Coupa 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 Pantomath too, 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 changed, which is good and bad, right? Like we hear 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 you’re 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 6 million credits and use 10? 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, the bill varies and it’s going up drastically.

So how much do you budget? How much do you factor in? Like at our scale, it’s okay. But if you’re a large enterprise, like 50,000 employees, like that’s a massive amount of variability you’re signing up for. So again, it’s the only point is like all of these things that are traditional conventional wisdom changing in terms of how you go to market.

Siddhartha Ahluwalia 1:08:33
So you mentioned also that you are selling on a problem that can happen at scale tomorrow with an enterprise, but today to show the ROI, what dollar ROI or what cost ROI are you able to make?

Shashank Saxena 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 80 people that are in your support org, it depends, it varies by company. 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 moved 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 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, but $80, $100, $120 an hour 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’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 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.

Siddhartha Ahluwalia 1:10:36
That’s quite powerful. And across both these companies, you know, if you have to, you know, I know it will be hard to recall, but what’s been the best piece of advice or advices that you have received that changed the trajectory of these companies?

Shashank Saxena 1:10:57
Overall, in terms of advice, it’s less to do with company. Like if you think of the best mentorship or board member relationships I’ve had. So one of my board members in at my last company was Steve Singh, the founder of Concur 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 I consider him a father figure in my life and 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 along those angles and that’s why actually he’s an angel investor in Pantomath too for that reason. So anything I ever do in life, like I always want him around the table. So those are the kind of things that have resonated and stayed with me along the way.

Siddhartha Ahluwalia 1:12:18
So what are the some of the principles or frameworks that you built along the way?

Shashank Saxena 1:12:23
I don’t have like a framework but in terms like principles right like a lot of it comes down to how well have people done, how much of their lives changed as a by-product of me starting a company or raising money or distributing a certain amount of equity. Like at VNDLY, I think the first 17, 18, 19 people that joined us all became like multi-millionaires when the exit happened and the conversations and you have with people right. It’s like a mortgage for a house that’s paid for a kid’s 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.

It just is very heart-wrenching and heartwarming in terms of the impact that you feel on people’s lives right. Steve once said this to me like listen Shashank, the difference between having I don’t have a way of exact numbers you use but let’s say 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 right. So it’s like how many people can have and one of the things he measured was during his concur journey is how many people that became millionaires along that journey with him right. Those kind of metrics when you start measuring your life in those things become very meaningful 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.

Siddhartha Ahluwalia 1:14:14
I want to touch upon this because many founders today are going through their M&A’s journeys and M&A’s are only happening faster we discussed that. Today zero to two hundred million dollars M&A happens in a period of 30 to 45 days.

Shashank Saxena 1:14:32
Yeah I don’t know how sustainable this is what’s going on right now but yes it is I’ve seen it more even as a VC even as a fellow founder I have friends whose companies are getting acquired or even yeah.

Siddhartha Ahluwalia 1:14:47
What’s your advice to founders on M&A the journey that you have 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?

Shashank Saxena 1:15:00
I don’t know if I’m one to give a lot of advice there because that is so so so deeply personal. 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 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 like shit I could have been the category king and a great example is Jyoti Bansal right like if you see AppDynamics AppDynamics 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 AppDynamics 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 that could have been AppDynamics.

So 3.7 billion dollar exit fantastic by every measure 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 off or don’t assume the next offer is six months out yeah 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 the next offer six months out.

Siddhartha Ahluwalia 1:17:47
But that’s a great framework to think from for founders.

Shashank Saxena 1:17:50
Thank you. Yeah so a lot of it is personally driven like in fact when you ask me like why did I start VNDLY 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 the 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 I want to like having a CEO title is good like none of that stuff factors in in my decisions or like a personal career path objective none of that factors and 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.

Siddhartha Ahluwalia 1:18:37
It makes sense because if you’re personally aligned a lot of professional part will take care of itself.

Shashank Saxena 1:18:43
Yeah and you do right things for the right reasons and the rest of it just takes care of itself.

Siddhartha Ahluwalia 1:18:48
What have been your strength as a founder during both the journeys?

Shashank Saxena 1:18:53
I don’t know 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 to 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 to 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 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.

Siddhartha Ahluwalia 1:20:25
What has been the 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 has helped you reach here?

Shashank Saxena 1:20:39
Taking risks and more importantly than taking risk is from a personal life standpoint putting yourself in a position where you can take risks and you don’t have to live with the guilt of it.

So let me give you an example when my wife and I were both working we used to 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 car 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 cut and do a startup and now my personal ambition I’m 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 that 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 that when the opportunity comes to take a bigger risk you are capable 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.

Siddhartha Ahluwalia 1:22:32
For first-time founders 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 is tough because you don’t get a direct answer sometimes and people are not usually working to solve a problem maybe but let’s say an individual is working towards optimizing their work aiming for their promotion that’s why they say right if your tool works well your champion gets promoted and that’s what you should optimize for.

Shashank Saxena 1:23:09
That’s exactly right.

Siddhartha Ahluwalia 1:23:12
So how did you learn to know the enterprise like selling is the later part but understanding the enterprise well.

Shashank Saxena 1:23:23
So for me I actually came from enterprise. 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 start your career at a SAP oracle salesforce service now workday 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.

Siddhartha Ahluwalia 1:23:57
And you were responsible for transformation.

Shashank Saxena 1:23:59
Exactly, so I was the buyer. So for a lot of these products when I was a buyer I know 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 when he was not very conventional. And when we sold the company we had like four reps right because then we didn’t focus on 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 they care about what matters to them because we came from that side we were the buyer.

Siddhartha Ahluwalia 1:24:34
Yeah and when you were the buyer what mattered to you?

Shashank Saxena 1:24:38
Okay, I’ll give a very tangible example 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 the 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 a list of questions right. We’re 20 minutes into the meeting, 25 minutes into the meeting, you’ve told me nothing about you and your product 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 crack. Like why are you 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 the same words back to me and wrap it around your product wrapper and 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. So at Bentley 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. It’s conversation 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. 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 of like having six prep calls before the demo to make them jump through hoops to see a demo and just rehashing same questions and 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. 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 sales people and those roles are important but 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.

Siddhartha Ahluwalia 1:28:20
And what would you advise to founders? Let’s 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 will be built or it is already built right and then they try to go back to the product teams.

Shashank Saxena 1:28:38
I mean that can work sometimes some bit but see the difference between a visionary and a con man is execution right. If you commit to something and you show up and deliver it and it works really well great like you 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 tackle. So yeah you have to be very very careful playing those games for the most part. Listen you can do that in seed stage when you don’t have and you’re trying to pretend as if you’re bigger than you are because otherwise you’ll spook out large fortune 500s 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 disclosed in the beginning we had no connectors so we would talk to the customer and we like okay what connectors do you need and those are the ones we built right like it was very simple as that. 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 there’s one or two that they don’t have and 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 you’re in the contract that’s signed. So there’s always that trade-off that you can have with the customer and have that conversation.

Siddhartha Ahluwalia 1:29:56
So at VNDLY let’s say when you were at seed stage and you were asked by customer for connectors how transparently would you tell this is not built or anticipated?

Shashank Saxena 1:30:05
Pretty transparently like our Workday connector we needed. 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 and Workday they click stuff and automatically those things happen in VNDLY 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 to the Workday to give us access to those APIs. So once 10 customers bring Workday, Workday made us a partner give 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. Once Workday approved it like I think three months after Workday approved it they made an offer to acquire us.

Siddhartha Ahluwalia 1:30:54
Wow.

Shashank Saxena 1:30:55
So we got acquired mainly because of the connectors that we built with Workday and how good they were. So that part worked really well. So yeah, I mean that.

Siddhartha Ahluwalia 1:31:07
Yeah, the radical transparency really worked.

Shashank Saxena 1:31:09
Yeah.

Siddhartha Ahluwalia 1:31:10
Thank you so much Shashank. It’s been an amazing conversation. Thank you for being so candid.

Shashank Saxena 1:31:16
No problem. Thanks for having me here and I enjoyed the conversation. Good luck on your podcast.

Siddhartha Ahluwalia 1:31:20
Thank you so much. It’s been amazing discussing your life journey, your lessons and what you’ve distilled throughout this journey.

Siddhartha Ahluwalia 1:31:27
Thank you.