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Anshul Gupta

Co-Founder

Actively

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Anshul Gupta, Actively

Anshul Gupta

Co-Founder Actively

Anshul Gupta, co-founder of Actively, shares insights during theCUBE's live coverage at the NYSE Wired Robotics and Artificial Intelligence Media Week. The session explores Actively's recent funding milestones and the firm's innovative approach through superintelligence in the business-to-business (B2B) space, offering guidance in blending traditional sales methods with advanced AI capabilities.

Gupta brings extensive expertise in leveraging AI to optimize sales processes and revenue streams. Alongside theCUBE Research analysts, the discussion delves... Read more

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Anshul Gupta, Actively

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>> Welcome back, everyone, to theCUBE's Live coverage. We are here in New York City, right on the show floor of the NYSE. Of course, this is theCUBE East. This is our access point, where everyone's coming into the network. Of course, there's an open, trusted network, the NYSE Wired community. We've got Silicon Valley connecting there and Palo Alto connecting Wall Street and tech. Anshul Gupta's here, co-founder of Actively AI. Got great funding news, so we get a bonus head here. Anshul, thanks for coming on theCUBE. Appreciate it.

Anshul Gupta

>> Thank you so much for having me.

>> Got a smile on your face. Got some finance... Not a big fat financing, but enough to kind of get some acceleration.

Anshul Gupta

>> Absolutely.

>> Congratulations.

Anshul Gupta

>> Yeah, thank you so much, John. Really appreciate it.

>> So, give us the news. You got some financing, give us a quick update on the news.

Anshul Gupta

>> Absolutely. Yeah, we're really excited to publicly launch Actively AI and announce both our seed and series A, led by Bain Capital Ventures and First Round. And really, establish our category positioning as go-to-market superintelligence for enterprises in the B2B space.

>> So, let's talk about the company, give a quick background. I love the superintelligence for sales. As you see AI factories, physical AI, you're seeing for the first time a blending of physical face-to-face assets, whether that's factories, robotics, flying vertical cargo haulers, trucks. But sales is also a physical face-to-face relationship and it's always been, "Hey, I'm on LinkedIn. I got these tools and CRM, outbound marketing, email marketing, webinars." So, that was separate, SEO, all kinds of separate, digital marketing, different bucket, different departments. Now, the physical and digital are merging on a first-party basis. This is a huge dynamic. Can you explain... Well, first of all, what's your reaction to that? I'm sure you might agree given that you're going down the superintelligence route.

Anshul Gupta

>> Absolutely.

>> What is your view on that market dynamic of now blending physical face-to-face with digital systems, digital money? I mean, everything's going to digital?

Anshul Gupta

>> Everything's going digital. Our perspective, and I agree with everything you said, our perspective is that sales fundamentally is a matching problem between someone who has a need, someone who has a problem, someone who has a budget, and someone who has a solution. And so, how can we build that marketplace to connect people more efficiently? And there's all this technology around the fringes that has the potential to make that better. And that's what we spend a lot of time thinking about and how in the age of AI and the age of intelligence can you accelerate company's ability to drive more revenue, build more of those connections and help provide more value to their markets.

>> And so, talk about the problem that you guys solve specifically and what mechanism are you using? What is the product?

Anshul Gupta

>> Absolutely.

>> Give a quick positioning of the company's offering.

Anshul Gupta

>> Yeah, so zooming out in terms of why we're so focused and excited about this problem, and I think it's such a big one to solve is, if you look at the companies, right? We're in the New York Stock Exchange, if you look at the companies that have the fortune of ringing that bell over there, 30% to 40% of every single dollar that they earn goes into that line item of S&M, of sales and marketing. So, every single dollar in revenue they're bringing in, 30 to 40 cents are going out in sales and marketing. And if you double-click on those 30 to 40 cents, a lot of that is spent on people. It's spent on people, human beings. And so, if you're a CEO, if you're a chief revenue officer, if you're on the board, it's incredibly important that you're optimizing those resources of every dollar that's coming in. Is it optimized to drive the maximal revenue and growth that you want? The problem is in today's day and age, it's nearly impossible because there's so much data. As you just cited, John, there's all these different sales and marketing systems that have pieces of the prospect funnel and the life cycle of sales. And we're asking sales reps, on average who have maybe 12 to 24 months tenure. It's a high-turnover role, as you probably know very well. We're asking them to answer a big data problem of, "Look through all those data points every single day and make the most optimal decision." And so, in sales teams, as you know, there's an 80-20 dynamic of your A players and then everyone else. And so, how can you replicate the depth that those A players are leveraging who then ultimately are going to go move on? And so, how can you leverage that at scale? That's where Actively comes in, which is where the system of intelligence on top. We train custom-reasoning models to every business in their specific go-to-market function, their specific products, their specific nuances. And we have the cheat code of being able to process millions of data points to tell teams where exactly they need to spend their time, what drives revenue. And it's called Actively because it's short for active learning, which came out of our research at Stanford. My co-founder, Mihir, has published in that space, which is all about how do you learn from what's working and continue improving over time and be always on and drive maximal revenue?

>> I mean, what I love about this AI leader series... We're here with robotics and AI leaders. Robotics gets a lot of the attention because it's robots and Nvidia just had an event which was more hype into the cycle, which we love by the way and it's legit. But process and domain expertise is where the action is.

Anshul Gupta

>> Yes.

>> So, then you have horizontal scale data all over the place. So, it's a data-wrangling problem, data-aggregation problem. But also, at the end of the day, sales is a process. You've got a funnel you've got prospects and you got customers. You want to keep the customers, sell more to them, provide more value, and get those prospects in to close or onboard them. So, very mechanical mechanism, that could be instrumented.

Anshul Gupta

>> Yes.

>> So, is it a funnel challenge for you or is it more of the data points you're bringing in, or both?

Anshul Gupta

>> Well, it's optimizing that funnel, right? Because to your point, the problem is very clear, which is we have a potential total addressable market as a business, that if your revenue team at a large company, you want to go tackle that market. To your point, attract prospects and bring them down. So, who should you focus on? When should you focus on them? Who's most likely to convert? To your point, that's an optimization problem where there's so many different data points to improve that process. And the impacts are huge, because if you can increase your win rates, if you can reduce the time spent to actually find the right people, that goes back to that 30, 40 cent optimization problem of every dollar that's coming in that I talked about.

>> Yeah, cost per order dollar is a big metric. Let me ask you on the target. Are you guys targeting B2B, B2C? What's the main target area? Or are there's patterns that match super intelligence in all verticals?

Anshul Gupta

>> Yeah, it's a good question. I mean, ultimately, the vision is to power every revenue organization of a certain size in the world and be that system of intelligence that powers their go-to-market. We're specifically focused on B2B. And interestingly, unlike many other players, we're actually hyper-focused on mid-market and enterprise companies specifically, rather than SMBs because of the nature of their data, the complexity of their processes.

>> Well, we certainly can help. We have a lot of B2B customers too. They sponsor us. Always looking for go-to-market. But go-to-market is a topic that comes up a lot. Certainly, when I've been doing 15 years of this, theCUBE, before that podcast thing, B2B always has a go-to-market, GTN they call it, as a key part of their revenue, strategy people. And a lot of things, and I want to get your thoughts on this, because it's an observation we have, is that some clients have modern approaches with people. Some people get it. And you can see the early indicators, they go to LinkedIn like a fish to water. Why? Because LinkedIn has direct access to graphs and the tribes of targets.

Anshul Gupta

>> Yes, exactly.

>> You can target prospects and go in the rabbit hole, so that speak and just win deals. But that's just scratching the surface, right? So, as an organization, most companies I talk to are like, "My people just aren't leveled up." So, there's a people problem. And okay, "What data do I get? What size do I need to be? When do I get the super intelligence? What's the requirements?" So, two questions. What's the requirements that you guys need to see to start getting benefits off the superintelligence path for sales? And two, how do people get modern in their tactics and strategies?

Anshul Gupta

>> Yeah. And what we tell organizations who we're talking to is look at your A-players. Every organization should have A-players of some level of size. If they're driving hundreds of millions-

>> Everyone thinks they're an A-player.

Anshul Gupta

>> Yeah, everyone thinks they're an A-player. But if you look at the leaderboard and you double-click on who's actually performing incredibly well, what we've noticed those individuals spike on is in depth and precision, rather than this sort of spray-and-pray approach. And so, the idea with super intelligence is can you replicate that depth and precision that the A-players have, but with the, as I like to call it facetiously, the cheat code of being able to index across millions of data points, optimize org-wide rather than just that territory that sales rep is focused on? And so, that is what, when we're talking with organizations who ask those same questions that you just asked, John, is what we tell them. And so then the question becomes, when are you at the right point to scale that reasoning and depth? It's when you sort of have an established sales motion, you have the outbound functions that are driving revenue and you're looking to optimize and increase it. And so, that tends to be companies that have multiple different segments, maybe they have multiple products, and that's where it starts becoming a really good fit.

>> Yeah, one of the things that's comes up in this AI wave, and this is again... I love this market, by the way, because everything's new, but it's not... Old is now in the new again, but mechanics are tweaked a little bit because the processes are different with AI and the data. So, I noticed that, and I've used the term digital twin with Brian Baumann here at the NYC wire because NYC Wired is essentially a digital twin of the community for NYC and theCUBE goes to events and we do our CUBE events. But when the event's over, we break down the set and we go to digital and we have studios. So, the notion of a digital, and I've been criticized for using that word because most people go, "That's not the true definition. It's got to be a meta version and a factory." But what you're basically doing is creating a digital twin of the A player. You're making a system that matches the process, identifies what's working and scaling it.

Anshul Gupta

>> And scaling it to everyone within the organization.

>> So, you're replicating a kind of digital-twin concept?

Anshul Gupta

>> In a sense, but our view is we do it at an organizational and system level, which is why we call it a system of intelligence rather than... You might've heard terms like a AI copilot. So, we're not an AI copilot because our view is we're at that intelligence layer that's directing sort of all the different individuals and where they need to spend their time, but replicating that A-player behavior.

>> Well, this is a proxy for the system.

Anshul Gupta

>> System. 100%.

>> I want to replicate the winning hand.

Anshul Gupta

>> Exactly.

>> I want to do that again. Repeatability with no friction.

Anshul Gupta

>> With no friction. Yes, yes.

>> Okay, so let's take that because now this brings up another structural question. Most organizations have pre-existing siloed workflows. I get my leads from this. I got my prospecting database here. I got my system of record, maybe it's Salesforce. So, now I have a legacy, siloed, stovepipes, whatever you want to call it. Now, I'm like, "Okay, now how do I abstract away that? Do I put a sales data layer on top?" Take me through the system thinking around if I want to deploy Actively.

Anshul Gupta

>> Yes, 100%. And so if you look at sales, the anecdotal shares, we spent a lot of time with sales reps. And when we were building this product out, they would share us their screen, their computer screen and they have 90 tabs open with all kinds of different systems that they have to go through and mine information through. And so, now to then introduce a net-new user interface and a net-new workflow, there's a huge, huge, huge bar and your adoption is going to be really low. So, our fundamental belief is the system of intelligence inherently should live and push into the systems that your reps are already using. And so Actively lives, air quotes, within systems like sales force or within sales engagement platforms. It can help tee up some of the stuff that you need to do in LinkedIn, as you were alluding to earlier. And that's our view where you have the intelligence reasoning engine operating in the background, but teeing all of that up first thing in the morning and how you spend your day.

>> All right. Talk about some of the momentum you got with customers. Can you give some stories and examples of the onboarding process? What are the requirements? Let's just say that theCUBE wanted to get behind the go-to-market with you guys, what do I got to do? What do I do? What are the three things I got to do? Is it disruptive? Is it additive? Do people need training? Take me through the mechanics of a use case.

Anshul Gupta

>> 100%. So, one of the core, I think, weapons that we have is we have a proof-of-concept or pilot motion that is an incredibly high-conversion rate. And the reason behind that is it's pretty low lift to get Actively integrated because it's living in those systems and workflows. And then, the second is we're able to show actual lift and tangible ROI metrics in a relatively short period of time, which then builds the business case to move forward, because if you're comparing someone who just... No matter if you're the smartest human being in the world, you just cannot process the level of data that a system of intelligence can. And the nice part about being in the sales revenue world is there's a direct tangible impact on on top-line qualified opportunities, on top-line revenue. That is a weapon for us. And one of the things that we deploy as well, because we're focused on enterprise and mid-market customers, they always say, "Well, our data's super messy. We've had acquisitions, our system... How can this AI come in and work..." Garbage in, garbage out. We hear that a lot. And so, one of the models that we've taken inspiration from is companies like Palantir that have the forward-deployed motion. And so, we similarly have forward-deployed engineers that are able to work specifically and customize the reasoning model specific to theCUBE, specific to... I know Newsmax just did their IPO, right? We wouldn't work with Newsmax, but specific to each of those-

>> They'll be a customer soon.

Anshul Gupta

>> But specific to each company, to the nuances of who you're targeting, why, your systems. And so, that forward-deployed engineering motion that we're able to set up really quickly is a huge advantage for us as well.

>> So, there's a little bit of professional services built into the engagement out of the gate?

Anshul Gupta

>> Out of the gate, yeah. And in order to customize the reasoning models specific to theCUBE, right? Or specific to Ramp, or Verkada, Ironclad, those companies that we work with.

>> What are some of the results? Obviously, got some validation with series A. It's a great milestone, it's good validation. Maybe the new acronym is SAGI, sales org artificial general intelligence.

Anshul Gupta

>> I love it.

>> And with agents, I can imagine that that superintelligence wants to be superimposed into the agent systems that are going to come out. Thoughts on that and the momentum?

Anshul Gupta

>> Yeah, so there's kind of two schools of thought in our space specifically. I know you live in Palo Alto. So, when you land in San Francisco and get ready to go down 101 or 280, you probably see these couple billboards for companies that are called AISDRs, right? So, they're saying, "Hey, you don't even need the human. We can fully kind of use agentic artificial intelligence and get the entire human out of the loop." And so, that's one school of thought, which empirically in the mid-market and enterprise has not been working well because they don't actually replicate what I talked about earlier and what you talked about earlier, the digital twin of the A player. What they do is take a volume-based approach of, in theCUBE's case, "Let's hit up every founder in the United States and just keep messaging them every single day."

>> Let's get those sponsors.

Anshul Gupta

>> Yes.

>> Yeah, let's get those sponsors.

Anshul Gupta

>> And some of them will reply, right?

>> Yeah, that's a statistical game, conversion.

Anshul Gupta

>> Yes, it's a statistical game that really just has a lot of deleterious effects because you're just spamming everybody, you're not actually relevant. And so our approach is much more different, which is how can you use the intelligence to make the humans, who we still believe that sales as fundamentally human, make them a lot more efficient. And so, that's sort of our counter take. And to the heart of your question is, okay, well, what are the actual results of that? What is the ROI? And so, we've helped fast growing companies, like Ramp, in the last year alone through our system of intelligence alone, generate tens of millions in net-new revenue from our attributed recommendation engine or companies like Ironclad, one of the preeminent legal contract management solutions out there in San Francisco. They're driving qualified opportunities at 41% higher rates. And the reason why is because we're looking out for signals and teeing up the reps for who's actually having real pain points that our system can solve? Going back to sales being a relevance-matching problem.

>> So, you see agents much more relationship-oriented or an army of agents doing a variety of tasks on behalf of that systems intelligence layer?

Anshul Gupta

>> Exactly. And then, the humans are positioned where the humans are best, which is actually having those conversations, having those interactions. And then, the system learning over time how you can do things better. Rather than saying, "Hey, you don't need any humans anymore to do sales."

>> So, before we came on camera, we were talking about Stanford, how great it is because we live in Palo Alto and had season tickets for a long time when they had good football.

Anshul Gupta

>> Yes, things have changed.

>> Before they went to the ACC. Pac-12, don't want to get me started on the whole Pac-12 thing.

Anshul Gupta

>> I know.

>> Talk about the pedigree of the founders, the origination story. How did this all come together? Were you guys riffing in school? Was it one of these things where it's like, "Hey, we came in from a tech angle perspective. We saw this as a untapped market opportunity"? I mean it's not on my bingo card to have Stanford and such great talent go, "Hey, let's go change the sales market."

Anshul Gupta

>> Go-to-market with superintelligence? 100%.

>> But this is what's going on. There are untapped markets that can be reconstructed from a TAM perspective from new entrants because the velocity of entry is there and value is value. So, there you go. So, how did you guys get to this? What was the thought process?

Anshul Gupta

>> 100%. So, both my co-founder and I were just obsessed with AI and they're at the right place, right time, where we had the early opportunity to be evaluating even some of the early foundation model technologies like GPT-1 and GPT-2. So, we've been in this space for a while, and my co-founder, Mihir, was working on his thesis in active learning, which is where the name Actively came from. And the big thing that we were really interested in is how AI can help teams or individuals that have to do what we call high-velocity decision making. Because our belief or hypothesis was that if you have a job that requires high-velocity decision making, that's where AI can outperform. As opposed to something where you have to make high magnitude decisions, but at a low-velocity. Imagine you're Chairman Powell, Federal Reserve chair. They have to make one decision every six weeks of up, down, or stay the same in terms of the rates. But if you take domains like sales or domains like trading, every single day, human beings are tasked to make 40, 50, 60 decisions a day that directly tie to specific outcomes and they're tasked with processing all of this data, in our limited sort of prefrontal cortexes, processing all of that data and reasoning through it to make decisions. And so, our hypothesis was that AI can really make a huge impact there and drive human productivity and organizational productivity significantly higher. And sales were a natural application of that, which we discovered over time. Because if you're a sales rep, you do it every single day. If I'm a sales rep at theCUBE, what are the companies I'm going to try to prospect? Why?

>> Where do I spend my time?

Anshul Gupta

>> Where do I spend my time? What am I learning? What's the feedback that I'm getting? When should I re-engage them? All of those questions.

>> What's an opportunity?

Anshul Gupta

>> What's an opportunity? That's exactly right. And so, based off where we're seeing success recently, how does that influence what I do going forward? Those are all questions that data can really help you answer.

>> Yeah. And I think one low-hanging fruit area that you guys probably see it too is every company, no one will say, "I want to talk to you about improving your sales productivity." Who says no to that meeting? I mean, seriously, everyone has the problem. It's like developer productivity, tools are coming in to make the salesperson, who has limited time, that's a scarce resource. Do I spend it on this prospect? Is it the right person? Could I be over here? So, all these decisions. Great point about the decision making.

Anshul Gupta

>> And particularly in larger companies where we see the pain point being even more prescient because if you have hundreds of sales reps, people are moving in and out of seat every couple of months or every couple of weeks. Just naturally, your territories are getting shifted. Imagine I was trying to sell to theCUBE and I just got promoted. There's a new rep coming in, they might not have known about our interactions, and they're going to just lob you an impersonalized note-

>> Spam email.

Anshul Gupta

>> Spam email. That happens all the time, and there's direct revenue impact to that.

>> What's your vision as a team, as you look at as sales teams become agentically-enabled, AI-enabled? You're starting to see AI native apps coming, tools like you guys have. I love that system thinking. I think that's the revolution we're living in. There's a lot of young sales folks who are now left Cal and Stanford. I throw Cal in there, even though there's a rivalry.

Anshul Gupta

>> There we go.

>> Okay. I always ask them... I won't get into, but the Cal-Stanford thing, it's a little inside joke. But you have now the pros that are growing and becoming the managers are younger digital natives. That's why LinkedIn's successful. LinkedIn's now a relationship layer, but that's just LinkedIn. It's a walled garden. There's other things. So, to the sales managers out there and to the young sales executives, what's in it for them? What does this do for them?

Anshul Gupta

>> Yeah, I mean, fundamentally, it's all about productivity. Sales is a nice domain too, because literally, if you do your job better, you get paid more because you get a fraction of everything that you bring in. And so, the incentives are literally perfectly aligned. And so, for us, AI has the potential to increase productivity, increase your performance and revenue that your organization can bring in. And the CEOs, CROs, board members are hyper-interested in that. And so, to your point, this newer generation of sales leaders, they have that spot on because they see people using ChatGPT and other AI technology in their personal life, and then they go into the workplace and there's-

>> Salesforce....

Anshul Gupta

>> there's Salesforce, all this manual work. There's such a dichotomy. And they know that there's better out there and that's why there's a lot of demand in this space.

>> Well, congratulations on the venture. Love the vision. Love the mission. I think you guys are right on the wave, it's perfect. Again, these old barriers are either going to be abstracted away or broken down, silos. So, the old way of doing things... And we see it with media like earn media, own media, it's all one thing. That's all digital.

Anshul Gupta

>> It's all digital.

>> And so, what are you guys going to do? You got to get that B round, so you got to put some numbers on the board. So, what's your focus right now?

Anshul Gupta

>> I think our focus is we're in scale mode. We're hiring for every position and we are really proud of having a really strong talent bar and team. Many of our engineers were former founders or aspiring founders, and so they really take that ownership in building product. We have world-class go-to-market talent as well. And so, our view is let's build the best team, continue to build products, continue to leverage that first-mover advantage of always pushing the envelope on new features, new ideas, new ROI, and continue to scale the business.

>> And so, thanks for joining us on theCUBE and the NYSE Wired network. It's awesome to have you on.

Anshul Gupta

>> Thanks so much.

>> And certainly, we'll see you in Palo Alto for sure.

Anshul Gupta

>> Absolutely. Thank you so much.

>> Congratulations on the series A.

Anshul Gupta

>> Cheers.

>> I'm John Furrier. I'm your host of theCUBE. We're here in NYSE, it's our local position on the East Coast. Of course, you see a lot more activity going on here as we build out this subnet, connect that to Silicon Valley. Of course, a lot of network's coming together. Join us for more coverage after this short break.