211: On this episode of Sales Bluebird, Abhi Sharma shares their experience in building Relyance, a privacy platform that uses machine learning and natural language processing to extract rules and apply them against operational reality. They discuss the impact of privacy on the world, his journey in building Relyance, and how the platform allows for quick delivery and provides value to every team. Abhi also shares his pivotal experiences building a startup at University and working on a project for ALS patients. Tune in to learn more about their go-to-market approach, the AHA moment in their deployment, and the importance of privacy by design.
[00:04:09] Passion project spawns successful startup career
[00:06:06] "Pivotal Moments Lead Entrepreneur to Life's Work"
[00:08:11] "Revolutionizing Accessibility: Eyeball Joystick Controls for ALS Patients"
[00:10:09] "Pizza Conversation Leads to Privacy Platform Success"
[00:17:55] Maximizing Data Security: Beyond DLP Scanning Solutions
[00:21:24] "Privacy Challenges and Cross-Functional Solutions Spotlighted"
[00:23:36] "Visualize Your Data Flow in Minutes: Relyance Platform"
[00:29:33] Unforgettable Deployment: The Magic of Lemonade Moments.
[00:32:35] "Scaling a startup: Timing is everything"
[00:36:39] Partnerships Likely to Dominate Privacy Solutions Market
Abhi Sharma on LinkedIn
Andrew Monaghan [00:00:00]:
Doing these interviews with the Innovation Sandbox finalists for RSA next week is really a lot of fun for me. Get to meet people who are doing really cool things at an exciting time for their company and for themselves. And this interview this morning is no different. Abby Sharma is the co founder and co CEO of Relyance AI. And in our chat, he talks about two college projects that he on that were absolutely pivotal in his continued success as an entrepreneurial tech world. He talks about the genesis over a pizza of Relyance AI, their first customer win, and when he knew it was time to start building a sales function as well as a whole lot more. So don't go away. Welcome to the Sales Bluebird podcast, where we help cybersecurity startups grow sales faster. I am your host, Andrew Monaghan. Our guest today is Abhi Sharma, co founder and co CEO at Relyance AI. Abhi. Welcome to Sales Bluebird.
Abhi Sharma [00:01:11]:
Thanks for having me, Andrew.
Andrew Monaghan [00:01:12]:
I'm looking forward to our conversation, Abhi, because Relyance was selected as a finalist in the Innovation Sandbox competition at RSA next week, we're actually recording this the week before RSA and this episode will go out, I think, live on Friday or Saturday. So exciting times for your company and what you're doing. Probably a little bit validation and a little bit excitement at the same time. So I'm keen to hear about the innovations that you're doing, how you're approaching what you're doing differently to everyone else and hearing what's going on at Relyance right now. So it's going to be a super interesting conversation for me. A quick break to say that this episode is sponsored by It Harvest. With over 3200 vendors in cybersecurity, it is hard to keep track of all the latest developments, as well as research and analyze categories and subcategories within cybersecurity, which is where the It Harvest cybersecurity platform comes in. Want to know which subcategories in cloud security are growing the fastest? You'll get it in a few clicks. Want to know and track everything about your main competitors and keep up with their hiring and news? Simple search to be done. Want to know the top 20 fastest growing companies based out of Israel?
Abhi Sharma [00:02:27]:
Andrew Monaghan [00:02:28]:
Just a couple of clicks to get that. It. Harvest is the first and only research platform dedicated to cybersecurity and it's run by Richard Stiennon, who has done it all in cybersecurity from the VP of Research at Gartner, a CMO at a cybersecurity vendor, a Lecturer on Cybersecurity, advisor to startups, advisory board member at Startups and a main board member as well. The whole lot. Find out more by going to Salesbluebird.com Research. That's salesbluebird.com research. Now back to the episode. All right, well, let's look a little bit at your LinkedIn resume, Abhi. I'm going to try and do some summarizing here. So what was interesting was you founded a company while you were at Carnegie Mellon. It sounded like you found a contact Kontect at Carnegie Mellon. When you left Carnegie Mellon, you joined at Dynamics at a time when I think things were just kicking off at D, right? They were just in that scaling mode. And you're a platform engineer there. And then you co founded an early team at Foghorn Systems where you spent a little bit of time and then fast forwarding to May 2020 officially on LinkedIn. Anyway, you co founded Relyance AI. Did I get that about right?
Abhi Sharma [00:03:55]:
Yeah, that's perfect. That's the perfect terminology.
Andrew Monaghan [00:03:58]:
And starting a startup or founding a startup at university, that's not what people usually do. I know my university days were spent a little bit differently than that. Tell us about contact.
Abhi Sharma [00:04:09]:
Yeah, so Context was sort of like a passion project of mine. When I was doing I was at Carnegie Mellon at grad school, and I had this big dilemma of like, am I going to stay back for a PhD or do I do my grad master's degree and just go work in the industry? And I was always conflicted by that. And I was working with this professor who actually came up with the original Force distributed file system back in the day. And I was a big fan. I was in this big dilemma, and he was like, why don't you just try building something and go through the motion of you have all these passion projects and you'll get a sense for where your heart is. I was like, okay, that's a great idea. So I started sort of building this little project. And in order to work on that little startup, what I did is I hacked the independent study, and I said, well, I'm going to get five credits for an independent study, that this professor is going to be my advisor, so I don't get to get two classes, and I'm just going to build this thing I want to build. And that was kind of the journey there. And I went through I even recruited another engineer to do an independent study as sort of one of the employees and built like a little indoor navigation utility that will give you pop up ads where you are inside location using Bluetooth Beacons. It's kind of a cool thing back then and took a course at Tepper School of Business where I had a VC in Pittsburgh saying, do you want to work on this as a full project? And eventually I became a startup in the thing and I could just run with it and people used it at Lowe's. And it was an interesting journey and kind of my first taste of what I would call actually building something that other people can use. And I don't think I've ever gone back after that.
Andrew Monaghan [00:05:57]:
How important was that experience to giving you the confidence and the understanding to go and do Reliance?
Abhi Sharma [00:06:06]:
I think it was very instrumental. It was very pivotal for me, and I think it was pivotal in three sense. Number one, I grew up really poor and so there was this drive and wanted to do something and I think it was a sense of validation that I could build something and other people can use and drive value out of it. Second, it clarified for me that I wanted to work in the industry instead of taking the academic route. So that was like another mind clearing moment for me. And the third thing was just kind of giving you this little boost or confidence that there is just immense amount of joy. And just a quick side, I worked on another project there for ALS, the Lou Gherigs Disease. And back then there was the Ice Bucket Challenge, if you remember. And we worked on a project very closely with a family where you could eventually communicate with your family just with your eyeballs apart from context. And as part of that journey. And we literally lived through one person who went through the entire cycle and unfortunately passed away, but they could communicate with their family just by their eyes, with a tablet mounted on a wheelchair and their bed. And I got 500 emails after context in that project on how it could have been life changing. And that feeling I felt I was in my front of my laptop. I say my brother died of ALS, but if he had this app, we could have communicated in this in this way. And I was just like sitting in front of my laptop reading an email, crying and I was like, that's it, that's what I'm going to do all my life. And I think the context plus that project gave me the right experience of that, the emotional juice to build a startup and then sort of the commercial angles and all the other things you have to operations that you have to worry about. And then since then it was being like a bug in my head. I don't think I can do anything else, to be honest with you. I'll be pretty bad at it.
Andrew Monaghan [00:08:01]:
Well, we talk about doing something that gives you meaning and purpose. That sounds incredible experience that you had there.
Abhi Sharma [00:08:07]:
Yeah, I was incredibly lucky.
Andrew Monaghan [00:08:09]:
And what happened to that project then?
Abhi Sharma [00:08:11]:
So that project actually became a big phenomenon in Pittsburgh. We were on local TV and news and radio and then we took the app and open sourced it and then the robotics division of Carnegie Mellon actually took that project and other people and students still continue to build on it. So like the next reincarnation that I remember was some people took the source code and then mounted it on a tablet and then were basically allowing joystick like controls with your eyes for people with Lugarix disease so they could be on a wheelchair and still move around a little bit just in a small area. And the whole concept of the idea was they have a little tiny laser mounted on top of a tablet. It will track your eyeballs and then you train it for like five minutes because people with ALS can eventually only move their eyes when the disease really progresses. All the involuntary functions are still on, let's put it that way. And so once you train it, you can look at like a little box which says up arrow key and if you keep looking at it, the chair would move a little forward. And so what we had done was just like, you look at different boxes and you say, somebody's texting on WhatsApp? And your group could say, andrew had a great day at soccer today. And you can look at a box which says thumbs up and you just look at it for 3 seconds, they'll send a message and then that way you could be in touch with your family even though you're completely bedridden and can't move any part of your body and your kids can feel connected. So yeah, we just open source the project and it's still somewhere in the code base of some project that some other students are working on hopefully.
Andrew Monaghan [00:09:47]:
Yeah, I hope so. That's an incredible achievement and incredible meaningful project as well. Must give you a lot of satisfaction looking back at that one. I can tell the passion that you bring to them for chant to it, but it right now. Let's fast forward a little bit. Abhi. So tell me about that pizza that you had with your co founder at the end of 2019.
Abhi Sharma [00:10:09]:
Yeah, so this is a fun story. It was late 2019 and my previous startup was getting acquired. I just hiked Kilimanjaro in Africa and come back and we were just catching up. So my co founder Leila and I were friends in a personal life before we started the company and I was like at that time in this headspace of like, okay, what am I going to do next? And I was academically researching this whole idea of progress study. I won't bore you with the details, but I was obsessed with working on a problem at the intersection of two different domains. I was in sort of that headspace and I met Leila for pizza just to talk about the hike. And Leila had spent like 15 years as an operator, as a lawyer, building privacy programs for dozens of industry verticals and she obviously hated all the tooling that existed. I was already in the headspace of designing some privacy platform that will better serve the industry's needs. And so we met for pizza and that pizza conversation became like an eight hour lunch followed by multiple stops at different coffee shops just discussing the impact of privacy on the world. How do you protect personal data, the intersection of AI and law and you're already seeing the craziness happen, like regulators have no idea how to regulate GPD or other things. And we actually talked about this like late 2019 over pizza in December. And it just felt like a meaningful, mission driven project that would have impact across the world. Everybody needs to care about this. And nobody had applied compiler and observability and machine learning principles to this industry. It seemed like a very top down data scanning type of solution. And so we had the perfect combination of an operator who is my co founder and then myself, who's coming from a different experience and lens and we just combined and mushed our brains together to build Reliance and haven't looked back since. It's been a little shy of three years. We have been operational, we've grown phenomenally fast and I think that hypothesis with which we started the company really resonated. So it was like the perfect combination of burata cheese on pizza and the most unusual combination of a privacy law and a compiler nerd just like jamming together to build something.
Andrew Monaghan [00:12:36]:
Sometimes it's the strangest combinations that make the biggest impact. Right, well, let's talk about Relyance then. What problem are you solving for a company?
Abhi Sharma [00:12:45]:
Yeah, so Relyance is a SaaS platform that's using machine learning to basically help a company build its entire privacy data governance and compliance program in a single platform. So the problem that really that we're solving for our customers is to give them two things. Number one, continuous visibility on their data processing when code is being written within the software development lifecycle. So really highlight for companies in a very visual, visceral way, how am I processing personal, sensitive and special categories of data and then continuously compare that with that organization's contractual obligations, policies and regulatory requirements. And anytime you have gaps issues, we will highlight them. And so the Land Started becomes your system of record for all of your privacy compliance needs from a compliance standpoint, governance standpoint, and also a system of intelligence for highlighting any gaps, issues or problems, but really shifted left into your business operations and developer operations rather than playing catch up or after the fact. And that's the essence of sort of what we do.
Andrew Monaghan [00:14:04]:
My impression is that what people are doing is doing assessments and asking for people to self report on what they're doing or not doing. Is that right? Is that what people are doing and it's not automated much at all?
Abhi Sharma [00:14:16]:
Absolutely. I think there are like three big macroships going on in the world. As you know, tech is becoming regulated, there's an elevated concern of privacy for everybody and then machine learning and AI is everywhere. And in light of that backdrop, what's happening is people are mostly sending surveys, forms, top down workflows and asking questions of like, hey Andrew, you're our head of data science. Did you change and build something new since we last talked three months ago? Of course you change and build like you built a million things. You push code like a million times a day. And so it's sort of like a very manual point in time information gathering on the semantics of the application. Like what data are we processing, why are we doing it? And is this within the bounds of what we commit to our customers and maybe to regulators? And the whole process and journey has been spreadsheets forms, questionnaires, maybe point of in time data scans that never quite contextualize and capture. Why are we doing this? Should we be doing this and do it in real time, if you will. And so reliant just flips all that approach on its head and we're like, okay, we are going to be embedded in your code infrastructure, ML pipeline, sales and marketing operations and then reflect back what's happening in real time rather than you just asking your marketing team, did you stitch some tools together? Are you sending emails to more leads? Of course they're doing that instead of playing catch up. I guess we make it easier for customers to run tandem. That's the best analogy.
Andrew Monaghan [00:15:55]:
And are they buying because they want to, I don't know, save money? Are they doing it because they know they've got some gaps and some risks or what's the main driver?
Abhi Sharma [00:16:05]:
I think it's a multitude of factors. There's definitely save money because it's a problem that is intractable if you do it manually service or farms or just throw more humans at it and it's out of date by the time you finish that exercise of data mapping, let's say, because the code has changed 20 times over. So there's just like efficiency, productivity, cost. Second, there's obviously it's the law, so you have to do it like if GDPR or CPRA applies to you, it's non optional for companies and it's also fairly sophisticated and complicated. So it's not like a small requirement you can only worry about once a year. And the third thing is the trust posture it is affecting. Bottom line right now, if you're in your sales workflow, it's like privacy has become the new soft two type two, if you will. You're selling cybersecurity products for a while. You know that if you're an enterprise SaaS platform, you don't have your soft two type two or ISO 27,001, nobody's buying you or you don't have SSO support. Like end of story, right? It doesn't matter how much your software works. And I think we are having the same moment with privacy compliance in sales and marketing workflows as well. So it's the combination of all these factors time, efficiency, the law and the complexity of all the workflows, that the alternative is ten x worse than using automation and software to solve the problem.
Andrew Monaghan [00:17:33]:
You said something interesting earlier, which was that it sounded like you can check what you're doing against your obligations from contracts with clients and customers. Did I hear that right?
Abhi Sharma [00:17:44]:
That is correct. That's absolutely correct. And also with vendors, your third parties that you use, your sub processors.
Andrew Monaghan [00:17:50]:
That must be a big step forward because I don't know how people do that right now without scanning contracts all the time.
Abhi Sharma [00:17:55]:
They don't. And in fact, that's one of the interesting things we brought to bear in the market through the platform, because there have been some data in motion DLP data address scanning solutions for privacy and security before it doesn't complete the picture. But you had some version of that, but there's nothing that actually compares to what are the constraints, right? Like you could be building an app and using Social Security numbers. That's fine. There's nothing wrong if you have a legitimate purpose for using this, assuming you're a fintech company. But then it's like, does it match what you tell your customer? Does it match with how you operate with your vendors and third parties? Does it match with, if you're a SaaS platform, your other companies, your commitments to, and nobody seems to kind of care about that, or it's done in a very slow like if you happen to tell the legal team and the security team about this, you caught an issue, otherwise you didn't. And so we just make that real time. And the funny part is people don't realize that the contractual obligation and policies, even in the world of law, there is a stack overflow going on. It's not like every lawyer is coming up and writing their first draft of a brand new policy with nothing to reference from. And data protection regulations have been specific enough that it's almost like privacy and security requirements and agreements are becoming like constraints expressed just in natural language. And then you can extract them as rules and apply it against operational reality. And that's where some of our sophisticated natural language processing and machine learning comes.
Andrew Monaghan [00:19:31]:
In within the platform and being selected for the innovation. Sandbox final, is it those ground baking moves you're making in the natural language processing Mlai? Is that the core of what the innovation is?
Abhi Sharma [00:19:44]:
I think so. And that's probably one of the reasons we got selected in the first place, because I think there are two big things fundamentally that we are bringing to market that are, I would say, differentiated by an order of magnitude. Number one, that continuous semantic analysis of the code while it's being built. And when I say semantic analysis, we mean not just like string or regular expression matching, but like a compiler understanding, okay, what are you trying to do with this code in terms of data processing and how does it affect your users, customers and vendors? That where we do a compiler like analysis with machine learning is one pillar, and the second pillar is the one we just talked about sophisticated natural language processing and contracts and policies. So you sort of have this automatic summarization of your constraints and then also we extract rules that then apply against that operational reality. So if you just checked in a low piece of code in your ETL pipeline or your main branch in GitHub, and it processes or uses data that you didn't have consent for, we'll highlight the risk. And so it just completely shifts the conversation on privacy and data protection and even security risk in the beginning of the software development lifecycle rather than at the end, which is how it's done today.
Andrew Monaghan [00:21:03]:
And as you're talking through those different examples and use cases, it springs to mind that this might involve multiple buyers inside an organization. I notice on your website you've got some video testimonials from people who I think are lawyers. So I'm kind of interested, what's your usual in who's the main people, the main group that you work with?
Abhi Sharma [00:21:24]:
Yeah, it's a multitude, and I think we are sort of split. So I feel like privacy in many organizations, I would say 60 or 70% of the times is still owned by the lawyers or the legal team, if you will. And then 40% to 30% of the time, the office of the CISA or the infosec team, and those are the two main ins into the organization. But once we have that first conversation, reliance's go to market motion is very cross functional because privacy is cross functional. So we're talking to security teams, engineering teams, and the legal teams. And we definitely have to both express and convince all of these buyers to the value of what reliance provides. And then that's when we kind of, let's say, make a successful sale in our go to market motion. And actually, I love that about reliance, because if you take this posture where, let's say we had no technology for privacy ever, okay? And everybody each organization did their own thing. Privacy is sort of the round peg in a square hole, if you will, because, yes, there are regulatory requirements, but then you can't apply them if you don't understand how your microservices are communicating with each other and what data is being processed. And lawyers don't have that view. Engineering know that, but then don't understand the privacy regulations. Same with security. And so I feel like because privacy is such a cross functional problem to begin with, a cross functional solution that takes these sort of multithreaded approach towards solving actually fits better because it provides something of value to every team that they can see and then adopt and improve their workflow. So RN is usually legal or the CSO's office, but then it's very cross functional across the engineering teams. And I'm hopeful over the next decade or so, those ratios will shift and engineering will have a more active role in both, not only adopting, but building, because privacy by design is such a valuable and topical topic for today.
Andrew Monaghan [00:23:28]:
And when you show these teams reliance for the first time, what's the wow moment? What's the thing? They go, oh, I get it now.
Abhi Sharma [00:23:36]:
I think that we have this view in the platform where we actually have a full visual like a circle of a 3d style graph of data, lineage of data flow. So what we do is we say we've never done any deployment and let's say you work my buyer, Andrew, we'll jump on a call, you have not done anything you'll go into Reliance and for ten minutes we'll click around a couple of buttons, make sure we get some service account access and you'll deploy a little container for your code analysis typically then in ten to 15 minutes and before you jump off that call, that 1st 60 minutes you've ever done anything with Reliance. A graph pops out right in front of your eyes and it starts highlighting data flows across your microservices and your systems and your source to sync with lineage which is very hard to do and that's when people go oh, I didn't know that we were sending this log from Datadoc to Zendesk oh my God, that seems weird, this PII over here or I didn't know we have these foreshadow It systems or I didn't know that this is how the lineage between my checkout service and shipping service and my Stripe API calls are and so it's sort of like people feel they have a mirror that reflects the nervous system of the data flow of their organization in an instance after they pushed a couple of buttons and I think that's the AHA moment where you visually see this and not only for the first time it brings security and lawyers inside the bubble to solve for privacy challenges but even engineering folks are like oh, that's interesting, that's actually helpful. I never saw the whole view come alive, especially in large organizations not one person actually knows how the whole system architecture works. So that's the moment that is the AHA capture of like oh, that is interesting, we've heard that reaction many times.
Andrew Monaghan [00:25:26]:
That's incredible. So let me ask more about that. So do you do that in the first call with people or the second call when in the process do you ask them to install that?
Abhi Sharma [00:25:37]:
That's happening when we actually are in the go to market motion. We have a live demo where we describe this through a dummy application we have built and we just say let's start from scratch. Here's an application that's not unlike yours, here's their code repo, here's their business processes and let's just hit a couple of buttons and the demo shows the whole thing that I just described in an instant and that's sort of the sales process. Now when the customer either decides to buy or do a pilot, that's when we do the same exercise on their system and it's that 60 minutes in the first call of actually doing a pilot or doing a deployment we showcase them the slide view. Now they might do more integrations, we might connect to more code repos eventually but that time to value in the first 30 minutes of you doing something is just magical, and it still is magical for me. After doing this for three years, I still get a kick out of it.
Andrew Monaghan [00:26:38]:
Before we go any further, let's get to know a little bit more about you. I've got a list of questions here. I'm going to ask you to pick three numbers between one and 35, and I'll read out the corresponding question.
Abhi Sharma [00:26:51]:
All right. Let's do two, nine and 13.
Andrew Monaghan [00:26:55]:
Two, nine and 13. Okay. Two. iOS or Android?
Abhi Sharma [00:26:59]:
Andrew Monaghan [00:27:01]:
Abhi Sharma [00:27:02]:
Andrew Monaghan [00:27:05]:
I think I'm the same. Ever since the first one came out, I've been iOS. Yeah, I don't see the need to change either. All right. Nine. What is one song you could listen to for the rest of your life?
Abhi Sharma [00:27:19]:
So it's a song called Valerie that I could listen forever, and it's the rendition by Amy Winehouse. I am just in love with that song and I could play it on loop forever.
Andrew Monaghan [00:27:32]:
What is it about it that captures you?
Abhi Sharma [00:27:35]:
I think, first of all, the way Amy Winehouse sung it, it's not her original song, but she had a spin on it and the vocals are beautiful in the way she just sings the song. And the other part is, I don't know, it just feels super from the heart, if you will, like the first time I heard it. And every time it's like sometimes you write a song because you're going through the process and you're being creative, and sometimes it just flows and something about that song just connects. And I think it's about a lot of emotions and this deep, unexpressed love, if you will, that's coming out through the song. And at least that's how it feels to me. And I just love it. Plus, the music and the vocals are just beautiful. So I could play that on loop forever.
Andrew Monaghan [00:28:25]:
She had such a distinct vocal style, right?
Abhi Sharma [00:28:29]:
Andrew Monaghan [00:28:30]:
I don't know. Like jazzy, rocky sort of style. It was interesting.
Abhi Sharma [00:28:34]:
Andrew Monaghan [00:28:35]:
I'll have to go and listen to Valerie then. I don't know it right now. And then the last one you chose was 13, right?
Abhi Sharma [00:28:42]:
Andrew Monaghan [00:28:43]:
Cats or dogs?
Abhi Sharma [00:28:45]:
Andrew Monaghan [00:28:45]:
Do you have a dog right now?
Abhi Sharma [00:28:47]:
I don't, but we're going to about to have one very soon. My wife has always had one throughout her life. It just bought a new place, so we're going to get into that very soon.
Andrew Monaghan [00:28:59]:
Yeah, we've had a dog for quite a while, like two or three dogs in now. And it's a lot of fun, good companions and good to get out of the house and have to walk them all the time, which is nice. Well, let's go back to another significant day in the development of Reliance Abby, which is the day you won your first real live paying customer, not a design partner or early adopter who is on and giving you a favor or whatever. But the first one that came in that you didn't really. Know much about. And they paid you real hard cash. Take us back to that day.
Abhi Sharma [00:29:33]:
Yeah, I still remember it. I don't think I will ever forget it. So the story goes something like this. We had built sort of like a beta plus version in eight or nine months. I think the vision of how the platform was going to be built was very clear on the day one when we had pizza, and it hasn't still changed to date, so I feel proud about that. But anyway, so we'd done a beta plus product. We'd spoken to this customer. They were very interested and in a lot of pain about this solution. And we said, okay, we're going to deploy. And I remember it's a call, eight in the morning. We had done enough demo deployments, but this was like the real litmus test. We were going to connect to infra monitoring. We're going to set up a code analysis job. And so we set up that deployment. It was an empty instance. We provisioned their tenant. Everything was empty. And then we had some engineers, a lawyer and a security person on the call. They deployed our container that does the static code analysis. And funny enough, at that point, our visualizer had a little bit of a lag in terms of how it drew the graph because it was the first product. But what happened was you kind of get lemonade out of lemons is it started to pop up the assets one by one, as if it was a string of lights. And dataflow started to connect. And on my end, I was like, why wouldn't it just render completely in one shot? But to the customer, it was just like as if the system was magically discovering the assets and plotting out the data flows. And they were just, like, blown away. I could see it in my eyes. I was like, almost ready to cry. I was like, this is amazing, the whole thing. And then one of the engineers said, oh, I didn't know we had this data flow. And I was like, boom, right there. I have my security person. I have my privacy lawyer. I have an engineer who just made a statement that completely validates what we do. And that's it. It's magical. That was the deployment. I won't forget it, ever.
Andrew Monaghan [00:31:33]:
I bet the whole company was pleased when they got that first order in from those guys, right?
Abhi Sharma [00:31:37]:
Yes. And in fact, the funny part is, after they saw that demo, not only they got the first order, but they negotiated price for the next two years. And that's when I knew we're headed down the right direction. Because if they didn't feel enough value, or they were uncertain about the value long term, they would not have asked for that in the contracts. And that's when you know that okay. The flywheel, even though it's their first deployment, the flywheel can turn or is starting to turn.
Andrew Monaghan [00:32:06]:
That is a great point. I think a lot of people when they're working with early stage companies, they're like, well, let's just try it for I think this could be good, but let's just try it right. And they're unwilling. Unable or cautious anyway about thinking past six months or a year. That must be very validating for you.
Abhi Sharma [00:32:24]:
It was beautiful.
Andrew Monaghan [00:32:25]:
So after that moment, at some point in your process, you started to hire a sales team. How did you know it was the right time to start bringing in salespeople?
Abhi Sharma [00:32:35]:
Yeah, that's a great question. I feel like we had done our first three or four deployments with customers. And when I say customer, these are like full blown paying customers. I'm not talking design partners, pilots. And we were able to replicate the value delivery in the same way we talked about it consistently over those first three to five deployments. So once we got about the four and five number, I feel like we were at the stage where we're like, okay, this is a giant problem. It's a very market segment, market vertical agnostic solution. And we're able to prove to ourselves that we can consistently deliver value on these three things that we built over and over again for a couple of customers. So at that point, we decided to do two things. Not only did we raise our Series A around because we had a lot of inbound interest and we were mostly like turning away investors at that time because we really wanted to validate that if I put more fuel on this fire, it's going to burn and burn bigger. And so we kind of did our Series A and then we had extra capital to expand and that's when we just started recruiting and hiring. We did it slowly but started hiring our sales team so we could get like solution engineer and Ae and just continue to scale the team. We did it slowly because you always want to make sure that you can make the Pod productive. It's very different with founder selling and then actually getting a whole sales and marketing trained up. And so that leading indicator of not only product market fit but a repeatable go to market motion was the trigger point to start to say, okay, we can start to hire in sales now. I think a lot of times you do too early or too late and it's not very effective. And Reliance is not my first startup so I've made plenty of mistakes before to realize that I would nail the timing this time.
Andrew Monaghan [00:34:27]:
Yeah, I think that was very insightful how you're approaching that tough coming in as a seller without some of that foundation in place. And obviously the worst thing is seller is trying to create or find the product market fit still and the founders kind of wants to wash their hands a little bit of anything to do with sales that's not what you were doing. So that's awesome. And it sounded like you hired you said Ae and An Se together at the start. Was that the first two hires you made?
Abhi Sharma [00:34:53]:
We hired An Se pretty early, and then Ae pretty early, too, and almost like there were a little bit of a gap between them, maybe a couple of weeks here and there. But yes, and we needed because we knew that Reliance is sort of a complicated sale. And by complicated, I don't mean the solution is easy to adopt, but you need to not only be an expert in privacy and governance, but we're doing NLP on contracts and code analysis that's not connect to an API and get some data. That's sophisticated stuff. So you needed to walk through and describe value through this multiple layers of abstraction. And so we knew that your traditional Ae would need additional help from a little bit more of a technical tilted solution engineer so they could partner together, or a sales engineer. And obviously, we still very much are involved as founders, almost in all the deals. But that was sort of the set up. And that was very intentional. So we could see if this balance between AES, you didn't need a one to one ratio, but maybe every couple of AES more Se. And it's always needed because we typically express the value. And then, of course, you do NLP, you do code analysis, you connect to our infrastructure. Well, definitely you're going to have a conversation with a technical person and they're going to want to ask more questions. And that's when it was crucial for us to scale, to hire Windows pairs, if you will.
Andrew Monaghan [00:36:21]:
One of the things that struck me as you were talking there was that traditionally, the big consulting companies, the big SIS, have made a lot of money on creating processes and systems for people to tackle privacy. How do they view what you're doing? Are they partners or foes?
Abhi Sharma [00:36:39]:
Yeah, I feel the jury is still out on it. I certainly tend to think that it will lean towards partnerships long term. And the reason for that is a lot of the consulting firms that provide solutions or services around privacy or data governance still tend to be very streamlined on processes, but very manual in terms of the things that you can do. I think when you're building a privacy program, and if you're starting from scratch, your first problem is, okay, what is my to do list in context of this organization and this application and whatever regulations applied to me and risk applied to me? And you have to be an expert to even figure out what the to do list should be. And then you have to take the to do list, and what people do is shove it down the rest of the organization to do those to do list and items, if you will, that don't always appreciate and understand it. And I think there are a lot of process enhancements systems and workflow enhancements you can do in these two phases, which is where I think the consulting firms add a ton of value in just streamlining and helping you a little bit. But the fundamental problem is the depths of the problem around privacy is so deep that even if you have a very streamlined to do list of what to do, doing it manually is just excruciatingly painful and you never get to the endpoint that you want. So unless they are spending all the time building deep technology, I don't think that they can really move the needle. So therefore, my conclusion that long term, it's probably going to be some function of partnerships as the whole market and solutions in this space mature.
Andrew Monaghan [00:38:29]:
I wonder if their view on what you're doing might have changed in the last few months. As the whole chat, GPT and GPD four, all that sort of stuff coming out, it must be causing a lot of their brains to be thinking, what does that mean for our core business if some of this seems to be so easily automated? So interesting to see how that plays out in the coming years. One last question for you on the sales side, Abby. So if there was such a thing as a magic wand that you could waive to fix one thing for your sales team overnight, what would you have it fix?
Abhi Sharma [00:39:02]:
Everybody fills out their medic sheet consistently, precisely in the most pristine fashion. It's very tactical, but it's like my biggest pet. Even if I ever have it in me to start another startup, I think that's what I'm going to build end to end, net new AI driven sales system that may or may not connect with salesforce. But I'm an engineer, I'm a process oriented person. I love to organize things and it just bothers me why it's funny. We use telemetry for everything else in the world. You're building a SaaS application, you measure your performance, you measure your uptime, you measure your latencies, you're doing fitness, you measure everything. And then suddenly when we get to go to market in sales, it's like we forget that there is also math to all this telemetry and there's a funnel and you need all these data points. And if I had a magic coin, I'll fix that right now.
Andrew Monaghan [00:40:03]:
That is the best engineers answer I've ever heard on that question. You know what's interesting about the sales side is they're measured in so many ways, right? You can go and analyze their calls, you can assess and analyze the success of their emails. You can look at the tone of the voice as they're talking to people. Are they doing certain actions compared to what it was ten years ago? You can get amazing insights into what they're actually doing. But when it comes to the salesperson still filling in something, there seems to be that disconnect like, oh, it's paperwork, it's admin, it's things like that. Well, what I would say is that what I found is there's a category of salespeople who are highly successful and are self aware enough to realize why they're highly successful and know that's an important part of it. And whilst they might like it still, they know that having the rigor of going through medic properly actually is going to help them win deals. And there are people out there that are like that, believe me.
Abhi Sharma [00:41:02]:
Yeah, no, I have seen it and we've had friends and coaches and they're very successful because they follow the regime of that rigor. And it's incredible if you just do those few steps really well, how much context and back and forth communication, you can even save it in the organization, you can qualify well and you know, all the stuff on what are the benefits of doing it well, but, yes, that's my pet peeve.
Andrew Monaghan [00:41:29]:
Yeah. You ask a salesperson, Is it needed? They'll say, of course it is. Yeah. And then you ask them, Why haven't they done it? I'll get to it. It's funny. Anyway, listen, I've really enjoyed our conversation this morning. If someone wants to get in touch with you, what's the best way to do that?
Abhi Sharma [00:41:46]:
They can email me, Abby, at reliance AI. I'm also on Twitter, abysmarma underbar B, or they can connect with me on LinkedIn.
Andrew Monaghan [00:41:55]:
Awesome. And next week is hopefully exciting and fun week for you. And Monday is the Innovation Sandbox competition itself, when you have to go on stage and present. So wish you every success for that and for the rest of RSA and the rest of the year.
Abhi Sharma [00:42:10]:
Thank you very much and we're looking forward to winning the thing.
Andrew Monaghan [00:42:14]:
Love the confidence. Thanks, Abby.
Abhi Sharma [00:42:19]:
Andrew Monaghan [00:42:21]:
I really enjoyed meeting Abby for the first time on this call and hearing him talk about his journey and what they're doing at Reliance AI. A lot of takeaways, as usual, for me. Three things to that. The first one was, I don't know if you picked up on this, but he's obviously very articulate and loves what he's doing. But there was two areas that really lit him up. One was when he was talking about the ALS project he worked on at Carnegie Mellon. You can tell that was passionate and meaningful for him. And the second one was when I asked him about the time when they won their first real live paying customer at Reliance. And you could tell it was a super impactful and exciting time for the company. So I loved how his passion came out. In both of those moments. There were things that really keyed into him and he was thoughtful about, especially the ALS one. It governed, it impacted how he went about his career after that. So big impacts and come from passionate things like that. The second thing that I took away was hiring the first salespeople. So they realized that through the first few interactions, he said four or five deployments, two things became clear. One was this was a massive problem, right. It wasn't nichey, it wasn't a small thing. They could have the confidence that if they hired salespeople, there was a lot of demand out there for them to go after. And the second thing was, he talked about even in just four or five deployments through the processes, that they had a sense of the repeatability about it as well. And showing up as the first salesperson without either of those things is challenging. Right. But especially if there's no repeatability and you're supposed to just figure it out. I think I really loved how they approached that. To say, we've got two things in our favor here now. We need to make sure that we're maximizing what we're doing. We just took some funding, so let's use some of that to build out the team. And the third thing that stood out for me and gosh, this is a common thing. Again and again, that phrase, time to value. Right? And in this case, what he was talking about, either in a pilot or in an actual implementation, within 30 minutes, the people are seeing their own live workflows, their own flows of data and where they're going and things they can never see before. And that is so important. Right. I think the standard is beginning to be set these days. The time to value needs to be in the minutes and the hours, certainly not in the months and the quarters and the years. So they've got a tool that enables them to show that so quickly. And I love how they're doing that. It's a real lesson, as we're thinking about building new tools and innovating, is how can we get that time to value down to a really short time and making sure that our prospects understand that we're able to deliver on that. So those were the three takeaways for me. Super fun conversation. Abby's a great guy, a lot of passion what he's doing, and real interest in making a big success. So I wish him and the team good luck. For next week at the Innovation Sandbox comp itself and for the rest of the year.