Episode 32: AI for Contracts with Linkquares CEO Vishal Sunak
Host Rob May interviewed Vishal Sunak, CEO and Co-Founder at Linksquares, a web application for legal and finance teams to automate the search and reporting process around company contracts, replacing current manual contract review processes that are time-consuming, inefficient, and create legal risk. Tune in to learn more about the founding story of Linksquares, how using AI in the product came about, how they talk about AI in the sales process, some exciting company updates, advice for business leaders, and much more.
Rob May, CEO and Co-Founder,
VIshal Sunak, CEO and Co-Founder,
Rob May: Hello, everybody. Welcome to the latest episode of AI at Work. I'm Rob May, the co-founder and CEO of Talla. I'm very excited because my guest today is Vishal Sunak, CEO and co-founder at LinkSquares. Vishal and I actually used to work together at Backupify, my last company. Have both gone on to start other new companies in the AI space. I'll let him tell you a little bit about what LinkSquares does and then we'll get into some of the AI around that. So Vishal, welcome.
Vishal Sunak: Thanks, Rob. Awesome to be here. LinkSquares really came out of a first hand experience we had working at Backupify and going through the process of being sold to Datto. It was pretty clear during an M&A event how important it is for companies to understand what's inside their executed agreements and largely around the integration, and also the valuation of companies to be sold kind of lends itself to like, what have you already agreed to? What do these contracts say?
Having that experience firsthand during the Backupify process, the exit there, it was incredible to kind of see that there were really no tools in the market that could help businesses in these kind of really crunch time scenarios to really understand what they've agreed to in like all their customer agreements. Given Backupify's scale at that point in time, a lot of third party paper, a lot of red lines. Trying to answer these questions that Datto had was extremely difficult with not a lot of tools in the market.
That was where the light bulb moment for us to go out and say this space is really interesting and I think this is something worth exploring. Fast forward the tape to today, companies that we work with and what they tell us is they really don't have a lot of insight into what they've agreed to in their executed contracts with their customers and their vendors. When a moment comes, which is happening all the time where they have to review them, it's really painful and manual.
That's what LinkSquares is doing, it's eliminating really manual and painful contract review and doing it through web application and powered by AI. We're excited about what we're doing.
RM: Now, tell us a little bit about when you got started, you were a little more workflow focused and AI wasn't sort of an initial part of the product. How did that come about? Was it driven by user request? Did you just see it as like this was the way to solve the problem you were trying to solve? Or how did you make that transition?
VS: I mean, getting a company off the ground, you have to get deep into the mind of a buyer, of someone who has a pain point. We had some knowledge firsthand from going through an M&A event, but I myself, I'm not a lawyer, I'm not a general counsel, I'm not a CFO. Taking hundreds of calls with them trying to understand where their pain lies, we quickly figured out that Microsoft Word will remain the standard tool of record for negotiation, doing things pre-signature, doing negotiations.
It was quickly understood that really my biggest pain lies in like after it goes to signature and the contract is now mine. I have to manage it. I have to own it. I have to live by its obligations. That's really where it was the biggest pain when we started to sum up in aggregate all these kind of conversations. Yeah, I mean, super important when you're getting a company off the ground, to take the time to really understand what are the themes and the trends.
That's ultimately where it's like, OK, it's post-signature analysis. Contract managers have been around for, I don't know, 30 years, maybe even longer than I've been alive. Largely have been focused historically without the tools that are in the market, like pre-signature. This was kind of like a new way of looking at the world, which is, we're going to help you analyze what you've already agreed to and help you with your legacy agreements, all the ones you've already agreed to historically, which has a different kind of flavor for the way that we think about solving this problem as your past and also your future as well.
RM: When you sell this or market this, how much is AI a part of the conversation? Do buyers care at all? Is it a secondary concern that you hit on? Or does it drive a big part of the sale?
VS: It's right on the forefront, I think, of what makes us really special in the market. We lens AI into a feature we call automated contract analysis. And that's really centered around the automated extraction of metadata, or structured metadata. You pick up the phone, you talk to a general counsel that are at a mid-market size tech company. Maybe they just raised their series C.
They've either come out of the pain of doing their fundraising due diligence and now they're thinking about the future. Legal teams are small, right? They're only 1% or 2% of a company's population. It's not like they scale like sales and engineering. Having those conversations, they say to us things like, well, I need all these metadata points, but I don't have the team inside the company.
We only have me as the general counsel and maybe one other person that works for me. We don't have the bandwidth to review all these contracts. And there's thousands of them. Quickly with some of the companies that are like venture backed or even PE owned, the access to structured metadata and what you've agreed to really becomes a key focus area. Then doing things like, can you get it into Salesforce? Can you enrich our Salesforce?
These are sort of the things that lend itself to saying, yeah, we can. AI is how we do it.
RM: You guys had a different technical experience maybe than a lot of the companies we had on the program, which is you initially outsourced a lot of your AI work to sort of a third party consulting firm, and then had to bring that in-house as you built your own tech team and everything else. Were there any lessons that you, were there any hiccups there? Were there any lessons that you learned that you would give to the people listening of like, hey, make sure you think about this if you're going to go that route?
VS: I think you have to have knowledge of how to work with outside developers. I think that's something that's really important. Having worked at Backupify, we had some of that experience in-house. It's definitely a challenge to communicate well and document well and really, on a technical level, understand like, this is the output I desire.
We chose a great partner here based in Boston. They were three PhDs of Brown University. We met them when they were, I think, a couple people. Now they have like 30 people who work for them. Their business is fantastic. Still use them as great consultants.
You've got to pick someone who's reputable. For us that was everything, especially because it's like Frontierland still. How I think about it is we're really inventing technology that has never really been invented. Get the best advice you can. Right? Be as detailed as you can when you communicate to them what your expectations are.
Write detailed specs even though it feels counter-intuitive in a startup lean and agile environment. Oh, we're just going to keep iterating on it, but when you're paying pretty expensive costs per hour to get this advice versus a salaried employee, you really have to make sure that everything is very nicely defined. That was a great lesson learned for sure.
RM: You do have a technical background, which a lot of people on the show don't, but are on the business side now. Again, you didn't come from AI. You came from other types of engineering. What's your advice to somebody who is a senior executive at a big company and they're kicking the tires on this AI stuff, and they're trying to really understand it, and they're trying to figure out what's real and what's hype, and they read crazy stuff in the news, and all this kind of stuff. Are there resources that you have, are there frameworks or concepts that you've had? Is there any advice you would give them for how they can get started trying to wrap their heads around it all?
VS: Back to kind of the founding story of LinkSquares is that it's really centered around what's the problem you're trying to solve and what kind of pain do you have. I mean, if you're sitting at a big Fortune 100 company and you're saying, well, we have issue with all of our legacy business agreements, or we're trying to make statistical predictions on supply chain and different use cases like that. It has to start with the problem initially. Then from there you have to work yourself back to maybe AI is not the best answer.
Maybe with AI today, getting your head around its limitations, because nothing is perfect, nothing is 100% of anything in the world, and saying, well, maybe I could get like 90% of the way there. This would actually be an amazing efficiency, even though I know there's 10% that we're going to fall short on. It's like thinking about that value. It's like, well, how big is your pain?
How expensive is your problem to go unsolved? If you think about the industry we're in with executed business agreements. I mean, these agreements are the foundation of the company with their customers and your partners and your vendors. The pain is visceral, it's there. If you think about other problems like, oh, we're trying to do some sort of statistical prediction on doing millions and millions of dollars of cost savings. Well, it's like, someone can get you 90% there.
That's an interesting way of looking at saying, maybe it's time for us to take the bite on AI. Right? Be purpose driven. Not one technology can solve every problem. Put on your engineering hat, break problems down and say, this is our problem today, and if you can define them, maybe there are tools out there in the market that can be found, that can solve your problem.
RM: I think it's a great use of an LP because what you guys are doing is much broader than a keyword search. Keyword search is going to return a whole bunch of crap that you may or may not want to dig through and it's not going to solve-- it's going to be slow from the lawyers perspective. And so when you have these things where you want to look for certain types of natural language clauses and things like that.
There's been a lot of work done in the last five years in neural information retrieval and in other kinds of NLU that can deal with some of the variability in language and kind of be like, this means the same thing, we're going to source it anyway, which is pretty cool. I think the first wave of AI was very much about prediction. You guys are playing into what I think is the next trend, which is sort of the automation trend.
It's interesting that what you're doing is you're making law teams, legal teams so much more powerful and efficient and you're giving them a chance to be more strategic. I assume nobody that you're dealing with is complaining that jobs are going away because you're automating this contract work.
VS: No. Quite the opposite. They feel more empowered to be able to do their job. We don't approach every day in talking to general counsels and legal team saying, well, you can let go three of your staff. It's more like, why don't we give three of your staff their own robot paralegal?
Make them 3 times, 10 times more efficient to do their job, so that they can actually have a better work experience rather than combing through files manually, like scrolling through scanned PDFs endlessly to try to find answers. And ultimately making themselves more efficient so that they have the opportunity to get involved in what we call higher level business strategy type of things, figuring out the next fundraise that has to be done, or dealing with a compliance issue, or dealing with litigation.
Contract review ends up taking a lot of the time. When you actually try to project like, how long will it take us to review 3,000 files one at a time looking for 10 pieces of metadata? It will take you a long time, will take you hundreds of hours. So I think it's the opposite. We've seen some incredible things, like some of our customers hiring dedicated people to run LinksSquares instances.
It's kind of like the opposite of what you're saying, it's like AI is here and we're helping a company, but also they're having job creation inside their company, inside their legal department, and pushing for these roles to exist to actually run our instance and get the value out of it that they need, which is really cool. That's been one of the coolest things we've seen.
RM: That's awesome. You guys are playing in the AI space and you're looking at companies that are adopting this kind of technology. Historically, it's paid to be a technology laggard because you could let the bugs get worked out in the latest version of Excel, Word, or whatever, and then you could implement it after the excited forward thinkers did that. Is it the same with AI? Or are companies falling behind if they're waiting on AI solutions or they're not looking at AI solutions now?
VS: I think if you're a company that has that pain I think the time is always now. And when we talk to a company that's just experienced some sort of really painful experience around, we had to review all our contracts because we had a security breach, or we had to review all of our contracts because we missed our SLA, or we're trying to raise our Series B or Series C, we have to comb through all these contracts, prepare disclosure schedules. It ends up bucketing into a category of companies that just can't wait, because they've tried to do it the ways they've been trying to do it, manually or pay their outside counsel hundreds of thousands of dollars.
Again, relating back to the pain, that's usually the moments where the best discussions happen, right? And so if you're looking at any sort of AI solution, be it like a Talla type application where the support and creation of a knowledge base is manual, inefficient, and you're spending hundreds of hours and many people are trying to keep it updated. It's like, why bother waiting if the future is here?
Even to what I was saying earlier, it's like maybe 90% will make you a lot more efficient than trying to do things manually, the old way, whatever way you were trying to do it. I think the time is now. But making good vendor selections is everything. And knowing what you're getting and partnering with a company that can deliver on, ultimately, what they promised to.
RM: When we built Backupify, one of the things that we did, we had a lot of small customers, we had 9,000 customers when we sold the company. We had a lot of really, really big customers. We were very focused on compliance and security and administrative-- enterprise things from very early in the company days. Have you taken that path at LinkSquares? Do you feel like that's paid off in terms of how you've built out the product with the kind of companies you're targeting?
VS: Security has been the fabric of the company since day zero, since the first line of code got committed. Largely based on what I learned at Backupify, how important and valuable it was to enable that incredible growth through saying things like, we're SOC 2 Type 2 compliant, we run on AWS, it's the gold standard, and you have control over your data. Those are a lot of the key themes that we talk about today in our security posture.
For a company at a relatively young scale, we've never lost a deal when a security review has to occur, a questionnaire, a phone call. And that's great, because these are the things that people care about. And so pro tip for people listening, and founders, is like, security should just be the easiest thing to get a checkbox on because there's so well documented ways to project outwardly you're doing all the right things.
It's been a big driver for us, big competitive advantage for us. Then, thinking about servicing customers at a larger scale, right? We have many publicly traded companies we have to work with. So, evolving the product to the next level, which is like full audit log of every action that every user has ever taken, and control of your own encryption keys, and single sign-on, and single tenant cloud environments for companies that don't want multi-tenancy. All these things kind of spiraled out of just talking to a lot of customers, taking a lot of at-bats on phone calls.
Then ultimately closing many of them to then understand, great, let's invest in this at a future. Because much like the Backupify story going up market, you can go up market, right? And keep adding onto it as the product matures. That's been our philosophy on the product bill.
RM: I mean, and then for founders that might be listening-- one of the guys that we work with, Ben Thomas, now has a company that does this, which is-- you call it practical assurance. If you're small, he provides your sort of infrastructure and really helps you sort of manage your security process and all that kind of stuff so that you're prepared. We use him here.
VS: My secrets out too. I use him. I mean, that's how we got started. And having a really long relationship with Ben many years now. It was a no brainer to make an investment in it because now it's paid off in huge dividends.
RM: We're just starting 2019. Any predictions or any things that you're really excited about for AI in 2019?
VS: I am always excited about the use of AI when it's not immediately evident. You started to see it in more some of the consumer apps that govern in your life every day, like even something really small like Gmail will give you three replies at the bottom. They'll tell you, “great, thanks” to someone that you're just trying to acknowledge. I think that's super fascinating.
It's like, if you think about what Gmail has achieved now with billions or trillions of emails analyzed, they can suggest that to and get it right most of the time, saving me 30 thumb clicks on my phone to say-- to acknowledge something that's commonplace that I would acknowledge back to someone. Like, great, thanks, thanks for sending that over, great job here, or like, yeah, see you later. I love that.
I mean, I think hopefully it transcends also into business applications, the way you interface with them and the way we can make things more efficient. But that subtlety of like, I think it's AI, but it's just such an elegant experience. I'm excited of seeing more applications taking that lens.
RM: It should be the-- NLP is making a lot of progress, right? I think that's part of what's exciting. I'm curious-- in your solution, you have to do, I assume, a lot of document ingestion, you have to read in a lot of different formats. Is it pure NLP driven? Do you use OCR? Do you use some kind of machine vision technology as well?
VS: We're doing all of those things. One of the biggest things we encountered early on the journey was how many PDFs that are fully executed business agreements were scanned, scanned PDFs. Printed out, signed by hand, put back on a scanner. Then ultimately converted into a locked image. OCR is a big part of what we do in the value we give to our customers. Then from there, really, we're marching towards the big vision for what LinkSquares is is that we're going to formulate the ultimate legal cloud.
The way we're going to do that is, first, we're going to collect over a million business agreements, fully executed business agreements. Using this data set, be able to do things like benchmark it all. One of the most fascinating things I think we've uncovered speaking to lawyers every day is that they care about their best performances in negotiation or the best ways they've restructured contracts.
They love to have access to say, what was that clause we used two years ago for that termination of convenience or limitation of liability? Being able to say, we've benchmarked all your contracts in your company against yourself, and then we've benchmarked it anonymously against your peers having access to that to then ultimately around predictions and recommendations is really like the future that we're getting towards and moving towards.
It gets me excited about this year collecting hundreds of thousands of more docs to ultimately start building these sort of recommendation predictions, predictive type of systems that can really help the legal counsel in ways that transcend hiring your law firm and transcend even what humans could do normally because now you're looking at millions of data points. That's why it gets me out of bed every everyday, gets me super excited about what we're doing.
RM: Well, that's been a common theme. I am an angel investor in your company and one common theme in my investments has been that I really strongly believe in this kind of model. You do a thing that allows you to get more data, which allows you to do more things. The difference between a lot of existing companies and AI companies is it's really hard to change your company DNA.
When you have an AI company and you're building it from the ground up, everybody thinks about the data you get and what you could do with it and it's just part of everybody's thought process. This is part of the reason that I think companies yours have such a good shot at really taking on even later comers that might come into this space because they don't think about it the same way. It's like when Google tried to take on Facebook.
They don't have the DNA where they think a lot about social. So, I think a lot of these vertically targeted AI companies are really going to surprise the previous generation of their companies they're going after.
VS: I think what's also interesting, especially in B2B applications, really thinking about, how do you build the wealth of that proprietary data set that you need in order to parlay that data set into technology advancements that ultimately add tons of value? It's really fascinating because as a founding team, Chris Combs and I, we're just two guys who love sales. I love sales ops and he loves selling. That's really how the company got started.
Then ultimately that's still the philosophy today because being sales first enables us to collect all these dots. It may be slightly counter-intuitive to what people think about AI companies, oh, you have to be a brainiac, you have to be a PHD. I don't have any of those as a founding team. Being sales first has actually enabled us to go further a lot faster and saying, we can add amazing value today. And then the other benefit is we get access to all these docs and we can ultimately then use them in aggregate to build incredible technology. I think that's something that maybe a lot of people I talk to in the AI space don't think about it that way, but it kind of ties back to, how proprietary is that data set?
If it's like Redfin listings or Zillow or it's MLS data, it's like, OK, that's publicly available, but how proprietary is that data set you're trying to get access to? So ultimately having a really great approach on our go to market side will continue to enable that to occur, but pretty hard to do otherwise.
RM: Then on the data, this is supposed to post, you guys are going to have recently announced some pretty big news?
VS: Yes. We've raised a little bit of money to continue on the journey. LinkSquares. In total would be about $4 million around. Led by Hyperplane and Mass Mutual Ventures, and then with participation with one of the biggest law firms in the world. So largely we're going to be spending this year tuning up our product, making it better.
We've already hired a ton of engineers now to come join us on this journey, including building our own data science team internally. PS, we're hiring. But yeah, we're so fortunate to have Hyperplane and Mass Mutual on board and really connect with both of them about how exciting this opportunity is for us.
Well, congratulations. And Hyperplane is entirely AI focused. It is their main thesis. And they were-- the first firm that I'm aware of that actually did that.
VS: Yeah. Investor in Talla as well.
RM: Investor in Talla as well. Yeah. Vivjan was very forward thinking in what he wanted to do. So cool. Vishal Sunak, thanks for coming on. And if people are interested in finding out more about what you guys do, what's the best URL?
VS: Yes. So it's LinkedSquares.com.
RM: All right. If there are guests you'd like to see on the podcast, if there's questions you'd like us to ask, the guests that we have on, please send those to podcast@Talla.com. And we will see you all next week. Thanks for listening.