On-demand Webinar

Navigate Regulatory Changes and Improve Contract Compliance with AI

In order to adapt to shifting regulatory changes, organizations need to move swiftly and accurately. Legal, legal operations, and compliance teams must constantly identify any gaps within their contracts to remain compliant, knowing that the business can face fines and penalties if they fail to do so. Hear from Jeff Marple, Director of Digital Transformation at KP Labs, and Memme Onwudiwe, EVP Legal and Business Intelligence at Evisort, as they discuss how to:

Effectively navigate new and shifting regulations including GDPR, CCPA, and more.

Automatically track key data to show value to business leaders

Effectively support top legal departments as a legal operations consultant

On-demand Webinar

Navigate Regulatory Changes and Improve Contract Compliance with AI

In order to adapt to shifting regulatory changes, organizations need to move swiftly and accurately. Legal, legal operations, and compliance teams must constantly identify any gaps within their contracts to remain compliant, knowing that the business can face fines and penalties if they fail to do so. Hear from Jeff Marple, Director of Digital Transformation at KP Labs, and Memme Onwudiwe, EVP Legal and Business Intelligence at Evisort, as they discuss how to:

Effectively navigate new and shifting regulations including GDPR, CCPA, and more.

Automatically track key data to show value to business leaders

Effectively support top legal departments as a legal operations consultant

Memme Onwudiwe  (00:00):

Thank you everyone for joining us today. Really excited. This is going to be an incredibly interactive session for those of you who joined us on webinars, and conversations like this before. You should know, and so feel free at any point in time to ask questions. There's a Q&A kind of section, and we will definitely address those as they come up. There's also a chat if you want to give us any feedback directly, but it's not a one-way street, it's a two-way street. We're going to be asking things of you as well. And so before we even do intros, I'd like to get intros from all of you. And so we just kicked off a poll, and so there's a lot of folks in the room. We have a pretty wide ranging conversation today that touches lots of different business units. And so it would be helpful if we had an idea of who we had with us, just so we can kind of tailor our conversation to that perspective. Interesting results we're getting back. Jeff, what do you think about them?

Jeff Marple (00:57):

I can't see them just yet.

Memme Onwudiwe  (01:03):

Oh, no worries. Oh, yeah, here they are. Here they are. So, yeah, compliance looks like at 10%, looks like legal and contract management, both about 30%, and about 20% of folks in legal operations. So, actually not too many privacy, and risk management folks here today looks like it's more of a general legal operations, and contract management tinge. And I think that's excellent because frankly, I mean something we see and say all the time is that when you look at data privacy, GDPR, and mediation, when you think about M&As, when you think about force majeure during COVID, those can look all different use cases, different issues that require different solutions, but at the end of the day, there's just one problem, and solution that needs solving. It's the fact that your company is signing thousands of agreements, and only keeping track of a limited amount of data about them.


And so when there's a requirement to know an additional thing about those contracts, you have to scramble, and have fire drills. And that can be of course solved if you're actually managing your contracts in a way that is keeping track of the obligations that are both owed to your organization, and that you owe your counterparties. And we're going to be diving into that kind of impact today. So, thank you folks for that information. That's really going to help us kind of tailor today's [inaudible 00:02:26] Hey, let's go into intro-ing ourselves now that you guys have provided us that same favor. Awesome. I'll go ahead, and kick off. My name's man Memme Onwudiwe I'm part of the founding team of Evisort. We are an AI company that has full suite of CLM solutions from the pre signature when you want to request a contract all the way through execution, all the way through storing it subsequently. The only difference is with most tools, you either rely on the information people type into an intake form at the beginning of that process as your data.


Which of course those contracts get negotiated so that data becomes outdated, and wrong pretty quickly, or you require your teams to literally retype all the information once that contract's done, just so you can memorize stuff afterwards. And with Evisort, we've actually out of the Harvard Innovation Lab, back when I was a law student is when we started, I've been developing AI with data scientists from MIT that can automatically structure the data in contracts. And so really excited to talk about how that can greatly improve the effectiveness of legal teams as they address compliance issues. Jeff? Over [inaudible 00:03:40].

Jeff Marple (03:40):

Yeah, thanks, Memme. Yeah, yeah. My name's Jeff Marple. I'm the director of digital transformation here at a company called KP Labs. KP Labs is a legal technology service provider we've partnered very closely with Evisort, [inaudible 00:03:53] and my work focus is mostly on what we call contract intelligence practice. We help customers, Evisort customers extract maximum value from the platform, and we accelerate the value that they can extract using the platform. We do that in a variety of ways. Everything from sort of simple basic implementation services to project work to sort of mad science-y behind the scenes middleware API craziness where we make systems talk to each other, and commingle data from different systems in order to really give that contract data even more value.


I like to think we're, besides maybe Memme, maybe some of the... We use Evisort quite a bit. All day every day. I'm pretty much in Evisort, so we know the ins and outs, we know what it's really good at, and can help folks. We're happy to teach people to fish, or do the fishing for them, and sometimes we do a little bit of both, so that's what we do.

Memme Onwudiwe  (05:03):

Awesome. Excellent. Yeah, no, our partnership has been excellent. Excited to show folks some of what we've been cooking up. [inaudible 00:05:12] Awesome. And so we're going to touch upon different things today. Number one, we're going to look at the changing regulatory environment that we're living under. That discussion's going to be a bit of on the focus from a privacy, and GDPR perspective, but as everyone on this call knows, the regulatory infrastructure that we're working under is in the constant flux. And so there's a constant need to be investigating your contracts to make sure that you're in compliance with an ever-changing regulatory landscape. And we'll talk about some really important milestones that are coming up. But I think even if you're struggling with other kinds of regulatory issues, it will still be an interesting conversation.


And the next up we're going to talk about the challenges that happen under the legacy tools of contract management. Those are contract management tools. They're effectively repositories that will allow on your teams to enter the information either via an intake form before the contract is drafted when a salesperson says, "This is what I want my contract to be about." And of course that doesn't capture all the changes that happen negotiation, or tools that require you to track information in your contract manually kind of afterwards, after you've signed the contract, you need to put that information in the track moving forward. Or AI tools that don't work out of the box, meaning they might say they have AI, but once you get it, you have to spend months if not a year using your own data to train it. You don't train Google when you use it. And so you shouldn't have to train your AI, and your contract management system, it should just work. And so we're going to talk about those differences there.


And then from there we're going to go, there's some of our favorite topics, which is contract intelligence, which is the next level of contract management that really opens up all these different kind of perspectives, and dynamics that you can have when it comes to managing contracts. It can take you from being reactive, like, "Oh, no, there's a sanctions regime. Oh no, there's a GDPR change that..." Actually being proactive, and being like, "Hey, finance, did you know we have this in our contracts?" Or we can actually go and renegotiate that in the next two months, right? Because we have these kinds of analytics that we're driving off of. And then lastly, assuming we have time, which we should, we're going to dive into a demo of the Evisort platform so you can kind of see it in action.


Let me say one thing, though. What you're going to see in this Evisort demo is going to knock your socks off. But I don't want you to be impressed. I don't want you to be impressed until you actually come to a demo with Evisort, bring a document we've never seen before, and watch us analyze it in seconds. Do not send it ahead of time, because, once again, these are tools, and solutions that actually construct the data of your contracts live. And for you to evaluate that it's not good to watch a demo from a vendor on documents that we've chosen. You should actually go live, actually bring documents to a vendor they haven't seen before, listen to the capabilities that they say that they can offer, and see if they can actually do them live.


Because all you do is receive contracts you've never seen before. And so you should make sure that any solution that you have, works not just on your templates because you know what's in your templates. It should work on contracts that you've never seen before as well. And so we are going to do a demo, it is going to be exciting, but please hold your excitement until you actually see it work on your own documents. And that's for us, or any other vendor in the space that offers AI services. Anything to add here Jeff?

Jeff Marple (08:50):

No, you did a great job. Just one thing to echo. We're focusing on compliance today, compliance privacy, [inaudible 00:08:57] but the techniques we use, whether it's compliance privacy, whether it's a sales related question, whether it's a procurement related question, wherever these sort of questions of the documents come from, it's kind of a similar process all the way around. So, we're going to tease that a couple of common compliance issues, and privacy issues that we're seeing today. But just know that if you can think about this in the abstract, that if there's data in those contracts that you think exists in those contracts, these same steps can be used in the same technology can be used to answer those different questions. I say that because of all the sort legal ops, and legal folks that we have in the room as well as well as our compliance professionals. So, yeah, there you go.

Memme Onwudiwe  (09:38):

Awesome. Excellent. Well, then, yeah, let's go ahead, and dive in. And so here, and like we said, we are going to take a bit of a tinge from a data privacy perspective, but let's just look at kind of the regulatory environment going on right now. 80% of companies have updated the general privacy policies, and the multiple times in the last year, which that's a lot of companies. And when you look at specific regulations like GDPR, the recent trends, updates that updated the SCC standard contractual clauses, those have a deadline by December 27th to be implemented. And then you also look at January 1st, 2023 is when companies are going to be required to be compliant with the CCPA and CPRA updates, which are basically California's data privacy laws. But it's not just those two. I mean if you look in the United States alone, California, Colorado and Virginia have already enacted their own data privacy laws.


You got states like Massachusetts, New York, North Carolina, countries like South Korea, and India that will also have their own requirements from data privacy perspectives, which just means that teams are going to need to have a deep understanding of all of their data privacy agreements, and even information, and agreements like MSA's addendums also that might touch on some of this stuff. And so we're really going to talk about how every one of these should not be treated as a different use case, as a different project. There's a different kind of deep dive or outsourced kind of headache, but how by actually having visibility into all your agreements when these issues, and new regulations come up, it can be simple to adjust, but before diving in, once again, we always want to make sure that we're kind of keeping our finger on the pulse of our audience here. What are you guys concerned about?


And we just have GDPR, CCPA, and other here. Those are the two big ones everyone's kind of talking about. It's own globe right now, right? But if there is something else that's really kind of scratching your itch back there, feel free to click other, and even go into the chat, and let us know maybe some of these other regulations that you're worried about data privacy or otherwise, frankly. Really fascinating results coming in here. I mean looks about 62% of folks good majority, healthy majority say in GDPR is the regulation they're most concerned about with CCPA, and other kind of middling around the same, about 15, 20% with a slight win to other. But I think overall the big shark here seems to be GDPR, which makes sense. It's all over Europe, that's a big place, and looks like the others that are in there are pretty interesting. What do you think about those results, Jeff? Is that what you're expecting?

Jeff Marple (12:39):

Well, I mean, based on our work, the work that we're doing, most of the work in this particular arena has been around GDPR. So, that actually doesn't surprise me with a handful of others coming in here, and there. But yeah, spent a lot of time looking for SCC's, and contracts on Evisort. So, yeah, that doesn't surprise me at all actually, to be honest with you.

Memme Onwudiwe  (12:58):

Yeah, well I mean the demo we're going to be doing is going to kind of highlight GDPR things, so at least it shows we don't need to make any adjustments there. Cool. Let's go the next slide, and dive in a bit deeper.

Jeff Marple (13:11):

Yeah, sure thing. So this is just highlighting those two areas that we put up in the poll, GDPR and CCPA, CCPRA. What, I think, the takeaway on this slide is that, and I spoke to this earlier, is you have sort a similar process regardless of what it is you're looking for. So, the first thing you're probably doing is going, "Okay, where are all the holes in my paper?" So you can use Evisort, too, and it's artificial intelligence engine to very quickly, much quicker than you might think. Understand exactly which contracts you need to pay a little closer attention to, potentially remediate draft changes to it. And that's the next step. So, I've identified the set of contracts, and that I need to repaper and now I'm going to repaper them. So, you're turning what I would call those nouns into verbs, right?


I've harvested data, and then now I need to act on it. So I go from theory to operations very quickly, and then finally there's a stop the bleeding step always, which is how do I keep this from continuing to happen? So, does that mean I'm putting protocols in place so that we're no longer using this language? Most likely a lot of you have already done that, but then as other potentially third party paper comes in, maybe not on your templates, you can leverage the alerting system within Evisort, and all of the intelligence that you've just built in to make sure when those new guys come in that you need to repaper those. You could even potentially use it on drafts as they come in. Maybe you have a draft folder that you're putting these into, and using Evisort to very quickly analyze those things.


The other piece I think that's really important is, and the compliance professionals in the audience will understand this, is that this is the flavor of the day, and there's new flavors ahead that we'd have no idea what they are. And so there's just going to be future regulations coming, and I mentioned a few of them that are already sort of in the pipe. There's probably something else that's dealing with maybe not data transfer but something else, who knows? Who knows what it'll be, right? And that'll be another fire drill and Evisort's here to help you out with that. We like to think of it as like, do you want to build a house with a hammer, or do you want to use a nail gun? And Evisort is the nail gun in this situation, if it wasn't obvious, that it really allows you to fly through some of this work without burning outside council dollars, or making your analysts cry as they sift through thousands of documents on their desk. It really takes some of the pressure off you.

Memme Onwudiwe  (15:51):

I mean what you just described, and you did it excellently, and eloquently having a step by step focus, what you just described is a kind of mediatory project that I think folks have likely been knee deep, and if not will soon to be knee deep in. Do want to, and we come in high, and fast with the polls, I know, but this is important for us to know. Have you folks done a remediation project like this in the past? I mean there's a lot that goes into it. It's not just saying like, "Oh, show me all my GDPR", I do that. You have to know which of your vendors are going to be affected by this. And then you need to find all the agreements with those vendors that might have language that might need to be remediated. And then you need to identify, okay, which ones are actually already good to go?


Which ones maybe have non-standard language? They negotiated it, but even though it's not something that's standards for us, we still can go on with this. We don't need to renegotiate. And then also, as Jeff was explaining, identifying, hey, these ones, they're non-standard, and it also would put us at risk if we were to keep them in place. And so we do need to take steps to actually remediate them. It's a rough 25, 75, actually. With 25% of folks saying yes, and 75% of folks saying no. I mean, frankly, that's good for us because if you haven't done it yet, that means we're going to show you how to do it. And frankly, when it comes to using advanced tools like AI, advice that I always give is don't clean your house before the maid comes, right? And so I'm happy that you guys are probably at a stage where you might need to do that first step of actually identifying who's under these kind of needs, who needs to be remediated, and how to remediate because this can technology accelerate that.

Jeff Marple (17:54):

100% agree. Evisort can allow you to get your house in order along the way. That's kind of a fringe benefit of a project like this, is that you're going to be able to look at your contracts in a way that you never have before, and really understand how they're organized, and whether they should be organized differently, and the way you want to think about that organization.

Memme Onwudiwe  (18:20):

And moving on to the next slide, here we're really talking about some of the struggles that folks have when they're managing these kinds of regulatory changes with traditional approaches. And this is important to talk about because of going to that last poll, about 70% of folks here haven't done a remediation like this. First things first. Auditing those documents, and reporting on them is typically costly, and inefficient because these are a lot of documents. If you have a document management system, you're probably tracking five to 10 data points in them. Very unlikely one of those data points had to do with Privacy Shield, or had to do with the status of the standard contractual clauses in every single DPA that you signed. And so sure you've got all those contracts, that's great, but actually going in them, and knowing what's in your tens of thousands of agreements can be costly if you have not been doing the work upfront manually.


And so that's a typical issue. Also, when we say that folks are under-prepared to respond to data privacy threats, I think a big place that comes up is in situations where there are potential data breaches. And God forbid that happens to anyone on this call, but in the event of that, a typical response is to find out what contractual obligations you have to your different counterparties, or even vendors sometimes to provide them notice that you did have a data breach. And some of these kind of notice times are actually regulated in some of these different legislations that we're talking about. And so some of the legislations will say, "Hey, you have to notify your county parties in the event of a data breach within 48 hours." And so that's also on the side of mediating your contracts. But just for you to know, unless that's, once again, unless that's a data point that you've been keeping track in time memoriam, then when a situation like that happens, it's going to be another scramble.


It's going to be another quote, unquote use case where you might have to bring an LPO, or kind of dive in from that perspective. And if you have this kind of technology and strong partners KPL to be able to address these in much more systematic manners. And then, thirdly, and not to overemphasize this as we went through a list of the litany of different, the web of data privacy regulations that are currently being built globally. But I mean the one thing, there's three things inevitable, right? Death, taxes, and I guess changes to international data privacy, schema. And so these things will continue to happen. And once again, even though we are focusing a bit on the data privacy side today, this really goes down to everything that requires you to go back, and look at your contracts, be it international sanctions regimes, be it there's a supply chain issue and you need to understand, hey, which of my clients can I partially ship to versus not ship to so we can optimize for revenue?


All of these situations have the core issue that there's kind of a natural tendency for companies to sign all these contracts, only keep track of five or six things, and then when you need any of the other 90 things in there, you got to scramble. And then lastly, a fragmented process. And so I hope a lot of you folks at least have some sort of contract management system today that allows you to at least have a place where you feel like this is the home for contracts, this is where our contracts are. But if that's not the case, it can be difficult. And then also as you're going through approvals, doing remediations, and keeping everyone aligned, not having a centralized tool like that can also be difficult. So, anything to add here Jeff, before we move on?

Jeff Marple (22:17):

Yeah, no, and for the folks that don't necessarily, if they're already in the audience that don't necessarily have a CLM in capital letters, [inaudible 00:22:26] if you're storing your documents in SharePoint, or Box, or Google Drive, or what have you, it's so easy to get those contracts out of those repositories and into Evisort, so that you essentially create the foundation of a CLM just by taking that first step. It really allows you to profile your contracts, and like I said, look at them in ways that you haven't seen them before. It can be pretty enlightening. So, don't let that stop you from taking advantage of a tool like this.

Memme Onwudiwe  (22:56):

Well that's such an excellent point. I mean the thing, and I really love that you brought that up because people are so used to thinking I get all my contracts in their CLM, and then I start doing all of this work to make it effective. And the beauty of Evisort is that we don't even need a Word document. You can literally just give us thousands of scanned PDFs and literally just come the next day, you'll have all the data. We've pre-trained these algorithms. It really just, and that's why I tell people, don't trust us, test us. Just throw some documents and watch it happen. The proof is in the pudding. But that is so important because folks can have these kind of imaginary barriers that come up to leveraging technology like this. When people say, "I don't let my team use AI." It's like, "You don't let them use Google? Do they not have LinkedIns? Do they not listen to Spotify?" Like your folks use AI every day.


Your car is driving you to work. You should not be going to that work, and reading through thousands of documents just to respond to particular issues. It's 2022, folks. And so excellent point. Thank you for that. Yeah, let's definitely move on. [inaudible 00:24:03] Excellent. And so Evisort does help you future proof your contracts. So, first things first, that centralization Jeff was talking about is key because one of the hardest parts about implementing a CLM is change management. You have to tell the procurement team to stop using in Ariba, tell the sales team to stop using Salesforce, tell your marketing team to stop keeping those agreements in Box, or Google Drive. They chose Google Drive, or Box, or Salesforce for a reason. They have their own business reasons for keeping documents there, and it's tough for legal to go in there, and dictate that. And so that's why it's so important that when it comes to Evisort, you can enable those teams to continue storing documents in Box, to continue storing documents in Google Drive, or Salesforce, right?


Because that's where they feel comfortable native. But every time they add a scan document to Salesforce, or Box, or Google Drive, or SharePoint is our partners through Microsoft, that documents automatically [inaudible 00:25:08] Evisort. And once it enters Evisort, we're automatically going to track over 60 data points in them. Pre-trained out of the box. And so it allows those teams to keep doing the same thing they were doing Monday that they were doing Friday, right. No change on their side. Except now legal compliance, and legal ops has access to 60, probably 10 times the data that they had before across all of these documents in all of these different systems, and people ask them questions about their contracts, they can actually answer them. Even the ones that you know didn't feel like you were ready to answer, you can actually get through those answers rather quickly. So that centralization point really is key. As I said, the centralization point is key only because we have world class artificial intelligence that actually can extract all this data from your contracts.


Just getting them in one place doesn't do that too much if it's obtuse to actually get visibility until it's in them. And so that analysis point is just so important. I mean it's the data. Data's the new oil, you all know this, but I think it's very important to key in on that. And then next step is remediation. Like I said, Evisort is a end to end contract life cycle management system. And so while we do love to talk about the magic of pulling out data from all of your executed documents, it's going to be really important, especially in a data privacy use case such as this, to be able to generate new contracts, especially new data privacy agreements that actually adhere to the different data privacy rules that we're talking about. And so in addition to enabling you to actually identify which agreements require remediation, we also have tools that allow you to quickly go in there, and generate documents to sign with your counterparties to actually effectuate that remediation.


And then lastly, the importance of that CLM is also that when folks want to generate new contracts, they're not doing wild West style, whether they're using a template that they liked using from a couple of years ago, it really sets up a structured process so that legal, or legal operations, or even compliance only needs to adjust those templates once at a global level. And then it is ensured that moving forward all contracts coming out of that are going to be on that updated regulatory language. Meaning that once you solve these problems, they're actually solved. They're not going to keep popping up because you've got some folks out there being non-compliant and using old language. Before diving in, and just because we have been talking a lot about data, haven't talked a lot about AI, would love to know folks online, your different experiences with these different kinds of advanced tools. And so this can be a better remediation project. It can also just be about your contract management more generally, but have you folks been using AI in these processes? Yes, no, or I don't know.


Interesting. We are getting a good amount of results back. Interestingly, it seems like the biggest answer is I don't know. And that's fascinating to me. My gut feeling is, I don't know, kind of means no, but you never know, frankly, AI is such a vague term these days. You can slap it on a banana. And so I could understand how there's confusion out there as to if your tools even really use AI, because frankly, if you're a contract management system in 2022, and don't have AI on your website, your marketing team's probably going to get fired. But to actually prove out, and show that capability is a different thing. So the fact that there's so much confusion in the market that over 60% of people on this call don't even know if they're using AI in their contract management actually strikes me as somewhat true, a little disheartening, but hopefully something that can be solved in the long term. What are your thoughts on that, Jeff?

Jeff Marple (29:08):

My guess is a lot of those I don't knows are folks that haven't used the technology before, and probably rightfully so, there could be a trust factor there where they're like, "Well, are there..." [inaudible 00:29:22] False positives, and false negatives that are being going to be returned using this technology, and I totally understand that. I used to be a buyer of legal technology for a Fortune 100 company, and had those same concerns. The nice thing about Evisort really most of the folks in this space is that when you do leverage that artificial intelligence, you can still do a human check pretty quickly. Because what you're doing is, instead of if you're looking for a certain type of clause, and a huge stack of documents. In the past, you'd have to go through and open up thousands of documents, thousands of PDFs, find that clause, potentially copy that into a spreadsheet, or make a notation some way, or somehow. With Evisort, you're able to do that sort of vertically as opposed to horizontally going across the documents.


Now, you can drop all that information [inaudible 00:30:15] right into a spreadsheet, and very quickly even pivot on that information. So you can say, "Okay, well show me all the assignment clauses that are exactly the same." Okay, well here's 200 that are exactly the same. You can look at that, and now you've just quality checked 200 documents, you've quality checked the extraction on 200. How do you feel about it? My guess is you're going to feel pretty good about it that it does a real good job at pulling the information out that you need. So, the weigh that risk factor against the amount of time that it took you to get there, where that same activity might have taken you 15, 20, 30 hours, maybe more depending on how many documents you had. There's ROI there, and it starts to remediate that risk.

Memme Onwudiwe  (31:03):

No, 100%. And it is a fascinating result to a poll, but my only hope is that if we gather this group a year from now, the numbers change significantly. So, let's cross my fingers.

Jeff Marple (31:14):

Yeah, that's right. All right, I'm going to move on the next one unless you have anything else?

Memme Onwudiwe  (31:19):

No, please dive in.

Jeff Marple (31:21):

All right, so this is just a quick step by step, and this is the last thing slide, I promise, before we get into the actual demo, which is probably much more interesting than at least anything I have to say. Memme's much more interesting than I am. But these are the steps that we go through almost on any sort of extraction project when we're hired by clients to do it. And the first thing is the data shepherd, it's what we talked about earlier is getting all those contracts into Evisort. And if you're using one of the icons that we have up on the screen here, Google Drive, or Box, SharePoint, Salesforce, Slash App, that can be done relatively quickly, sometimes extremely quickly. Getting all that data in, and letting Evisort start to do its thing, which by the way, within hours you're going to start to get data back on thousands, and thousands, and thousands of documents.


The other piece that is interesting, and especially for those folks out there that sort of have a CLM today, and they maybe they've spent some time, and they have good contract hygiene, and good metadata hygiene coming from those CLMs, Evisort has the ability to take extractions from your existing CLM, and lay that data over the top of the document. So, all that data that you fought hard for to get, you're not going to lose it. And maybe you're coming from that perspective, that's sort of your source of truth, and you're thinking about maybe seizing something like Evisort as an analytics tool. You're not going to lose that. As a matter of fact, you're probably going to think about things in terms of how do we find the right documents from that perspective. In other words, a lot of our clients give us requirements in their CLM data elements, and that allows us to get after those documents in the right way and speak that language.


So, just something to think about that, that all can stay with it. So, that's for the first step. The second step is to really get rid of the noise, and find the signals. So, if you're analyzing X thousand number of documents, and maybe you're looking for some sort of privacy related provision, maybe not all of those are going to be in scope. So, are there certain types of documents that you want to look at, and certain ones that you don't? Are there certain elements of the business that we're looking in, and others we're not looking for? Are you looking for things before, or after a certain date? What have you. Those types of questions can very quickly allow us to call out the sort of static, and as soon as you understand those requirements, a couple of quick searches, and the analyzer tool, and you're going to get down to that super set, what we called the super set of data where we're going to start to look for a responsiveness within those documents.


While you're doing that, Evisort has the ability as well to create custom fields. So, if you have project related field where you're doing standard contractual clauses review, perhaps you're going to create a field, a custom field that says that this is in, or out of that set, and you can very quickly tag those. What I'm showing up here is the ability to edit documents. So, if I'm doing a privacy review status, which isn't necessarily, this could be a combination of several data points together where I consider this privacy review to be sufficient based on the contents of the document.


I can create a custom field, create that value, and then say, "Okay, move these 5,000 documents into that category", and within a minute or so they're now in that, and now you have that data. So now you're able to query that data even more... You can get even finer grained on that set of data that you've created. And that's an incredibly powerful tool. It sounds kind of pedestrian, but when you are really trying to find the needle in the needle stack, this is how you do it. Anything to add onto that Memme?

Memme Onwudiwe  (35:04):

Yeah, I mean I think it's so important, because as much as I am a futurist, and evangelist of artificial intelligence, part of the magic of the sauce is these other kind of data fields, too, that AI isn't filling, but that you're able to manually add to sift through. I really think that the beauty of Evisort isn't just the AI, I mean that is beautiful, but the fact that we're creating a data lake of all the data. The stuff that someone manually typed into Salesforce, and the advanced analysis that we did on your 500 page doc, and we pulled out all the key language.


And then you can go and do that mix and matching where you say, "Hey, we manually tagged agreements as we've been reviewing them." That like, oh, this is Josh's agreement on Josh's team. This is Sarah agreements on Sarah's team, but show me contracts with Josh and Sarah that also have a forest measure that refer to all of the different things, either quarantine, pandemic, et cetera. And it's that merging of these two different data sets into one data lake that is almost infinitely queryable, as you said, kind of laterally, and longitudinally that allows for some of these advanced use cases to be capable, so well done.

Jeff Marple (36:25):

Well, it allows that sort to... At a certain point there's potentially a human opinion, a legal, or business opinion that has to be made based on the language that you're extracting from the contract. And so Evisort does an amazing job of extracting the language that needs to be potentially have a decision made about it, or potentially it's a combination of three or four different data elements together that you want to say, "Okay, when these all equal these values, that's when I turn this to sufficient", whatever that situation is. The beauty is that you're able to capture that so that if you need to go back and look a year, a year and a half, two, three, four years later, you've got it right there, and you have that in perpetuity. So, that's a little trick that we use. I don't want to give away all of our secrets, but that's a good one.


So, now that tagged your super set, and now you're going to go through, and you're going to be looking for your relevant language, and you're seeing examples of some of the things on the screen that Evisort's able to pull out of the documents really on the fly. And so what you're seeing in the screenshot is, and Memme will show this in the demo, is that the different clauses that you're looking for are highlighted within the document itself, and you can jump to them if you're doing a document bed document review. These also allow that same language that you're seeing on screen, the highlighted language, to be dropped into an Excel sheet, a spreadsheet, married up with the contract data that goes along with it, which, allows you to hand that off to an analyst, or hand that to an attorney, or whoever needs that data. Because now you've turned the pros that's in the document into essentially you've moved it into a database, is the way to think about it. So anything to add on that, buddy?

Memme Onwudiwe  (38:17):

I will only say folks, I mean I know we're going pretty deep here, and we've got a lot of folks on the line. Do feel free to ask questions if there's anything that you want us to dig deeper on, or focus on at all. If we're saying any things that require any clarification, I know we're approaching contract remediation from a very different approach, from a data centric approach that leverages advanced artificial intelligence, and minimizes human interaction. And so if you've got any questions about that great forum to ask. You can also reach out after. Don't worry we're not shy on LinkedIn, and other forums, but do feel free to hop into the Q&A, and chat if you do have questions.

Jeff Marple (38:57):

Yeah, please. Questions are fun. I don't even listening to me talk this long, so we'd love to hear anything that anybody have to say. There we go. Okay, we're getting one right now. Okay, does Evisort work with documents uploaded, on image format? I don't think it does, actually. Is that right, Memme?

Memme Onwudiwe  (39:17):

Yeah, if it's a jpeg, we would need to take it into a PDF form first for you and that's something that we could help out with. But if you mean image, because a PDF is an image file, it is kind of an image of a document. And so if it is a scanned PDF, if you scanned it upside down, and sideways, and spill coffee on it, we are still going to be able to analyze it. And that's the kind of thing that it's great to ask, but it's even better to test. And so that's something that when we do, do live demos with people, we don't even let people buy Evisort unless they actually see the AI working on their documents live. But when you're doing that, do not just send in pristine Word document MBAs, right? Bring out some of those grimy old scanned PDFs, and actually push the system because you need to test on some of those edge cases.


You need to actually push it on some of these real time environments, especially organizations. And we work with Microsoft, and Bank of New York Mellon, which is the oldest bank in the United States. They're going to have old documents, they're going to have blurry documents, but you're still going to need data across all of them. And so even if it's a scanned document that does not have the texts pulled out, that requires optical care to recognition, we have inbuilt optical character recognition if you actually go to, and we can send this out after.


We did a case study with Adobe actually a few years back because we built on top of their optical character recognition, that's OCR, that's what turns the scan PDF document into a Word document, where not only are we turning that document into a Word document, but we can also maintain things like tables. Because some of the most important data in your documents are in those tables. And if you use an RCR that turns those tables into images, and kind of mixes up the text, you're losing some of the most important data in the contract, too. So, Nilda, that is an incredible question, and I hope we answered it well.

Jeff Marple (41:19):

Yeah, great. So, there's a couple more slides here. I'm going to go through them real quick cause we're coming up on time, but once you've identified the data, you understand the questions that you want to ask the data, these are potentially questions that are going to come up over, and over again. That was sort as the ability to save off that search criteria, so that very quickly you just hit a button, boom, here's all the latest, and greatest to fit any particular category. There's also a built in dashboarding feature, which is really kind of a visual search because it's a dynamic dashboard, sort of a bird's eye view of your contracts looking at a different way. So Memme's going to show that I'm sure, so I won't get too far into that.


But then the last piece, and which is always interesting to some folks is can you take that data, and pump it into any other sort of BI tools, whether that's Power BI, or Tableau, or Domo, or you name it. And that's of course possible as well because at the end of the day, you just turned it into a data source. Now you take that data source, and marry it up. Maybe you have your own customized dashboard around data privacy, and compliance, but maybe you are also marrying that up with an existing suite of reports that the business is viewing or legal is viewing. And you want to combine that with different parts of data from different parts of your business.

Memme Onwudiwe  (42:31):

Yeah, I'd only say that I'll definitely show some of our internal dashboards, but also the beauty of Evisort we're not creating this data and trapping it. You can export it any time in Excel. And so Jeff, I may have got a couple kind of beautiful dashboards in Power BI as well, which after the demo we can dive into, too. [inaudible 00:42:49] I do see that we have a question from our good friend anonymous attendee, and they're saying that they saw that Evisort has Adobe sign that's one of the platforms it connects to. Do we integrate with other e-signature tools? Yes, we do. The other big one, of course is DocuSign, which we do have a full robust connection with. And so you'd be able to do that. We don't currently have plans to come out with our own e-signature tool, but if we did, it'd be kind of cool if we named it e-signature at E for Evisort.


But yeah, no, we work with of course the top e-signature tools like Adobe Sign, and DocuSign. And if you don't have an e-signature tool, you also can just upload a wet sign contract. It's not going to be completely reliant on that, because once again, we're going to get the data out anyway. We're not going to allow on you to go through a format that makes it easier for someone to manually type that data. All we need is a scanned PDF of it, and we're going to pull out the data. So feel free to wet ink, too, it will not affect the quality of the data that you're getting out on the back end.

Jeff Marple (43:50):

Great. And then last but not least, you get into the, Sorry, I need to actually change the slide. To the contract life cycle management circle of fund where you're having requests for contracts, you're drafting them, redlining them, getting an internal approval, and execution, and then dropping off a repository along with amending, and reporting on them, and so forth. And this is just here to tell you that Evisort can handle all of those duties. There is a workflow aspect to it. So you can take in new requests, you can draft, you can go through negotiation period. There's an approval process that can be built to your specifications. We integrate with DocuSign or Adobe Sign to pick up that signature, then drop it back off in whatever repository you're using. Hopefully Evisort is part of that solution. And then we showed you the dashboards where you can report on those, and potentially repaper as the latest, and greatest data privacy issue comes up.

Memme Onwudiwe  (44:51):

And the only thing I'd add here is that in between that last blue one, and that first green one execution, and central repository, there is a step of additional analytics. And that's important because the only time in this process you're collecting data is that initial intake step. And maybe the sales salesperson requesting, and anyone requesting at that point might not know all the deal terms, and you see those other steps include negotiation. And so if you're relying on the data that is just typed in the initial intake step to actually be the data that you're running your organization off from a reporting perspective moving forward, that opens up a lot of risk from all of the potential changes that happen to negotiations and redlining.

Jeff Marple (45:33):

Right. All right, Memme, I'm going to hand the screen share off to you my friend, okay?

Memme Onwudiwe  (45:39):

Cool. Sounds like a plan.

Jeff Marple (45:40):

All right.

Memme Onwudiwe  (45:43):

Excellent. [inaudible 00:45:45] Can everyone see my screen? Ah, you can't talk. Okay. Awesome. So, [inaudible 00:45:50] thank you very much. What everyone's here is seeing is Evisort. As you can see, we also can sync with multiple repositories. So if you had a bunch of documents in SharePoint, in One Drive, in Google Drive, in Box, and Dropbox, Salesforce, Ariba, when they're poured into Evisort as those other teams store documents, they automatically enter Evisort, and we're analyzing them. I do want to dive in a little bit to this analyzer feature Jeff was talking about, because for a lot of the use cases we're talking about today, this is really where the magic's going to happen. And so here is how you can take all the data we're generating from a metadata perspective. You can also leverage the folders they're in based off the clauses that we've tracked in them or even based off regular search.


Or for those of you familiar with Westlaw, and Lexus Nexus, and those advanced bullion searches. Show me documents where indemnity is 10 words away from supplier. You can also dig that deep. And so once again, yes, we have the advanced AI to generate data, but you can also use advanced manual searching as well. And it's really in that nexus where the data lake really comes to life. And so let's focus on one that is pretty well known, and important right now, which is issues around Privacy Shield. And so this is both timely, and not timely. For folks who don't know, Privacy Shield is kind of one of the early big remediatory projects folks had to do from a data privacy perspective. It was basically an agreement between the United States and the EU saying, "Hey, if you have this Privacy Shield thing, then you're good for GDPR."


So people signed a lot of contracts saying, "Hey, we're Privacy Shield. Don't worry." Until a few years ago when they said, "Ah, actually, April Fools, Privacy Shield isn't good enough." And so folks then had to go, and find which agreements do we think were compliant under GDPR because of Privacy Shield, and let's go and mediate those so we can actually become compliant again. And so the reason I say it's both timely, and not timely is because obviously that happened a few years ago, but for those of you up on the very fun world of data privacy news, we'll know in the last couple weeks they've actually announced Privacy Shield 2.0. And so now there's going to be a whole other Privacy Shield that allows American companies to be compliant with GDPR. And so hey, you can call us a few years down the line when that changes again, too.


And when that does change again, too, hopefully you're going to do the process I'm about to show you instead of manually reading through tens of thousands of agreements. And so basically within our platform, we've trained an algorithm to track the Privacy Shield language, and agreements. And so you'll just be able to say Privacy Shield, and this isn't going to pop up every single time. The words Privacy Shields show up in the contract. We do have searching functions that allow you to do that. Well, this actually is an algorithm that has been trained over multiple different clauses to actually look at the meaning of the text. And so you could be working with a company called Privacy Shield USA, unless they actually have a clause that has the effect of enabling the first version of Privacy Shield in it that you want to remediate, it's not going to come up in the search and about to show you.


And so let me go, let stop talking about it, and start being about it, and actually click the yellow button. And so when we run this search, you'll see all the documents where this kind of language is found show up, and you can actually expand it to see the different clauses that have been found under it. And then we can even dive into one of these documents. So, this is Tritan MSA. So, when you look at this, you'll see that we've pulled out all this information. None of this information was manually typed. It's all being analyzed by a system, and literally you can take any of your contracts uploaded to this system, and you'll literally get data like this within seconds. Once again, actually tested out before taking any claims from here, or any vendor, but I'm telling you that's what's going to happen. And so in addition of course to the privacy information that we're going to dive into, of course we're going to highlight that a lot of this operational information's going to come out.


The renewal status, the renewal terms, the upcoming expirations, what's the breach, dislike, payment terms, et cetera. And so when you identify contracts that have these issues, you can be like, "Oh, crap, the governing laws Ireland." That's in Europe, we're going to need to look at this deeper. Oh wait, and this actually has an upcoming renewal and it automatically renews. We're going to have a decision point in the next year, or so, that will allow us to come to them and say, "Hey, we actually want to remediate this through a negotiation." And then diving into the contents of the document itself, for the folks who are asking about scan PDFs, this is what the original document looked like, it was a PDF. You can download the original PDF, or you can download the word version we made. And so this is the original scanned PDF. Of course we used optical character recognition, and that optical character recognition allowed us to turn the searchable document.


We can highlight text. Within here, you can also see where the AI is finding the different information like the title of the agreement. The title, by the way, will always be what the contract calls itself, but the contract type will just be what our AI analyzes it as. And so even if it calls itself a confidentiality agreement, et cetera, if you need all your NDAs, you'll be able to find them quickly. In addition to this different metadata, and of course we're going to show you exactly where the AI's finding this. We're not just saying it, we're showing it. We're also going to track lots of different language in here. And so the one that we were just looking at was Privacy Shield here. And you can see exactly where that comes up in the contract. Notice the clause isn't called Privacy Shield but refers to the language in there, too.


But something else to look at is here is outdated data privacy language. And so here we actually tagged the original standard contractual clauses that, as many of you guys know, have been recently changed, and set off a lot of the reason we're doing a panel today, because a lot of people are struggling with this. And so in addition to tracking things like data privacy, you can track your outdated SCCs, and then ahead of that December 22 deadline, which is coming up, you'll be able to quickly identify, and understand how to mediate these agreements. And so these are just a couple quick use cases that you can see from this perspective. I think, as Jeff showed, there's a lot of different clauses where these kinds of information can come through. But really what we want to emphasize here is that this is not just about privacy.


This is about the fact that if you sign agreements without keeping track of what your obligations are to counterparties, and what the obligations you're owed from counterparties, then you're going to have a bad time in the 2020s because these regulations are going to keep changing, is going to be lots of calamities. There's going to be lots of situations that require you to investigate your agreements to actually know those things that you've been obligated to. And so you should take a stance where you're actually capturing all this information before you need it automatically versus constantly reacting, and doing project after project that has you bogged down. And not asking higher level questions. Memme, you're asking, what higher level questions should I be asking? The job of a lawyer has been the same for the last 100 years despite the development of technology? What am I supposed to be doing with all this advanced technology? Let me give you some examples of what you can can be doing when you're not constantly fighting over, and over again with changing regulations.


And so here's some of those internal dashboards Jeff was talking about within the platform with literally turning your contracts, and the data and giving you visibility into what you're doing. So you can say, "Hey, finance, did you know that you know what? 23% of our contracts in sales have a termination for convenience that is zero days?" Which means that if we have the worst month ever that this is how much revenue we're going to lose. Great. We didn't know that either because we haven't been tracking it in every single document one by one by one, but we now know it because we have our documents in the system that structures that data. And so what that now allows us to do is say, "Hey, let's investigate these documents." This is the profile of the documents that have this kind of slow termination for convenience. And you can actually dive in, look at when they're being coming up through renewal, and other kind of information about them.


And so you can go to them and say, "Hey, yes, we have all these agreements that have this kind of risk from a termination from convenience perspective", but using the same data set, I know that half of them are actually going to come up for a renewal within the next fiscal year. And so we'll be able to, through strategic negotiation, actually reduce our risk by 50%, right? And that's something that your finance officer will never ask you to do, but they silently wish that you could. And if you too busy manually keeping Excels of upcoming dates, or trying to train a system that didn't work when you got it, you're not going to be able to actually do some of these use cases that can really drive business into illegal from a brake pedal into a gas pedal cell. I know it went a bit long there. We got two minutes left. Do you have anything to bring us home, Jeff? Oh, you're on mute.

Jeff Marple (55:22):

How about me forgetting to turn on mute? How could I top that? No, it's really interesting folks. If anything you saw here today struck a chord with you, I invite you to investigate it further, and do something that Evisort likes to call to have a sort challenge is where you bring your contracts in. This is all sort of the abstract, so it's actually your paper and then it becomes really, really quite real. And if anyone wants a sort of third party perspective on things, feel free to let me know. I'm happy to tell you more about the product, how we use it, and how we help our clients use the product. So, I guess that's kind of the only thing I have to add, Memme.

Memme Onwudiwe  (56:02):

Awesome. Well, thank you so much. It's been a pleasure hanging out with everyone for this hour. Please do come to subsequent webinars that we do, and as the chalkboard behind Jeff says, "Documents are tiny jails for data", but Evisort out here for a jail break, so don't worry.

Jeff Marple (56:22):

Take it easy everybody.

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