Name: AI on the Inside™ – A Simpler, Smarter Agiloft
Uploaded: 2025-07-10T17:03:19.422Z
Duration: 54 min 16 s
Description: AI on the Inside™ – A Simpler, Smarter Agiloft

Transcript for "AI on the Inside™ – A Simpler, Smarter Agiloft": Hello, everyone. Thank you so much for joining. We are going to, give just a few minutes for people to log on here, and then we'll get started. Looks like we still have a few people joining. So if you're just joining us, we're just giving it a few minutes, and then we're gonna get going here. Alrighty. It looks like people are still trickling in, but we'll go ahead and get started. Good morning or good afternoon depending on who, where you're joining us from today. My name is Anne Marie Pollitt, and I work, on the product marketing team here at Agiloft. And we are so excited to have you, and dive deep into AI on the inside. So for those of you who joined us in May, we focused, specifically on the Agiloft and screens integration, and we're gonna take a little bit deeper dive today, talk beyond screens, what you'll see with AI on the inside, and then go into a little bit what, our new pricing and packaging plan looks like. So We've got a lot of exciting stuff for you today. So before we get started, just a few housekeeping things. Everyone who is joining us today will receive a recording of this webinar, so do not fret there. You will get a copy. And then also at any point, today, if you have any questions about what you're seeing, what you're hearing, please do not hesitate to drop that in the q and a. We will have time to go through those and answer the questions at the end of the presentation today. So please do not hesitate to let us know. Alright. So today, I am very, happy to be joined by, two of our tops here at, Agiloft. We have Andy Wishart, our chief product officer, who, you know, continues to captain and steer this ship in the right direction, as we're growing. So I'm very excited to be joined by him. He's going to go through a lot of wonderful things. And then Jack Davis. For a lot of you, this might be your first time meeting Jack. He is the vice president of account management here at Agiloft, and that is a new role. And he's gonna talk to you a little bit more about what that role means, and what his plans are here at Agiloft, but we are extremely excited to have him on the team. And, you know, obviously, speaking here today with customers, we just wanna continue to show our investment in you, as an organization, and so we're extremely excited to have Jack here, with us. Alright. So before we get into, the meat and potatoes of our presentation today, we are going to, run a few polls. So if you go to your polls tab over there, next to the chat and q and a, you can see our first question. So how would you rate your understanding of how AI is being used in CLM today? Very knowledgeable, somewhat knowledgeable, limit understanding, or not familiar at all. And don't be shy. If you're not familiar, please let us know. That's that's why we're here today. We're gonna help, get you all educated. Alright. And let's see if we can get the results of that poll. Maybe? Oh, interesting. Don't see the poll. Okay. Good to know. Alright. I see I'm seeing the question again, but not the results. Maybe we can come back to the polls. Alright. Seems to be having a little hang up on the polls here. So let's keep going. We're going to move it. Keep, pushing forward here. And what we're gonna do is kind of start with Andy, and he's going to not only show you what AI on the inside means, but take you back through our journey and kind of remind you how we got here and what this means for you all as Agiloft customers. So it's been an exciting journey, and we can't wait to take a look back. Andy? Excellent. Thanks, Anne Marie. That's a shame about the polls. I like a poll. I'd like to, and I know we had a couple more questions coming up, so it'd be great if we could potentially get back to that at the end. Welcome, everyone. I'm always I always feel really grateful about getting an opportunity to present to our customer base. And I'm I'm very thankful that you've taken the time out of your day to learn a little bit more about what we mean by AI on the inside. I've got quite a lot of things to show, so there will be, a decent chunk of this, which is going to be demo based. But I thought first, it would be good to set the scene and maybe start by taking a look back, at Agiloft's recent AI journey. And if I go back to summer of last year, that's when we launched the Agiloft font lab, and that sat alongside our existing AI powered, contract analysis, contract review capabilities, and our Ask AI capabilities. Now we were one of the first CLM vendors to deliver this type of no code approach to generative AI, and that's enabled our customers to automate contract and workflows and and to really, automate those workflows in very innovative ways through passing of data into large language models, getting results back, and using that to drive processes within Agiloft. And I've I've got a customer example that I want to share with you a little bit later on. But central to our thesis back in summer of last year was this concept of AI your way. And we believe that AI should be expert led that, puts you, the customer, and the user in control, and it's flexible in ways to help solve unique problems to your organization. Then if we fast forward a little bit to January, we, announced the acquisition of screens, and that was very, very exciting moment for us in Agiloft history. Our first acquisition, we brought onboard a, very innovative team that have been taking a transformative approach to, AI powered contract review. And screens is enabling our customers to create a standards based approach to contract review by creating playbooks that reflect the principles and the concepts of how they, as an organization, should contract. And and and those principles are then defined within the screens playbooks in in the form of standards. Now some of you may have seen the screens solution when we did our last webinar back in April. But, if you haven't, I got a refresher for you coming up as well within this presentation. Shortly on the back of that announcement of the acquisition, we launched screens integrated into the Agiloft CLM platform, and that's to ensure that data that is derived from running those playbooks in Microsoft Word across your contract documents during the review negotiation stage, that that data flows into your repository, into your core Agiloft platform, and enables you to report on that data, but also enables you to trigger workflows and approvals based on that data as well. Now, also in April, we announced an entirely new approach, to how customers access AI within our solutions. Now we believe fundamentally that AI is is core to the value proposition, of contract life cycle management. The AI plays a fundamental role going forward, in the automation of workflows and processes within CLM, that it's not, an add on that you might mature to go onto. That it's actually part and parcel of the fabric of what CLM, is made up of. And that's why we made the decision to AI on the inside to create a simpler, AI enhanced package for how you purchase Agiloft. So, we're we're going to go into a lot more detail. In fact, Jack's going to cover that in the second part of the webinar as to what that looks like. Now since the announcement in April of AI on the inside, we started the work of transitioning some of our customers to that new packaging that has AI XR. And today, it's all about giving you an overview of what AI on the inside means and a walk through of those capabilities as well. Okay. So let's start, maybe taking a a sort of bigger picture view of what we mean by AI on the in on the inside. I think it's that belief that AI can enhance almost every step in the contracting workflow from initiation of the contract request, triaging of the contract request, to contract review, negotiation, the approval steps, and and also post signature, activities such as interrogation of the data that is held within your contract repository, interrogation of the contracts that exist within your repository as well and and, and the analysis of that data across, different themes and different different concepts. I think the other point I would make is that, really, AI is a force multiplier that can help transform your contracting processes to help ensure that you're delivering value against your, company's business objectives and your business initiatives, whether that's, you know, support in revenue growth, whether that is optimizing supplier management or optimizing spend with suppliers, maybe having greater visibility over, some of the discounts that exist within your contracts or rebates within your contracts and and having greater visibility in management of the contractual risks that are within those contracts as well. And, lastly, again, just to drill over the point, like I mentioned earlier, we're by making this easier for you to leverage AI capabilities in Agiloft by including, those capabilities in our new subscription packages. Okay. So what are the capabilities themselves? What's included? Three core use cases, that AI on the inside within that core Agiloft package is contract analysis, review and negotiation, and Ask AI. So for contract analysis, that's all about enriching the data that you hold for the contracts within your repository. Now contracts that have gone through, the creation via template or collected data throughout the workflow, you're going to have a rich set of contractual data related to those contracts. But there may be contracts that preexisted your new improved processes on Agiloft, or you may have gone through, perhaps, an acquisition of, another entity, and you've got another set of contracts that, you need to get into your CLN and you need to review. Or you're reviewing third party contracts as part of a process where, you know, as you receive those contracts, you've maybe not got the same rich type of data as you would have if you were generating or creating those contracts based on your template. So contract analysis uses AI to turn those unstructured documents into structured and actionable data. The second pillar, review and negotiation, is where screens comes in. So the screens acquisition that we made in January offers a new approach to reviewing contracts during the negotiation process and offers surgical redlining capabilities that will align the language in those contracts with the standards that you define within your playbook. So review and negotiation are real central use case where generative AI is really transforming the way in which that's done, within contracting and CLM processes. The third aspect of AI on the inside is Ask AI, which allows users that have access to the contract records to use the Ask AI chat experience to answer ask and get answers to open ended questions. So those contracts are being reviewed by generative AI large language models to support that natural language chat experience. Now we're gonna see all three of these in action in a moment. But let me first talk about, additional AI offerings that, do still sit outside of the core package, and that is PromptLab and batch analysis. So PromptLab is our generative AI generative AI powered no code capabilities where customers can build prompts and, that is going to work on data within your contracts or your contract documents to produce outcomes that then trigger further workflows in your processes. So that could be used in a very open ended and generic way of performing analysis of a contract request to help determine who that contract needs to go to for, approval. Now PropLab is an add on, still an add on as part of the packaging, and we will see PropLab in action, as well shortly. The other aspect is with our AI capabilities, around contract review or contract analysis, you're able to perform those on single documents or collections of documents within the user interface, driven by the user. But if you've got a very large batch processing to be done perhaps as part of your initial journey into CLM, that batch analysis is part of a an additional add on to the core package. We'll come on, and we'll we'll see an example of that in the demo as well. Let's take a moment to think about the impact of what our AI capabilities have had on some of our customers. We recently published a case study with Rush, and where Rush, described the the impact that work that they've done around Agiloft's PromptLab had dramatically improved their process and times around contracts. That, contracts were, on average, taken seventy days with their manual workflow, and they've been able to reduce that by 75% down to eighteen days by introducing, AI powered workflows from the contract request that would enable, that contract to determine what additional compliance checks were needed that were determining who needed to review that contract request. So, by automating that process has resulted in a dramatic reduction in contract turnaround times and end to end contract processing times. But, also, more importantly, by building an AI powered process that users wanted to use, more contract requests were coming through the CLM. Whereas prior, people were maybe contracting and not getting that under, that information stored within the CLM. So there was a significant reduction in untracked spend by delivering an AI powered process within the Agiloft CLM platform. It encouraged people to use it. That higher level of adoption meant greater compliance when it came to the contracting processes. Okay. Let's take a closer look at the capabilities of AI on the inside, and I'm going to start with the contract analysis, that turn in of unstructured contracts into structured actionable data within Agiloft. So let's bring up Agiloft. This is a live demo, so keep that in mind. Let's bring up Agiloft here. I'm logged in. You'll see me up in the top right as Andy. I'm on the contracts table. I'm in my repository. I'm gonna open this contract here. It's a SAS agreement with a fictitious company called Kraftwerk. There is no data about this contract. It's just been routed through to me from a requester. The only thing I got, as part of that request was, a, the contract document itself and, a comment that I can see in my notifications area. Just following up on this request. Can you please take a look at the underlying contract? Now we want those contracts in this case, it's a third party agreement. We want to collect rich information, rich data, rich clause tracking like we do with first party contracts that are being generated from Agiloft templates. So let's see how we do that. Let me come into the attachments tab. I'll open up the contract record into doc viewer. This is a 17 page SAS agreement. I only see the basic information that was included within the contract request itself. I'm gonna come to the tags option on the ribbon here, and I'm going to click on analyze contract. Now that's going to send that contract to AI to identify data points or metadata. Sometimes we call them key terms in the contract as well as clauses within the contract. Now that takes around about fifty seconds for a contract of this size. Whilst we wait for that, let me talk a little bit about what's going on behind the scenes. If I open up a new tab here with Agiloft and go into the Bento menu and click on AI platform, I'm an admin in this knowledge base, and look at label models, I'll see a list of published models. These are AI models that have been published into my Agiloft KB. We push them from our centralized list of, Agiloft models. They are of either type clause or either type key term, key term, something like an effective date. There are 95 of these, labels that will identify individual things within your contracts. Now if you want to learn more about those 95 and they, we're publishing new ones frequently. If you come into Agiloft Wiki and go into the administrator guide and scroll down to artificial intelligence and click on AI analysis labels, you'll see the list of nine 95, extractors that we're building that that sort of structured information from. Let's come back into Agiloft. We'll be done with the analysis now. So we can see from that analysis that it has picked up information like the parties of the agreement. There's party one, party two. There is the initial term clause, but also the initial term duration. So you see the indication of the clause labels, which are the yellow ones, and the, sorry, the data points, which are the yellow ones and the blue ones, which are the clause. And if I scroll down, we'll, find that we also identify things like the force majeure clause and also inside that, the individual force majeure events. Now this data can then be mapped into your contract record. If we just close that and do a refresh here of this record, we will see on the attachments tab that all of the that clause information is now populated within the contract clauses. So contract analysis is about creating that rich experience, creating that rich dataset from unstructured degree unstructured documents, particularly useful where the origin of those contracts is third party paper. Let's stick with third party paper use case again and think about the impact that screens has on the review and analysis of, third party paper during that negotiation phase. I'm just coming back into the repository where I've got another agreement. This one is a nondisclosure agreement. And, similarly, it's made its way into my queue from a request. They're just uploading a third party agreement and saying, and kicking off that workflow for the legal team to review. So I'm coming into the attachment. I'm gonna click on the contract document itself. That's going to open up Microsoft Word, which is doing on another screen. I'll bring it over in a second. Here we are. So you should see that now, and I'm going to open the screens word add in. So screens is our our the recent acquisition, it's, the screens word add in. It's integrated into Agiloft. When I sign in, I sign in with my Agiloft credentials, which I'm gonna grab from over on this screen. And if you're new to screens, the, recent acquisition that we made that is now powering our automated contract review solution, The thing to think about first is that screens applies a playbook, a playbook of principles or concepts or what we often call standards that is then assessed against the contract that you're reviewing. Now I can either create my own playbook for my organization, for my team, or for a specific contract type, But I've also got access to a library of community playbooks, and, that library contains around 35 playbooks that have been created by subject matter experts in the industry. I'm gonna use one from subject matter expert I know well, which is Laura Frederick, the, founder and CEO of How to Contract. She has a screen, a screen playbook within the screens community. And I can see information, about the purpose of this playbook and how it would be applied to my contract. So I'm gonna select run that screen. That's going to do an assessment of the contract against some of the principles in that playbook. Now a principle in the playbook may be something simple like the governing law should either be Delaware or New York, or it could be something more sophisticated like the, there should be a specific carve out in limitation of liability, for breaches of confidentiality. So those are principles that are defined within the screens playbook that are then assessed and applied, by the AI. They drive the AI to do an assessment of that contract and determine whether those principles or standards pass or fail. So we can see in the, results, there are some that pass. Right? The purpose of the NDA should be clearly defined. That is the case here. There might be another one, like, there is a restriction on the use. Like, it should be clear that, it should be only associated with the purpose of, the use of the confidentiality information. Now there may be standards that fail, like in Laura's case, within her community standards, our community playbook, she believes the principle of having just two parties as part of a confidentiality agreement is best practice. This one has three, so that fails. Now this is really rich information about the compliance of these contracts against your principles and your standards. Let's take a look at a couple of others where there have been failures. So no non solicitation. The standard or the principle within this particular playbook says, actually, NDAs should be silent on, nonsolicitation. There shouldn't be a nonsolicitation, clause. Now in the case where this one fails, we can make use of the red lines that were proposed by the screens, by by Agiloft solution. And that red line is going to do something pretty simple, which is it's going to delete that entire clause. So there is an example of a very sort of simple redlining where, that clause doesn't meet the standard. The red line is then going to delete the entire clause, and it will create a AI powered comment from the definition within that playbook as well. Let's look at another slightly more complex example that requires more surgical, insertions or deletions into the agreement. The standard here is that the NDA should be no more than three years. That fails. There is further down let's just see, perpetual trade secret, another failure that the NDA, that the confidential obligations in the NDA should survive in perpetuity, with regards to trade secrets. So, a red line will be offered for this one. The screens by Agiloft solution provides some reasoning that, when that guidance within the standard was applied by AI on the the the document. We provide reasoning back to the user as to why that was the case. When I open up red lines here, I'll actually be notified that for this particular clause, there are two failed standards. The one that is going to make the change from four to three years, but also the other one that is going to make the change, to ensure that those trade secrets survive, are kept confidential, in perpetuity. Now in addition to those individual red lines, it is possible to resolve all fails with one click. There we go. Four further red lines were made, including the one at the top, which indicates it should only be two parties to the NDA. It's done an automated red line of that. And I can review and step through those, four red lines, in turn so that I I'm clear as to which parts of the document have been modified as part of that automated contract review. Now there's rich information here. I'm gonna close this document now. That's gonna save the contract back into Agiloft. That rich data about the passing and failing of, those standards makes its way into Agiloft against that contract record. So let's try and refresh this. Actually, am I in the right one? What's happening here? I am not seeing something that I was expecting. Let me try that again. Okay. I'm not seeing the screens data, which, obviously, I was five minutes when I tested this all out just before the webinar. But we'll maybe come back to that in a moment. But, what you will see let's just try one more time. Nope. Okay. What you will see is a tab within the contract record that contains the information, the the data from that screening. So the standards that have passed, the standards that have failed. You may have seen in the word add in the, standard score. Like so that standard score is created when, that standard score is created when the playbook is applied to the document. It's a measure of how, it's a measure of how it aligns to the standards. So, a score of 100 is perfect. Everything aligns to the standards within that playbook. A score that is much lower, indicates that there have been failures within that analysis as well. Now we can take all of that information and aggregate that information into dashboards within Agiloft. So here we've created an NDA central dashboard that is a look into all of the NDAs that are, in progress, including the one that we just opened, the Acme, Acme core one. And but if I scroll further down, I can see the data from that screening within the automated contract review. I can see that information within, aggregated here to indicate which standards are failing most frequently when we run that NDA playbook against the contract. So we can see that the a breach of, notification, that standard is failing frequently in the contracts that have been reviewed by screens. So that rich information from, analyzing those contracts against playbooks is integrated into Agiloft to enable us to aggregate, but also to take fail, failed data around standards to trigger additional workflows downstream. Let's look at a third thing. And that third thing is Ask AI. Now with Ask AI, we we can access that from within the document viewer. So if I open up the Kraftwerk SaaS agreement, again, that we were looking at earlier, into doc viewer, let's open the task pane a little bit further and click on Ask AI here. This is where I can ask free form questions as a user that has access to this document. I can ask free form questions, where the AI is going to answer, based on those questions. So I may have a question like, can the supplier increase prices during the initial term? I put that into the chat, and we'll get an answer back from SKI. At at the date, the supplier can increase prices during the initial term, but they're capped at CPI. And we provide a reference to the part of the document where we've, determined the the answer, where the AI has determined the answer. Let's take a look at another example. Is there a higher liability cap for reach of confidential information? No. There isn't a cap. Maybe another example, can the contract be assigned without consent in the event of a sale? So you get this sense. You should get the impression of the Ask AI feature being a chat natural language type experience. I'm pretty certain most of you will be familiar with this type of interface with regards to generative AI. But it's able to ask questions that are answered from the content within the four corners of the contract. So those are the three core capabilities that are part of the new packaging, the contract analysis for turning structured unstructured documents into structured actionable data. The contract review capability is powered by Agiloft screens for, during that presignature process of reviewing contracts against the principles within your playbooks and and, enabling the AI to turn failures into passes through those automated red lines. And then thirdly, the Ask AI service that we see here that enables users to ask unstructured natural language questions about their contracts. So all three of those things are a core part of the new Agiloft, packages. I'm gonna pause there and, transition back to the deck now, and, and I'm gonna bring Jack on. And Jack's going to talk about, in a little bit more detail, the, details of the new core edition. So, Jack. Andy, thank you so much. And to the broader audience, it's a pleasure to be here. I'm excited, number one, for Agiloft and where our journey continues to take us. But I'm most excited about my role and also what we're bringing to our customers as we transition from what you've known traditionally as Agiloft, obviously, you know, to, your your current k b two, the new core addition. We've made a significant investment in account management. In the last twelve months, the focus and the priority has become customer, and and that's my top priority. I I want to make sure that our customers understand that we are increasing and maximizing value from the Agiloft investment by continuing to making sure that we are bringing platform capabilities that align to your evolving business goals and use cases. And this is never more evident than what Annie has just provided today across AI on the inside. We're also providing tailored insights to help drive ROI faster and more consistently. I want to see all of our customers in their journey go from looking at their current data as a repository to really being able to leverage all of the power of the platform of Agiloft as we continue. In addition, we're driving strategic partnership and and innovation with the account management organization. We are acting as trusted advisors. We want to bring the customer priorities and align those with our Agiloft off road map, and that is not more evident than in today's presentation. I'm really hungry to facilitate access to product experts and new feature briefings with all of our customers, both on today's call as well as across the globe. And we're ensuring operational success and continuity by helping improve license utilization and workflow efficiency by simplifying what we're doing today and in the future. I wanna support renewal readiness and expansion planning by having consistent meetings, cadence calls with our with our customers, and we're learning so much more customers on where we want Agiloft to go in the future in your partnership. And that, in turn, is driving higher customer satisfaction and loyalty amongst our customers. That being said, I wanted to share a few quick snippets of information that we're hearing from our customers, and this is all around these new enhancements. Remi from SQ mentions process times got between 3040% quicker, And we're more consistent. Even if team members haven't worked with a particular client, they can open up the relevant playbook and review the contract as if they have all of that expertise with them of previous experience, and this is specific to our screens integration. And lastly, oh my gosh. I just used screens to evaluate high level risk with a new supplier just to estimate how long it may take to negotiate an MSA. Fifteen minutes. Amazing. Think about the time that is currently spent when you are evaluating title rewards with a new, supplier. It is a time consuming task. Love this feedback from our customers, and we just wanna continue to spread this great information across our customer base. So we want to make it easier to choose Agiloft. We wanna simplify. We have streamlined editions now. We've streamlined our licensing from seven different license types down to three, and we're also making our hosting options that much easier. And the way I see this and our organization sees this as we're as we reduce choices, that means we can drive faster time to value for our customer. And this simplified packaging removes friction from the buying process. I don't wanna have to have a customer go through a workshop just to understand how they should license our platform, and this new simplified process does exactly that. As Andy mentioned, AI is included by design, so now it's embedded to solve real business problems. That's the focus. Let's limit the separate add ons and tactical hurdles to make it easy to contract with Agiloft, and let's shift the focus from tools to outcomes. As we move forward, you're gonna be always up to date. New features are automatically included. And the nice part is now we are including a test environment that mirrors your production environment for all users so you can go through that evaluation process and test. One addition is gonna cover most capabilities, and we have continuous innovation without renegotiation as we move forward. So for our customer, it's all about clarity and confidence. I want you to have fewer options and make it easier for clearer pricing. Let's simplify the packaging and reduce confusion, and let's make it easier for you to scale as your business grows. That to me is my top priority and our top priority in the account management organization. So are you ready for a next gen CLM? What we have to do is connect you with your account management team or and or your customer success manager to discuss next steps. That evaluation goes through the understanding as to how you currently license and how we can simplify that process for you moving forward. In addition, as I have conversations on our ace events and talk to customers one on one, it's important that we help drive business value. So we have a coffee chat that's on the horizon on July 30 where we wanna help you substantiate the business case for greater adoption and expansion of the platform. This is a great opportunity to understand how we tie business value to that discussion. And if you're not a part of our community today, do me a favor. Please become a part of that online customer community. There is no greater resource in account management than our customers having one on one conversations and talking strategically about how they leverage the power of Agiloft Optum. And there's a QR code that's gonna be placed in the chat so you have access to that. On behalf of myself and Andy, thank you so much for the time today. Just a great presentation to really highlight the power of what we're doing today and in the future. And for moderating, I just wanna say thank you to our customers. I look forward to these conversations. I I look forward to meeting you face to face. So, again, thank you very much for the time today. There is gonna be a number of questions, I'm sure, in the q and a, and we will make sure that these get addressed individually. If you're not sure how to contact your account manager, not sure who that is, or your customer success manager, do me a favor. Leave me your name in the chat, and we'll make sure to get back to you one on one so we can have a great conversation. But, again, I'm excited for this role. I'm excited for account management. I'm excited about putting the customer first in our journey as we meet the next generation platform for CLM. So thank you so much on behalf of our team. Thanks, Jack. Thanks, Andy. Appreciate it. Great presentation. As said, I just I wanna emphasize too, I the reporting piece. Right? I think, from my role, I spend a lot of time, reading the news and seeing what's going on in our industry, and I think the general kind of hesitation towards AI, I'm I'm very proud to, be a part of an organization that I feel like is doing it the right way. Right? We're we're wanting to kind of take away those tedious time consuming things so you can look at those reports, make real policy change, and improve contracting as a whole for your organization. So it's super exciting. Okay. So, we do have some questions I'm gonna go through. And gentlemen, please, at any point, if you wanna grab one and answer, please do. First question, and this is gonna be kind of twofold. For those, that don't have an AI platform today, what are their first steps about getting started? And then, on the opposite side of that, for those customers that already are AI platform users, what do they need to do? Do they need to meet in order to transition to the new package? What is their what are their next steps? I'll take that. So for though and, Andy, please chime in at any time. But, for those that do not have AI currently as part of their pricing and packaging, it would require a conversation with your account manager and customer success manager so we can talk about that migration from what your current licensing looks like to the new AI inside of the core of migration. For those that already have AI as part of their packaging, you have full access to all the AI functionality that has been presented today. If there are questions, around that, please don't hesitate to reach out to me personally. I'm more than happy to make sure that we get those addressed. You will have full functionality and capabilities of AI on the inside in your current subscription. Yeah. Maybe just one more thing to add to that. That includes screens. So if you are an AI platform, you have the AI platform package today, you will have access to screens. Awesome. Okay. Andy, a contract analysis question for you. AI labeling on third party documents. Do we expect the built in Agiloft labels to extract content, or do we think, additional training will be required? It's a great question. So in the example that I demonstrated earlier, all of that data that was extracted from both clauses and metadata, was from our pretrained out of the box AI extractors, one of you you know, those 95 that I showed on the Wiki and within the AI label library. There are times when, either, that list of extractors doesn't cover the data point that you're, that you're looking for, or perhaps your documents are constructed in a way that you're not seeing the performance from those, out of the box pretrained labels. And for that, we've seen customers utilize the Agiloft PromptLab to do further analysis to, identify those data points and extract those data points. So PromptLab gives you that flexibility to, send those documents to a large language model with a prompt to be very specific, around where to find the date point that you're looking for. And we have other capabilities as well, but we're seeing more success by, filling those gaps between our pretrained and our prebuilt labels with the, Agiloft Chrome plan. Alright. What about from a security or privacy point of view? Will the AI model or training be restricted to only your KB? Will the AI training be restricted to your KB? So let's let's just be clear. With all of the AI capabilities that we're making available to our customers within Agiloft, There's no element of those models being trained on your content for the benefit of, other customers. None of that is happening. When you've got content within your KB and that is being passed to the AI engines for analysis and coming back. All of that data is your data. It's stored within your KB. There's no persistence of that data within the models. That's really important to us and presumably very important to you as well. So, your data lives within your KB. When it's processed by AI, there's no learning in that AI from your data. Another, data security question. How is the contract data, ring fenced when running it through screens? What is being tracked or stored? This is a concern for sensitive company information. Yes. So the the screen solution that is integrated into the Agiloft platform, is an AI based service. Obviously, that runs within the Agiloft infrastructure that is Agiloft code. We've acquired that company. We manage that in the same way that we manage, our whole Agiloft application. And when, you create playbooks and screens and when you run data within screens that is stored within your tenancy, your tenancy within that, screen solution that's part of the Agile infrastructure and separate from any other customer in the same way that data within your KB is separate from any other customer. Love it. This is an interesting one, maybe for you, Jack. If we're currently purchasing our license from a third party, should we be reaching out to to our Agiloft contact or with our partner? You can do either or. So I wanna make sure that you have clear communication with your Agiloft contact and, the third party. We're more than happy to get this addressed for you. Awesome. There's one here that I I actually wanna take. Is there an Agiloft AI prompt library, that can be shared across the customer community? So I just want to, take the time to shout out the work that's being currently done on the customer community. We have new dedicated resources to that environment, and I think there's gonna be a lot of exciting changes coming, in that in that vein. So please, take a look. There is a lot we're very excited to do the relaunch of of that environment. So I think part of that will eventually be, a shared prompt lab space. Andy or Jack, if you wanna, speak more to that. But similar to the way we have these community screens, we absolutely wanna be able to, share knowledge among Agiloft users and and help us all get better. Yeah. I could add to that. So, in fact, let's bring it up. I'm always keen to show a little bit more things. Here's the screens community. So this is the list of, community screens you probably saw earlier me selecting the one from Laura Frederick. So the list of 35 and growing, in fact, maybe more towards 40 now of community screens that are available within the Agiloft screen solution lives here within community. We've got plans to, in the future to be merging that within the larger Agiloft community. And I think the, the attendee that asked specifically about PromptLab, so we publish prebuilt example prompt templates into customer KBs. We've got centralized library. Through time, we would like to have, a similar approach for those prompts, as we've got here with the the community screens as well. So more more to come in that area. Awesome. So we have some more, questions in the chat, but I think as Jack mentioned, we'll follow-up directly on a one to one basis. I do want to note that everyone who attended today, everyone who registered will receive an email tomorrow with a recording of this session. So don't fret if you wanna, share it out amongst your teams. You'll have access tomorrow. And, yes, please come join our coffee chat. I think we're going to explicitly write discuss AI and how, making a business case for that because as you can see the changes it'll make, we wanna help you all make that case and and it kind of explain that value back to the broader organization. So we're looking forward to see you then. Thank you all again for joining us today. Andy, Jack, appreciate your time. You bet. Thanks everyone.