On-Demand Webinar: From Automation to Insight: What’s Next with Datacor and AI

-Hello, everyone. Welcome to today's webinar. I'm Grace Kirk, and I'll be your moderator. Before we get started, just a quick technical note. All attendees are currently muted. However, we highly encourage participation. Please submit your questions in the Q and A or chat box at any point during the presentation, and we will address them at the end of the webinar. Participants will also receive an email with the recording of this session as well as the presentation slides. With that, I'm very excited to welcome Sundar Kapuswami, DataCourse Chief AI Officer, and Jeff O'Brien, VP of Product Management. I hope that you enjoy today's session exploring more about Datacore's capabilities and advancements with AI. With that, Sundar, I'll turn it over to you. Thank you. Good morning, everyone. So today, Jeff and I are gonna chat through, you know, why the AI moment is now, what our commitment is, and how we can partner with you to build some of our capabilities. And then, you know, most importantly, we'll talk we'll go through some key AI capabilities, some some demos just so that you can, see the features, come to life. A quick note is, for those of you that attended our interactions conference a couple of weeks ago in Houston. Some of it might be a repetition, but some of it could be brand new as well. So just a heads up on that. K. So so why is the AI moment now? I think most of you know and you've been probably following the industry that almost everybody is into AI in some form or fashion. There's a big gap between the leaders and the laggards. The leaders are really pulling away, meaning when they lead with AI, when they are highly invested with AI in the right way, the revenue per employee generated in the business is three x versus those who don't. And then not everyone is winning. Only, you know, twenty five percent, of the folks, applying AI are seeing real returns, but, there's a big, price at the end, to capitalize on. So, when you look at something like this, I'm sure you all might have heard multiple variations of this, but the obvious question comes to, you know, how can you be in the top twenty five percent, you know, to capitalize on this. Next slide, please. So to be in the top twenty five percent, you know, the four mindsets that we see that separates the winners from the rest are one, start now. Many, organizations have a cold start problem. It's it's very big in. And, you know, the thing is, once you find the first use case, it suddenly expands and your people will find, you know, several more after that. The second one is to think big, but to start small. It's, again, rather than trying to boil the ocean, start with one workflow and then expand on it, which leads to the third mindset here, which is focus on multiple narrow wedges versus trying to go after one big moonshot. And ultimately, waiting for perfect data. You know, today large language models work pretty well with imperfect data. Not to say that data is not important, but perfection is also not needed. You can use AI and clean as you go. And this is this is just a view to say, if people are wondering, you know, how to be in the top twenty five percent, how do I get started, is just a few tips, to get going. Next slide. Yep. And at Datacore, when we think of AI, we think in terms of AI and humans because we believe your business multiplies when you use the right combination of them for the right reasons. The AI focuses on the mundane work and what's predictable, like a lot of document automation, data entries, as well as surfacing signals that you don't expect, like forecasts, anomalies, and ultimately capturing knowledge as it goes. Whereas the humans focus more on judgment, improving the AI and handling the exceptions and ultimately growing the business. So this is kind of how our AI solutions that we are building gets better over time. And you will see this theme as we go through the different capabilities that we walk through further. You'll find this common theme of the AI and the humans working together. Another big reason here is we are not using the humans as a crutch. It's a very deliberate strategy because we believe that's what helps build trust with the solutions as well as helps reinforce and improve AI. Let's go to the next slide, please. And ultimately, when we have this going, our vision is to go, you know, from friction to flow. So today, from typically using manual entries at every step of the workflow, we are transferring it where there is both automation and augmentation to get answers quickly, use AI to read documents and workflows and humans review exceptions and ultimately, you know, be ahead of the game and be proactive versus reactive. That's our vision. And I'm sharing all of this just so that you can get a sense of how our thinking is behind this. Next. So with this, we've kind of, we are investing in forty plus capabilities in every single workflow or domain within your business or your operations, as well as across all of our capabilities, ERP and CRM definitely, as well as capabilities across our LIMS, MES, and track of audit simulation products. Products. So, I'm going to the next. Yeah, so you will find a QR code that comes up later where yes, there you go. There you can actually scan through your phone and get a sense of what capabilities that we are building within each of these broad buckets or domains. Again, it's not every capability listed there, but you will get a good idea of what we are building. It'll be great if you can scan it and just submit to us what you're interested and not interested in. It'll give us a bit flavor. And we're also partnering with our customers to actually build it out together. So just wanna make sure you capture this note. K. Slide. So before going deep into the AI capabilities, the key point we wanna say is we are building this AI capabilities, not AI for the sake of AI, but to make sure it solves a problem that matters to you and drives value for you. And the second part is you will notice that along the way we are building it with you, and we'll explain more about that as we go along. Next. So when we think in terms of our AI capabilities, we're broadly thinking in three three big areas. AI workflows, which is any workflow, any document related workflow or non document related workflow that you're doing manually today, like reading documents, extracting information from that manually, entering it into the PRP, into the ERP, or your system of record. All of that, we are automating with the human in the loop. The second category is our data lab, which is essentially our data lake services that we will talk through. And then lastly, a set of standalone capabilities that you can use with what you have today to drive value for your business. So we'll go through examples in each of these buckets so you get a clear idea of what's coming. And starting with the AI workflows, the first use case is a procured pay automation. So today, you know, a lot of our customers go through a very manual purchase order process, just heavily manually driven today from a court to a PO, hanking, a lot of the materials, packing slips into, receipts, and then finally matching, with the invoice. This happens several times, you know, every day, every week, with our customers. So we go to the next slide. What we've built is, automating this process, you know, from any quote or a slip or an invoice straight to the ERP. So the way it works is it starts from your source, which could be, you know, a picture you take on the phone, in your warehouse or what you upload on a PC or you have attachments in your email. And the AI goes through that, understands what it is, checks it, then ultimately posts it to the ERP. So what the AI does step by step is it, first of all, tries and understands what kind of document it is. It finds details from the supplier and the order. It pulls the facts and checks checks the numbers, and ultimately, it clears for the watcher creation. And when everything works well go next. When everything goes when everything works well, the human can just hit approve. And if something doesn't work well or there's a mismatch, it's flagged for the human and they can make, updates to the system, ultimately leading to a timely and efficient posting, more accurate posting. And as your shipping volume increases, the solution scales with the business. And let's see how it works. So here's an example where there's a quote from a vendor, and you can see on the right hand side there's a plugin built into the Outlook email and when you click on that, what happens is the AI recognizes and identifies identifies that it's the supplier and it's a quote, and it suggests to create a PO. So then you process with AI. You click that button. What it does is it starts extracting each line with all the unit costs. You can see on the right hand side it's extracted everything, and all this happens within seconds. And then you click create the PO at the bottom and it's done pretty much, the moment you submit it. There you see it's correct. So this is an example, of a happy path where it looks at the attachment and sends it all the way through. The next example, is a packing list in the warehouse. And again, you click the same button at the top, which says the automation, and then here it identifies that this has already been acted upon. So there are many times our customers, you know, look through this to see and they check has it been acted upon or not and so on. But with this functionality, you don't have to waste time on it. It surfaces it already saying it's already been done, so you can move past it. Next example, here's an invoice from a vendor. You go through the same process, click the plug in, and, it identifies, process it with AI. And in this case, there was a mismatch. As you can see, the next slide. Yep. It highlights a cost variance that was detected, which was a freight surcharge, and it allows the human to go in and make a change. So what you've seen so far is AI identifies what's already been acted upon, identifies variances like this one, and ends up completing the process fully. So this is a functionality that we've built, and we're partnering with customers that are interested in using this functionality for them. So I just showed you the general workflow which covers like eighty percent. Maybe there is something unique for your business. We are happy to talk through that with you. But if you're interested in any of these, this feature or anything else that we're to show further, please reach out to us. Okay, We go to the next example, which is another workflow automation example that is automating your cache application. So cache application, you know, you get different payment types that you see on the left, EACH, chats, lockbox, credit cards, etcetera, as well as different remittance types. And today it's a very manual way of, you know, doing the remittance matching and the reconciliation and ultimately recording it in the system of record. And this leads to delayed application of cache reduced visibility, more cost and higher error and compliance risks. So this manual process we've automated and let me show you how, you know, what's under the hood on the next slide. So here the way it works is the AI classifies the type of document and understands what kind of a payment type it is. Is it a check? Is it an ACH or whatever? And then the LLMs do a very structured extraction of data from that piece of document and it does a whole bunch of standardization and validation and ultimately does the matching. So you go from a payment to posting. And just like the previous examples, when everything goes well, it shows up as green and the human just hits a submit button. And, but when not, then, the human can intervene and make a, any adjustment as needed. Let's look at an example. Next slide. So here's an example, and, we are uploading a check-in this. Next. Yeah and once you upload the check here, in the next slide you can see it extracted all the data from the check. Looks all good. The human is allowed you know, to review this and it captures all the relevant information from the check as you can see here, the company name, the check number, the amount and all that. So you get to get a chance to review it or make any changes. Then next, it checks for a matching. And if it does match, in this case, does match with high confidence, and then you confirm the match, and then it's posted to the ERP. So this is a good view of how the cache application works and, you know, what used to take several hours is down to minutes now, giving time back to you. Again, anyone interested to learn more about this or if you want to try this capability for your business please reach out. So we have many such you know workflow capabilities. You see some examples listed on the left, many such documents, but there are a lot more. So let us know on what you're interested and what matters to you, but this is just to give you an idea of the different use cases we are working on. And as we showed you before, scan the QR code that gives you a good idea of the comprehensive list that you can submit to us. Keep going. So now I'll hand it over to Jeff who will talk about our Datacore Datalab offerings. Jeff, you want to take over? Thanks, Sundar, and thank you everybody for attending today's webinar. Very excited to share what we're working on with the Datacore Data Lab. This is a data lake, data warehousing solution with analytics and addresses some of the most challenging reporting and analytics problems that our customers tend to face. So within the, Datalab offering, we're we're providing essentially three different product offerings designed to meet our customers where they're at, whether you're early in your analytics journey, whether you have an in house team capable of building dashboards and analytics, or if you're earlier in that journey and are looking for a partner like Datacore to build this for you. So the three offerings at a high level are data lake as a service, Managed Analytics, and a Professional Services offering. The Data Lake as a Service provides a fully managed cloud lakehouse with pipelines from all of the Datapore products into the data lake. We can also extend that to non Datapore products, possibly data that you may be collecting and have on a SQL Server or elsewhere. If you're leveraging, for example, HubSpot for marketing automation, and you want to bring that data into the lake, you know, Data Lake as a Service will help you do that. Managed Analytics is a set of role based dashboards, and we'll take a look at an example there. But for our managed analytics offering, we've built the initial dashboard for the CFO user persona, and we're looking to get that out to customers for feedback and partnering where we'll continue to enhance that dashboard. We'll be working on a, you know, set of analytics for the Chief Operations Officer next, but over the next few quarters into next year, we'll be rolling out additional role based dashboards for other executive personas and business domains, such as the, you know, the Chief Sales Officer, officer, head of R and D, etc. And then the professional services offering comes in and can help our customers with any number of different data and analytics challenges. So you may take a look at our managed analytics and decide that you'd like them customized a little bit to be a little more meaningful to your business. If you don't have the in house capabilities to do that on your own, our professional services team will be able to perform those changes on your behalf. If you'd like a totally custom dash board, or if you'd like to engage Datacore on maybe a little more of an ambitious AI machine learning type initiative, this is where our professional services can come in. But what I hope everybody leaves here today knowing is that regardless of where you're at in your analytics journey, we have the solutions and the team to support where you want to go. And so to give you just a little bit of a better understanding of our data lake architecture, we're basically building out the pipelines from every one of the, Datacore products into the Datacore data lake. And so we have started off with European CRM, but we'll be getting MES and warehouse management, the Datacore formulation product into the lake. We've already got a bit of a head start getting TrackAbout data into the lake. And over time, we'll also include our compliance management and LIMS offering. So we'll bring all of this data into the lake, as well as partnering with our clients to bring, you know, various external third party data feeds. So it could be, you know, market data from some reputable source, again, could be a third party product that you may utilize. We can assist you getting that into the lake. And all of this data initially lands in, you know, the bronze layer that's kind of the raw structured, unstructured, semi structured data, including documents, basically, in whatever way, you know, the data is made available to you, we get it into the lake in that form. And then our team can do the heavy lifting of transforming all of that data into the silver layer, you know, the classic data warehouse where this data has been cleansed, modeled, and optimized. If you have an in house team that's capable of building dashboards and reports using something like Tableau, Power BI, Qlik, your team will probably want to point your own BI tool at this silver layer. We're then taking it a step further and building out purpose built domain based data marts for different functional areas and executive user personas. This is highly cleansed data, very easy for your business end users to leverage the data and possibly build their own reports, customize the dashboard without necessarily having to go to IT or data core professional services. And so this is where we'll see our managed analytic offering at this gold layer. But all of this is underpinned by data lake as a service. And again, all of it is augmented with our professional services offering to help you get the answers and insights out of your data that you need to make better business decisions. So, we've built some data products in the lake already. If you are a TrackAbout customer, you may have heard about or seen our rental analytics solution. This takes all of the TrackAbout returnable container asset information and allows teams to understand how changes to their rental programs would impact revenue. So essentially giving teams the ability to, you know, to model changes, to understand how they can make changes to their program to drive strategic growth in rental revenue. Also has a natural language query engine where you can ask questions of your data in natural language, and the system will return a response to that query. And so this gives you a bit of an idea of what we can build in the data lake. In a moment, we'll also take a look at a live example. So with the live example, I'm going to show just a little bit of our CFO dashboard, and we'll explain some of the capabilities that you can expect from the managed analytics offering. We'll spend just a little bit of time, taking a look at some of the self-service capabilities as well. Self-service capabilities being maybe a little more oriented toward, you know, data teams that you may have within your business. So going to walk through our Datalab offering. First thing to know is I'm accessing this through a browser. So, you know, this is fully web based. We can extend access to the appropriate users within your business, control access to, you know, capabilities and certain dashboards based on on how you would like to secure secure the system. But for anyone that that we extend access to, you know, essentially, they'll they'll be able to sign in through the browser of their choice. And if I'm an end user that just needs to be able to access and view dashboards, you know, I'm probably going to be spending most of my time in this dashboard menu item. Right now we have our CFO dashboard listed here, but over time, you know, we'll be releasing additional standard dashboards for for those different executive user personas. But if I click in here, what we're seeing here is a CFO dashboard pointed at a real instance of the ERP. This happens to be posted, pointed at an instance that our sales team uses. So, the data is, you know, representative of what you all would have in your ERP. But this is just a, you know, a dashboard organized into multiple tabs. So we have tabs for financial performance, cash flow, AR and collections, gross margin and profitability, and some budget versus actual analysis. We have plans to add additional tabs here. So maybe a view on accounts payable, different views that would be meaningful to customers. But within any one of these dashboards, we can have multiple different widgets returning different KPIs. We can have, you know, various charts and visualizations to help you better understand the data. And one thing that is helpful to point out here is that all of this supports drill down. So if I am looking at my net revenue and trying to understand actual versus budget, also versus the prior year, I can come in here and perhaps I see something about the month of June that stands out to me. And maybe I want to inspect that a little more intently. So I can simply click on the month of June and you'll see that now that I've selected on a specific period, the numbers and visualizations elsewhere in this dashboard have updated because now we're just focused on the supporting data in the month of June. And so what I'm seeing here with gross margin percentage by product line, you know, OpEx versus budget. All of this is now being focused to transactions in the month of June. And then I can see the detail of those transactions if I want to get deeper into it and understand what makes up the number, all without having to go into the ERP. By default, all of the data that we're seeing here refreshes on a, twenty four hour basis. We can we can increase this to, once every four hours, depending on the dashboard or the KPI. And if, there are scenarios where you need more of a near real time view of the data, we can tackle that on a case by case basis to increase the cadence of getting that data into the pipeline and reflected in the analytics. Just quick through a few more of these examples here. So, you know, this view is focused on cash flow and liquidity. So we've got, you know, thirty day cash flow projection bringing in, you know, AR inflows and AP outflows. We can see the trend of days sales outstanding, days purchase outstanding as far as AR and AP collection of payments goes. We've got some additional detail, supporting there. And again, just as we saw on the previous tab, we can click on one of these sections to filter down the data and take a look at those specific periods. Perhaps my favorite tab on this dashboard is our AR and Collections tab. One of the things that we've done here with our AR Aging by Customer is we have taken a look at average days to pay by customer for the past three months. And then we've compared that with the three months prior to show a trend on days sales outstanding. And so what this can help your team understand is whether or not your customer's average days to pay is extending? Or are they remaining constant? Is the average days to pay potentially even shrinking? Why this might be important to me is, you know, I may have a customer with net thirty day terms and for years and years and years, they've paid, you know, somewhere around, you know, thirty, maybe thirty two, thirty three days. If we start seeing a trend that now that's starting to extend out, thirty seven, thirty eight days, I may want to take proactive action. Yeah, I may want to inspect and change their credit limit. I may want to reach out to understand what's going on, put them on different payment terms, but could be a signal that, you know, something's going on with that customer and that could create a risk in your business of not being able to collect. Yeah, some of the other stuff that you would expect in any BI platform, you'll find in what we're delivering here. So this is nice that I've got all of my customers, and I've got kind of the AR aging risk color coded here. You know, perhaps I really just want to take a look at all of the customers that the system has flagged as red. So, you know, I can simply click on that column and I've sorted it sorted the data by that column. Note here too, you have some other things. If you want to reorder the columns, you can do that. If you want to suppress a column from a view, you can freeze or pin a column. So pretty typical capabilities of interacting with the BI platform. One thing that I'd like to show off here too is the the natural language question and answer capability. Let me just refresh that screen here. Within this within this platform, you can ask all sorts of different questions about the data. And, you know, depending on what data is accessible to this particular dashboard will determine whether or not you'll get a response. But we've gone through and, you know, we've kind of built out the semantic layer. So your team does not necessarily need to know what we call, you know, the tables or the columns back in the system. They can just ask questions, you know, in the language that they would ask your business users. And so I could, you know, ask a question here around I'm going to ask a question for, you know, which customers are at the highest credit risk. You know, I can also probably, you know, get that answer simply by looking at the table, filtering the data in a certain way. One thing that is really nice about this platform, when you ask a question, it will read back to you how it interpreted your question. And so if you're not happy with its interpretation, you can come in here and you can change how that's been interpreted and then save it. But so, you can ask questions about your data. And if you, like the results that it has, provided, you have the ability to, you know, save this and add it to a pin board in case that's a question that you may ask on a regular basis. And so if I take a look at the pin board here, I've got a couple of things that I've added to the pin board in the past. So again, if I'm going to ask that question, once a week, once a month, etcetera, you know, I may want to say that to the pin board so I can get at it, on a regular basis. So a lot that we can do here, you know, again, just showing the additional, visualizations. So, scatter plots, the thing that's really nice about this, makes it very easy to identify outliers that your team may want to inspect. You know, we can build heat maps, waterfall charts, all sorts of different visualizations that you would expect out of a typical BI platform we can build out here and deliver to your team. I'll spend just a moment taking a look at some of the capabilities that your technical team members may find value in. So everything that we saw in the dashboards, it's, you know, built on that data lake as a service foundation, that data lake architecture that we saw with the bronze, silver, and gold tiers. If you have, you know, a data team and they want to explore the data at any of these different levels, you know, they can drill into, you know, the bronze tier, the silver tier. They can drill into the specific tables that we brought in to understand, you know, more about the data that's in the lake. So we've got a view of the schema. We can also browse that data. And so this is just returning some of the data from that table that's in the bronze layer. I've got some abilities here where, you know, maybe I want to filter down this data for some purpose. So perhaps I want to filter this down based on, you know, the product number, you know, and I can put in a product number, tell the system whether I'm looking for, you know, the exact match, anything that contains a particular value. And then once I apply that, the data will be filtered down further. One thing that, you know, I'll just point out with the catalog here, you know, bronze, silver, and gold layers, this is where all of the Datacore data lands, but, you know, if we work with you on building a pipeline to, you know, a third party product, a different, you know, data source, we can bring that into your workspace where you'll see your tables and data for those third party data sources there. So a lot of exciting stuff going on with the Datalab offering. We'll just you know, one one thing that that I'd highlight here, we're actively working with clients to launch. We would love to hear from you all what you would like to see in the managed analytics offering. If you have a challenging data problem that you're trying to solve, we'd be happy to partner with you to work on that together. And if you're trying to make the decision about what direction you want to go in from an analytics perspective, you know, trying to build it all in house, maybe working with a third party service provider, you know, we'd like you to consider working with Datacore. I think one of the things that we can do that perhaps, you know, third party wouldn't be able to do is we understand our data better than anyone else. We understand your business better than anyone else. So we should be able to get you to a place of value, you know, faster than a third party. So moving on from yes. Jeff, before you go into the third category, which is stand alone capabilities, I was looking at the questions from that came across, and I just wanna address a couple of them very quickly. I probably should have proactively said that. First one is related to data security. So we've it's it's it's super important to us for any AI workflows or use cases that you've seen so far. The data does not leave our fire our walls. It's all inside of Datacore. We use LLMs, but we do not send any of your data over to the LLMs at all. So the I just wanna make sure everybody understands that that it's all of this is done in a very in a security friendly way. Super important. That's one. Number two is we don't train the LLMs with your data at all. We use it just for classification, and we throw it out. What what we do is learning, self learning, meaning, I showed you examples where the human had to intervene and make some changes. And when those changes happen and the next time, a similar situation happens, the AI will will remember those changes and and, you know, improve upon it. So that's how the self improvement or the self learning works. And then we use a variety of LLMs. We we use the best one that that gives us the best quality and performance, you know, whether it's, the GPT models from OpenAI or Gemini from Google or Anthropoc, all of that. We use a combination of that, and we've continued to update a lot of it. Some of you might have received functionality previously from us that was using standard OCR to extract documents. That works but up to a certain limit, meaning when the format is always the same and it's a consistent format, OCR works very well. But when the format and the layout changes, OCR starts failing, which is why we are upgrading our OCR, especially with sales hours, as an example, that used to use OCR now with AI. So in case you've seen not so good performance with your previous one, just ask us and, we'll be upgrading, you know, that with the new AI version. And I think and if I if any of you are interested in any of the things I mentioned or what Jeff shared now or what we're going to continue to share now, reach out to us, use that QR code to express your interest or just email us. We will show our email IDs again towards the end of the presentation. So just wanted to quickly touch upon a few of the questions that had multiple upvotes to quickly answer right now. So now we'll go into the third category, which is the standalone capabilities. Go ahead, Jeff. Great. So, yeah, we've got a few standalone capabilities that we wanted to share with you all. And, you know, just moving right into it. First one is Datacore Compliance Management. Every one of our customers, whether you're a manufacturer or a distributor, has some number of compliance documents that you have to collect from your suppliers. And then you also have some number of compliance documents that you have to distribute to your customers. And so we've built a system that allows you to define your supplier and customer onboarding process, as well as all of the compliance documents that you have to either collect from a supplier or distribute to a customer. And we're providing a set of capabilities that moves your day to day management of these documents out of Excel and email into a fully AI enabled automated manage, manage, you know, exception, you know, dashboard user experience. So lever leveraging AI, we are able to send to your supplier an email that comes from your domain. It looks like it came from your business, a user that your supplier knows and trusts, to let the supplier know either during that initial onboarding or as compliance documents are about to expire that you need to collect, you know, a current compliance document. That document could be an ISAR certificate, a halal or kosher certificate, a certified organic statement. If you're if you need to collect sales and use tax exemption certificates from your customers, you can collect those types of documents. The way the system has been designed, you define the documents that you care about, you define, what data you want to extract and validate, from those documents. And then, you know, the AI in the system really just handles everything else. And so what we're seeing is, as suppliers are responding to the emails, they're providing the different documents that you require from them. Our system ingests the documents, classifies it to identify, is this an ISO certificate, is it a lot number explanation file, or something else? Classifies the document, extracts the data, validates it. If there's any discrepancies or the AI can't process the document, we will workflow it to the appropriate user in your business for review, action, and disposition. But then ultimately, you know, we get to a point where we've got a finalized document, it gets stored in our document control, we've captured all of the metadata. And if it's a document that will expire, Based on your rules, it could be thirty days, sixty days before that document expires, the AI will proactively reach out requesting an updated copy. It's a very similar story on the distribution side. All of the various compliance documents that you may need to provide to your customer will be providing a way to automate the distribution of those documents as you're onboarding a customer or as your own documents come up for expiration and you get the updated copy. So anyone in your company that is chasing these various documents, whether they're supplier documents or customer documents, we can use this solution. It's great for your compliance teams, purchasing teams, accounting teams. Over time, we'll also expand this for collecting and distributing various documents to employees. So if your HR teams have use cases there, we'd like to talk to them and understand what those use cases are. Next up, we'll talk a little bit about the AI powered chemical database. Our customer regulatory teams spend countless hours pulling down chemical regulatory data every time you bring a new formula to market. And so we've automated all of that research. Our AI goes out to sixteen regulatory bodies, we're adding new ones all the time, pulls down all of the chemical regulatory data on a per CAS number basis for the CAS numbers that matter to your business. And we bring all of that into a single user interface. So instead of having to potentially go to eight, ten, twelve, sixteen different websites and dig through those sites to find the data that you need. We put it all into a single user interface where you can browse that data. All of that data before we publish it for our customers to use. It also goes through a full internal review by the Datapore regulatory services team. So, you can have confidence that the data that's being published into the database is accurate and up to date. This is available this quarter in a standalone fashion, where you can just log into it through a browser. And later this year, we'll be integrating it directly into the Datapore formulation and regulatory control product. Last up here, we'll just talk a little bit about some AI vision technology that we've built out and we're putting into the TrackAbout product. So TrackAbout has had the ability to scan barcodes for the longest time. And scanning barcodes is great for automating inventory movements. But one of the challenging aspects of, getting to a place of value on Trackbot has always been registering the assets. Someone has to go out to the floor, look at every cylinder, and pull data, you know, like read the information on the cylinder, type it into TrackAbout. The user has to understand what certain symbols mean and everything else because different cylinder providers put different things, you know, their cylinders. And so that just takes a long time to register a cylinder. And if you're registering hundreds of thousands or even millions of cylinders, every second counts. And so this new AI vision technology allows the user to simply take a photo of the cylinder. Our AI extracts all of the data, validates it, and puts that into a workflow where if everything pulls and validates perfectly fine, the user can simply register cylinder. If they need to correct something on it, you know, very simple workflow for corrections. And so this takes the cylinder registration process from, potentially taking, several minutes all the way down to seconds. And this same vision technology can be applied elsewhere as well. And so one of the things that we're hoping to hear from customers is, where would you like to see this technology applied elsewhere? You know, are there scenarios where your team needs to take a picture of an asset or something happening on out on the shop floor? Is there a use case where you would want to, simply be able to take a photo and have Data Guard drive the downstream processing? So this is just a, you know, a sample of the ERP agnostic standalone capabilities that we're building. There's plenty more to come. But just to kind of wrap up and maybe address a couple of questions, Just want to reinforce that DataCore is investing a lot in AI capabilities built for you and with you. If you've seen anything here that is interesting that you would like to learn more about, maybe want to partner on a specific use case, please reach out to either Sundar or myself, speak to your customer success manager, and we'd love to talk with you more about your use cases and how we can solve problems for you. So with that in mind, we've got a few minutes. We can address some questions. Yeah, I think I saw some more questions here. I'll quickly address it. I think one of the questions was related to payments. We support processing of full lockbox files and multiple checks because the system takes a file and splits it into individual checks or ACHs or whatever. So you don't have to split it. You can send the entire file. I think that was one of the questions. So I just wanted to make sure I address that. And then a lot of questions were, you know, is a sales order AI capability available today? The answer is yes. You know, we are using AI now instead of OCR. We do we we stick with OCR where it's fully effective, but where it's not so effective, we are replacing it with with with AI so that it can handle multiple layouts, multiple formats, and so on. So that's one thing. And, Jeff, do you wanna take that last question question on the Oh, yeah. For the chemical database. So chemical database is going to be included in the Datacore formulation and regulatory control subscription. So if you already have formulation and regulatory control, you'll be able to access the chemical database in a standalone fashion this quarter or a fully integrated fashion next quarter. If you haven't purchased the formulation and regulatory control product, we will still make the chemical database available for subscription in a standalone fashion. We feel pretty confident that the customers will get the most value using it integrated with formulation and regulatory control. But then for your users that may have to research the regs, if they're not necessarily, you know, working within formulation, still being able to access it in a standalone fashion will be valuable as well. It's a fully web based solution, so you'll be able sign into it, through a browser. You you don't necessarily have to go through through the ERP to get to it. And I still saw some questions coming on how do you get in touch with us if you want to partner on it. I think as Jeff said, email one of us, talk to your CSMs or scan that QR code that we showed you earlier, submit your questions there, multiple ways. And then a lot of the capabilities are available now, like payables, sales order entry, cash application. We have we are working with customers who are actually posting cash live using our solution. Right now, as we speak, the purchase order automation from quote to receipts that, you know, is is gonna it is coming out probably end of this month, early next month, I should say. So you will continue to see a lot of these features coming out at a rapid pace. And if you want to partner with us, reach out. Yep. Sundar. Jeff, thank you so much. Again, I just want to remind everybody that you will receive an email after this webinar with the slides, with the recording, as well as with this contact information should you want to get in touch and participate with us as we bring these new features to market. Obviously, it's one of the things that Datapore is very dedicated to, and we to build this in partnership with our customers. So we appreciate you all, joining today, and stay tuned for that follow-up. You should have it by tomorrow. Alright. Thanks, everybody. Appreciate your time. Thank thank you all. Bye. Bye.