Good. Afternoon everyone my. Name is Francisco diva and I'm the product lead for. The computer vision and autumn, of platforms. Into. A stock I'll, be delighted to introduce the, computer, vision platform, and then I'll, turn it over to our friends. Uipath. Infra. Gain and automation anywhere, so that they can describe, how, they're using our technology to, make the robotic, process, automation, technologies, better. So. The mission of the computer vision team is to, enable our partners to, build the next generation Enterprise. AI solutions, and this. Focus on the enterprise, has, led in the last few, years to significant, breakthroughs, in to the application of AI across. Different, verticals like. For instance we have industries, applying, our, technology, to attack damage in their facilities, for. Procurement, is use cases or million entertainment, companies using, our technology to implement intelligent. Content management systems, and the, focus of this talk. Companies. Are using our OCR, and natural language understanding technology. To. Derive insights, from, their data provide. Structure and automate, complex. Business workflows. Now. To recap, the computer vision platform, is comprised, of two sets of products the, first set, of products is our, pre. Trained products, and the second one is our customizable. Or ml products the. Vision, API allows. You to. Build. Models, without. Or use models without having to, code. It's completely plug. And play into. Your application, and all, you have to do is just query this, api's with a REST API is, great and perfect, for generic, well understood use cases and, our. Order my products can be customized, we with your own data. Leverage. Behind the scenes Google, state-of-the-art, neural. Architecture search, hyper, parameter tuning. Technology, so you can build high quality models with it and this, is great for more. Complex specialized, use cases. Now. These two categories of, products. Provide. You a very comprehensive, set. Of tools to, implement computer, vision models but. We know that AI tools are only part. Of the equation in, what it takes to, build enterprise. AI. Enterprises. Today are still facing significant, challenges wrangling. Their data and seeking. Buy-in, to actually produce models, at. The last model, so. For that reason in cloudy, AI we, determine that it was critical for us to, democratize. This technology, to. Partner with key, companies so that jointly. We. Can solve the enterprise, deepest. Challenges, and. One. Key challenges, that most, enterprises. Face today is that 90% of, their content it's, completely. Dark and unstructured, and, requires, significant, amount of human effort to be able to understand, and integrate, into a business workflow. For. That reason. Yesterday. We introduced, document. Understanding, AI. Document. Understanding AI is, our portlet, solution to. Help you understand, structured, data things like invoices, legal, documents, tax forms and then, automate, business workflows. And improve your. Decision-making. For. Instance, processing. An invoice today is very, very tough task.
Today. When, you get a physical invoice a human needs to type the fields directly into an earpiece system now, imagine, having to do that across tens or even hundreds, of thousands of invoices, it's a lot a lot of work and, it's. Very error-prone as well. With. Our technology, we can turn a scan. Invoice into. A digital structured. JSON, with, all the fields so that then together with our partners, we. Can integrate this data into a downstream, business. Process like a like a p2p, business. Process, so. This is really. Very transformational. Now. At a high-level document, understanding AI helps. You track. Structure. From your. Unstructured, documents. With. The goal of implementing. Better. Organization of, your data, enable. Search and on top of that you can automate, repetitive. Business, workflows, and improve, decision, making. And. Now to explain, how, this technology is being used, in. A. Real. Business use case in, the RPS pace I'm delighted. To invite, mark. Director. Of AI, at uipath and Robby senior, director of the other services, at the Seco. Thank. You Francesco, so I'd like to start off by talking a little bit about who you I path is in case you're not familiar with us as an organization, we, are today now one of the we are the fastest growing organization. In, the enterprise a company software history that's, growing at the pace that we are we, have over 2000. Enterprise, global, customers, that are using our platform on, a regular basis, and we have over 1500, uipath. Employees, that are growing daily inside, our organization at, a pretty steady clip we. Have over 300, partners, with whom we work within a very large rich partner, ecosystem, that, enables, our platform, to be as robust, as it is we're. Invested, by multiple. Investment, partners, we currently have 400 million dollars worth of investment, 3, billion dollar valuation and, we are recognized, as a category, leader for, our PA robotic. Process automation, from. A sponsorship perspective. We have great sponsors including. Excel, capital, G Colleen, or Perkins, and Sequoia as examples, of the, companies that are backing us in this space and. Just real quickly from an overall perspective what, is robotic, process automation, and what's, uipath, specific. View of this it's, really about enabling, the, easy deployment, of scripts. That can take the work that humans do on a daily basis, and automate. Those tasks, on a highly, repeatable, basis, so, these, tend, to be rules based scripts that we can basically then mirror. What the human does on the screen working with applications. And moving data from one app location to another the, way that we make it very simple is that we work at that UI layer, hence UI, pal so, the integration, doesn't require us to work if we don't want to necessarily at, the API layer or, worry about going down into the services layer the data layer to be able to perform this type of integration, so, if. You want you can integrate UI, layer, or a, API, layer as well the, other compelling aspect. Of why our customers are, finding so much benefit, from the platform, is it's, a low code environment, so, business, users can actually start to work with our platform today, to, simulate, maybe, a particular, process, area that they want to automate and they, discover, that they can actually do that process without having any type of programming, skills and take, that process and. Advocate. It inside the organization, ultimately. To be implemented, as, part of a production. Effort, so. One. Of the interesting things that's happening, in the RPA space is kind, of this beautiful, marriage, between our PA and AI and, so, AI is really going to enable a next-generation, capability. Of smart, robots, so, that fundamentally, we can shift from robots, that are basically, performing.
Those Ruled, based tasks. That are highly repetitive in nature and. Ultimately. Transition, that to more. Of the increasingly, complex tasks, exist inside an organization, that can't necessarily be, codified by rules, and that, require, more cognitive. Type of capabilities. So when we think about AI or machine learning how, can we use those capabilities like forecasting. And probabilistic. Modeling. And scoring. And deep learning type of capabilities, including, computer, vision to, enable robots to, be able to have these new skills to, be able to do better, work more, work inside, of the enterprise so, we see this as an inflection, point actually and it's fundamentally, going to transition, the industry, from this, rules-based structure. To a very intelligent, based, robot, they, again can take on more of the tasking. That we do inside of enterprises. Today now. If we take this and we actually apply it to the space of document. Processing, so, every enterprise in the organization. Deals with large volumes, of documents of varying types throughout, the organization, and their what's important, about those documents, is a key business data is locked up in those documents and, sometimes those documents are in print format and we have to digitize, them to be able to get that data in some type of electronic, format in other, cases we have a digital document that we can get that data from but, the important thing is how do we get that data out reliably. And quickly, so, that ultimately we can move from data to insights, because, that's really what's important, to businesses, I want to unlock that data for my documents, I want, to be able to get insights, and do further knowledge mining, up against that data so, what we can see here is fit as an example. What role does a robot have in that the robot can help automate the process of collecting documents within, the environment, so, it can ingest documents, from an email box as an email attachment maybe, invoices, coming in from an email if there's, a file storage system, it can inspect a file system and fetch documents, from there if there's, a blob storage, type of environment, it can also pull documents, from there so that's a great, skill for a robot, to be able to go look for the documents, that we need one.
Of The benefits that we can do is once we have that document. Then we can assess the document, features and know, for example a particular document, type as an invoice and that we need to process that invoice in a particular, way so we, provide a lot of capabilities. Working, on the capabilities, within our platform to be able to look at the pre processing, aspects, of that, document, extracting. The data from the document, and then any type of post-processing, activities. To be able to get that docket, the data fit. For downstream. Into production, based systems, like in SA P for example, so, the value there is a when we extract that data we get that data in a very common, data format, and, it kind of an intelligent, JSON, structure which gives us key, value pair information, that the extraction, of table level details, in addition. To that we know the confidence level information, that comes from the algorithm, relative. To how well the extraction, was performed, and that's, really important because we can set a lot of rules up up against that type. Of data to be able to treat high confidence, data differently, than perhaps, low confidence. Also. When we have this data there's a lot of things that we can do with respect to leveraging classifiers, so we, can, have document, classifiers, that can find a document that's an invoice, or a healthcare. Based, document, or a benefits, based document, so it can it can treat, those documents, in a different manner we, can also use that data naturally, to be able to support additional machine learning models around scoring. Algorithms. Or specific, custom, fit for purpose models, that, are appropriate, for a particular business domain, or a functional, area, and. Then essentially. Once we've got that data the. Robot can actually work with the human to be able to accomplish a mission so, this is all about humans. And robots and AI is basically, working together the. Way that they do that is through, a human loop process, so. As, we'll demonstrate later, today we, can actually extract that data and, if it doesn't meet that minimum threshold. Level of the confidence, details. That come in that JSON payload, we, can go into a human a loop process, so the human can actually see what was extracted. And then, correct or validate. Certain features from that data extraction, before it goes to downstream systems, after. That process the robot can pick it up again and populate. Google. Sheets or go into sa P or go into a variety of different landing, sources, that. Would include perhaps, some data repositories. For the data mining side so. From, an AI and RP a case study perspective, work this individual, use cases for invoices, but, the capability. Can support documents, of many types, invoices. Traditionally. Tend to be manually processed, so there's a lot of effort associated with getting the invoice up and running and. The invoice is are interesting, because they have many different multiple, feature sets so we see machine generated text, we see handwriting. We have objects, on there like signatures, and the logos, so, it's a consistently. Hard problem, to be able to actually process, these documents, at, scale, and. So, we're, changing the game by working together and bringing our investments, in AI and our RP a platform.
And Our partnership, with Google through, Google's document, understanding, AI capabilities. To be able to help reduce the costs and errors associated with that manual, heavy lifting, of extracting. Data or sweat chair when i rekey, the data into a terminal, or. Into a screen and. Then I can as a result, benefit, from increased, efficiency. And greater speed, and then start to look at things like fraud. And fraud detection in, some of my invoices. That might be in a particular market where we've detected higher fraud so, the approach is to extract content from the forms and tables and, then basically, allow that human loop process system to, begin and you. Know we'll actually demonstrate. That capability, later, on and a visual video presentation. But at a high level essentially. What we're looking at is the supplier invoices can come in the, robot can pick it up it passes it over to the document. Processor, from uipath, where, we then interact, with the Google document, understanding, AI to be able to get the extracted, value out if we, need to perform that validation, step that begins a human loop process, and then, the robot can pick up the document afterwards. After, the humans validated, or refine the data to, be able to carry it over into the backend systems, so, with that I'm gonna pass it over to Robbie to talk about how we're actually applying it in his environment. Thank. You much. So. But. Co-innovation. With, Google, as well as, you iPad, here. I'm going to demonstrate a sample. Representative. Use case that, we, have experimented. With and have proven the use case and, here. We are seeing a sample. That is as close as possible to, the representation. Of every, feature that we just heard from mark like ranging. From unstructured data to, you, know the computer generated machine generated data. Is also there handwritten. Signatures. Are their seals. Approval, seals are there and within the seal again there is unstructured, handwritten. Information. Is there so different. Types, of unstructured. Information is available. So. Gleaning, information, insights. From that unstructured, data is, this use case essentially. We have here all of that information and, most. Importantly, how the, Bart from. The UI platform. Is able to dissect that, information, different. Types of notation, and is, able to progress forward and when, it works on that information, it. Makes, a PA. Call, to. The Google document, understanding, AI. Feature. And then the response is coming back as we heard from our the inner JSON structure format. And based. On that information. To. A certain level of confidence factor, we. Are able to decipher the. Unstructured, content information. Whether. It is the key value pairs including invoice, number, of the day to the supplier details. Particularly. The most important one that the transportation, number, and number, of similar. Key value pairs or. Are, extracted, and it is intelligent, extraction, and. More. Importantly, it's the line. Item, detail with. The table parsing, which is another very important. And powerful feature, from. This. Google. Document understanding, AI API, call and. With. That. Knowing. That extracted. Information and, the. Quality, and, its. Confidence, level is extremely important. We could predefined. Certain, thresholds, and then. Once. The threshold is met the. Bots can proceed. Automatically. To update the back of his systems whether it is ERP, sa PR you, know any other back of his system accordingly like this wheelchair, type of information is you know not required anymore by the humans the body is able to do that. You. Can see the bottom picture, how, that is dissected, back with the response into the JSON, structure every. Element is dissected, there and we, have that information and, the threshold, threshold if. It is not met then. We have the opportunity, for human in the loop and to. Override. That information, do, a side-by-side comparison, like. This you. Have the original, invoice. As. Well, as the. Parsad. Information, side, by side and then, the human the, analyst is able to make an override, if needed based. On the threshold not, being met by, the API. Call. The JSON structure which is which. Is seen here in the picture and after. The review the Bart can continue, once the human, in the loop is updating, and then you. Know the next step the, Bart can proceed and then update the back of his systems as required. Here. Like. I mentioned earlier parsing. The table structure, is very important, one. Powerful feature here worth mentioning, is it's. Not, a power, specific. Table, structure, that you have to template eyes know this. One is a powerful. Computer. Vision, based. Intelligent. Extraction. Feature in, that API call we make and that's.
Why It's worth mentioning that parse, and information is shown in the you a path studio on the left side image on the. Right hand side is the actual invoice, on which the, extraction, was done and. The. Validation station. Is. Providing. That view and and then if you, human-in-the-loop is there all, those fields can be updated and overridden if it is meeting the threshold then system proceeds to the next step automatically, and. Also. Additional feature that mark mentioned is, robots. Can use, intelligent. Classification. There is inbuilt, feature for. Geography. Based detection. Or. Candidate. For audit. Kind. Of detection all these are classifications. That are available in the platform with. Your iPad platform, and then you, could dissect that information, and classify, them into, these kind of categories, in the picture, we are seeing here as the. Representation of, categorization. For, example, on the right hand side you can see after, the threshold, certain. Amount level. You. Want to automatically. Have an audit mentioned, so the Bart catches, it and the platform you have a platform automatically, mentions, that, it is candidate. For auditing, those. Type of features are all built in this, is. Ml. Based AI, computer, vision based. Combinatorial. Use case between, the Google platform, and the, uipath platform, and we did the coin ovation. With, both the partners and. With. That the. Benefits, are of. Numerous. Cases, right I mean it is not just the structured. Unstructured, information. Dissection. That we saw just for invoices, honestly. The benefits, are much, more bigger. Than that it, reduces, the manual effort phenomenally. Reduces. The invoice processing time, because, the bodies, doing on an, automated manner, like. Hundreds, of thousands of documents. Processing. In, some cases and shears. The data is reviewed and validated, by humans, and also. Accuracy. Is. Not going to be an issue because, here the BOD is the one which is actually acting. On it and also another, powerful feature is you're actually able, to glean, that information. And catch it if it is in error prior, hand before, updating, the back of his systems like s APs and other things because here, it is upfront known, and, it allows humans, and BOTS to work together faster. And with. Higher performance over. Time and as, an extension this. Could be extended to various. Other domains. Like supply chain and you know in the warehouse and various, other places and, with. That I would, pass it back to Bart for, the video. To demonstrate these, features yeah excellent Thank You Ravi so, the best part if we could start that actually, let me t this up so the, best part is to actually see in action so you saw this slide where which was great but will actually demonstrate, how these capabilities, work in the platform, in the, first instance, we'll, go ahead and get this started if we could roll the video would be great, so.
Here We're showing how. We can actually start the UI path robot, that's already been built to be able to look into a particular file directory, and see that there's documents, to be processed, we, have a number there that we can take and we can actually upload those to Google Cloud if we wanted to have some storage up in the cloud for that particular, document it can, then be further processed, by the document, understanding, ai and. Then we can determine whether or not we want to actually look at that document based upon the extraction, results, so if we, never had seen the document, before and this is for the first time that we saw an invoice for example, we may not have all the features from the extraction. Associated, with the taxonomy, that's associated with invoices, so, the human can go in very quickly and actually highlight, the actual aspects, of the invoice number the. Purchase order number the, date who, the supplier is the details, with respect to address. Information bill, to and ship to the typical common header type of information that we would see as well, as the subtotal, and the total information so. It's a very quick process again, how the robot can make that available for the human to be able to do this and a very easy UI. Type, of way and I'm being able to complete the process so, another, example is what if we actually meet the minimum threshold value so if we uploaded the second document it goes into Google Cloud and the buckets that we can see that, the document, understanding. Ua i is capable, of extracting the features there if it, meets our criteria for, the minimum threshold, level then we wouldn't necessarily have to have that human loop process, to be able to exist. The document, could continue along its way if, we, have another document that we take through here to demonstrate the table capability. As well as Robbie pointed, out that's a really powerful feature, here to be able to actually parse tables, the, confidence, level might be a little bit lower but what you can see here from the invoice is the, detailed, level data data that's coming out of them the. Invoice itself, so, that's a particularly, challenging. Task to take oftentimes, its pre scripted, or templatized, as Robbie mentioned, here, we're just using computer, vision and the ML type of capabilities. To be able to detect that information, parse it out correctly and again, through validation station. The human is basically, able to see the extraction side by side with, the actual document, itself for that verification, so. Another. Thing with it's interesting is just to see what does a robot see so there's a JSON payload, we, can take that data and naturally we can feed it into something like Google sheets if we wanted to and we could see all the data that's coming out of the actual extraction, process itself in a human legible. Type of way as well, as the detailed. Table, data that, comes out at that or we could use this and analyze it further if we had some reporting. Or analytics, dashboards, that we wanted to be able to put up against this data but, more importantly, probably what we want to do is we want to take that invoice data and we want the robot again now to, pick up that data and start to log in to s ap on our behalf so, the robots, basically, then authenticating.
With User and password credentials. Here, automatically. Knows that it's in the tasks to be able to input invoice, data it can go to the appropriate screen, with an S ap to be able to input that data and put, all the corresponding, information that it extracted. From that process, automatically. So, again the robot is actually performing, these tasks, that a human would typically, do in the environment, without. If, we could stop it here it would be great without having, to necessarily. You. Know go in and do perform, that function itself, so, what we've demonstrated here, really is an incredible end to end type of capability. This particular, example is for invoices, but, it can be applied to any type of enterprise document. Unstructured. Semi-structured. And structured, and that's, another powerful, aspect, of the platform, is that it can be truly used at an enterprise level and just finally tool and here we, have different graphing, that Robbie show before is a classification. Technique our time is out we really enjoyed speaking with you and thank, you very much. Thanks a lot mark and Robin there was an awesome, presentation, and thank, sort of for being a great partner so. Next, I would like to introduce. Abby, SVP, of product and engineering or automation anywhere, and cans, BPO, digital experience, and insights and evoking. From. Automation. Anywhere, and. We. Are again, one of the leading vendors in this entire space we. Are the ones who actually coined the term digital workforce, and the. Idea is just like a human you. Are you know you actually execute on things or do work then, you think about things you analyze, things and you go drink your coffee well. There is a digital equivalent to, that and that's, actually what our product portfolio consists. Of so, we. Have our core enterprise our PA platform. And you. Know the bots can do they can work with 400 different types of frameworks they can actually. The, way I put it is BOTS can see couple, levels deeper than a human can and they. They are, less, likely to make mistakes right so, the core, are. PA platform, can work with any of the applications that are out there then. We actually have had a cognitive, component, to our product lines since the last four years. And. We, can again we. Have several applications, of that including. Understanding, documents. As well as processing, documents, we, have a bot store that is public and open where, you can find 30 to 40 different domains, with all kinds of different documents, being being processed. We, also have a smart bi. Platform that, actually gives you real time not just operational. Insights about the bots you. Know what what each of the bots is up to you know all, kinds of bots security, analysis, as. Well but what. We do is, we. Also work, with the content that is processed by the bots and that. Gives you real-time business insight so we believe that our PA can actually, play. A major role in, for. You to spot business opportunities, and it's not just about cutting down process, cost and cycle times. So this together is the digital work force the digital work force is available to you in our bot store by, process, or by digital, workers. If. You look at any business process, it consists, of these steps. With, respect to data you, are either capturing. The data or you're enriching, it validating. It processing, it reconciling. It and analyzing. And reporting. On it right so. Traditional. RP a while it goes all, the way, a while. While it tackles majority. Of the steps AI, is still needed in several, different aspects so we have AI embedded in the core platform so. Core computer, vision based on deep learning that. Will actually even, if we don't understand, the framework even, if we treat the screen as a giant image the, bots are still able to work hundred-percent reliably, but. We, have expanded, this and, formed.
This True partnership, between our PA and AI to. Be able to embed, any AI, models, so, one of the highlights of the. Platform. Is the, fact that you. Can actually embed, traditional. You. Know you can embed AI models, from. Providers. Such as Google as, well as you can have custom AI models, that you build yourself so for example running a Python script and so, on right. So. We are delighted to be here we have partnered with Google at a. Very high level and. It. Is not just about. You. Know using Google AI but it's also about. Running, bots in the Google cloud and. Finally. You. Know humans, interacting, with the bots, through your Google productivity. Suite so, we, have a very interesting use case here and here. To talk talk, about that use case on how, we have leveraged the Google computer, vision API. To. Prepare. For 10x, growth at, a customer, where you. Know achieving this kind of transaction growth was just, not possible without using, BOTS and so here to talk more about it is ganz, from a partner info game. Thank. You a. Quick, note about infer gain we are a 30, year old software. Engineering firm we are based down in Los Gatos and we've. Got offices all over the world, we, are partners, with some of the leading technology companies and, we assist, fortune. 500 companies on their journey to digital we. Are partners with Google and with automation anywhere, and we. Will talk about a very interesting case study that we've got here for. Our customers. What. We going to talk about is a customer, here which is basically the world's largest travel. Marketplace. And, they've got more than 400, million guest arrivals per. Year on their. Web properties, and mobile properties, and. One of the interesting problems they had was the the. Customer segment where you had more than 10 properties that you wanted to transact, on your on their, platform. These. Properties, if you, ever went through their. Website. You would see that there's. Almost 10 pages, of data that you have to provide in, order to list your property on the platform, and go through all this stuff so highly. Manual process, took. A lot of time it, look up took up to 30 days for the entire process for a property to be listed of. Which 5 to 7 days was just the data part of it and. You. Know though there was a lot of onboarding, assistant, provided, the, whole onboarding, assistance, was provided manually. So you had to call up a team the, team would go through it and stuff. What. Was causing a lot of friction in, this experience, for the customers, was basically the fact was because. This was a manual. Process they. Were seeing a very low activation, rate activation, is the process from when you sign, up to have your property on the site to, actually doing the transactions, on the site and that, was in the low 30s as an, activation rate and that's primarily because of a being. A manual process you, had poor data quality so. You know you had a lot of errors in the data that has been processed. There, was incomplete, attribution. So you couldn't run those metadata. To run campaigns and promotions and, search and. This. Process was being done on a much smaller scale, of volumes and the. Cost anticipating. 10x growth and this. Was just not a scalable, process, for, them going forward and. You. Know when. We worked. With Google and automation anywhere to help, this customer, there was one simple goal that, they had which was how. Do we optimize the, conversion, funnel which means if, I sign up number, of customers, on this side, I need, them transacting, more and more that was the primary thing so if you look at the conversion. Funnel there are three steps in the conversion funnel one. Is you. Have to create a listing, on your on, the on their site with the property and that was something that I was taking a lot, of time like we talked about second.
Is How do you enrich this listing with amenities, data, and other data so. That you can activate, this listing and put it in front of more users, out there and the, third thing was how, do you create more more and more better. Data so that you can run promotions and search so this was the goal with which you did it and that's. Where we first. Brought in, the. Robots that automation anywhere was and we hosted that on Google cloud and. We basically had. The robots doing the, listing creation, and also, the gathering the data so. That a. Lot, of that can be automated and someone does not have to do it manually and once. That listing was created, with all the data on it it went to a human person a team out there which, approved the data put the listing data out there and once, that was done we use, the API integration, into Google cloud to, basically transfer the data to. The. Web property that our customer held and. Once that was done that was the listing was happening, for the website and. This. Was earlier. Being all done manually, without api's, and without a cloud, infrastructure, is all being done automated. Once. We did that and we started seeing some benefits for our customer, we moved on to the next stage of the project which is basically the fact that you've. Got something automated, you got something how, do we scale this automation right, so scaling the automation had three basic parts to it as. You can see the first thing is in, the first phase we already improved the turnaround, time and we. Made it a self-service, so we also integrated JIRA into. The Google. Cloud infrastructure, that customers. Can look at their own tickets and figure out where the property was in the whole process of onboarding, but. We also wanted, to move this to international, scale moved to more countries and that's. Where Google clouds whole scale and infrastructure, came into play and for, them to roll it out to multiple countries was much easier and the. Last stage of the product was to basically use google's AI services, to, see how we can monetize, the metadata that we can pull out of the properties. And, how do we move. It to new product categories a new language categories, in the sense of how, can we use NLP how can use language translation. Services. That Google has so, that we can move, this and this was the road map that we used working with Google and with automation anywhere to, move the customer to, a larger scale. What. We particularly used about I want to talk about here is we, actually embarked, on a very. Fast, and effective. Integration. Of Auto ml vision models into the existing, robots. And the process, we. You. Know this this is the whole project that we did in eight weeks that goes to show the ease of use and of the, auto ml product and we, basically took, all the properties, that they had we took thousand images so, each property comes with some images and in, the previous world someone had to say to the images and look, at each image and see which ability, was in, the, image right and amenities, are very important, if you ever tried, doing. A vacation rental or a hotel because you want to see the property you want to see what it is in it but, it's got a microwave and it's got a fridge but it's got a bathtub. And. That's so more so in the international. Market so we took the thousand images we did the training on the images and we did multiple iterations five-plus, i trations on the on the training and each, iteration took, around two to four hours of model training time to, do that so it was fairly Swift very fairly.
Good To do it and you. Know we looked at a three. To four major amenities, out there TV bathtub, and a, king bed because. That's based, on these amenities, the. Recomm the pricing recommendation, engine of a customer, could actually recommend more effective, prices. That you should be charging for your properties, right, and in. Eight weeks we. Were able to demonstrate a nine, percent accuracy, and upwards, of 90%. Across. All categories that we see out there and that's, where the auto ml vision was not only. Fast. And, efficient. But it was very effective in generating, the, the data that. The customer wanted. The. Customer Alda you know if you if you look at if you look at what. The. Customer is looking at you, know going back to the problem statement that existed, out there one. Of the things was this. Customer was doing this entire process manually, which means they were probably, taking a month to do this entire process of which just, the data capture, part of the, property, was, five to seven T's right, and and, in that one month if you know anything about properties, in, that one month you lose your customer because he's focused onto something else and stuff and that's, where the activation. Problem, really came in right because they were you, know in a month you are losing them you didn't know where the property was and, activation. Was also missing because like I mentioned earlier the, amenities data was not a lot of metadata was not there for them to promote their property, among, the search engines and promotions, and stuff like that so by integrating. Google's AI services. And api's and automation, anywheres robots you know we were able to demonstrate a, hundred, percent increase in the activation rate which is direct, revenues. For the customer that exists out there right, so, that's something that we were able to do that. Total cost went, down 40% I mean being. A digital unicorn, they really never worried about cost but it's, very important, as you scale the process, you want to make sure that per, transaction and per property of cost goes down so we were able to demonstrate. A 40%, reduction in, cost but, I think to me the most important, fact was and which, was causing the biggest friction, among the users was we, were able to turn around the. Turnaround time was reduced but, almost fifty plus seventy to fifty percent which is basically the fact that something. Which took five to seven days was being done in two days this. Is very important, for them because they could get back to their property. Managers fast and tell them that hey we. Signed up you you're on live and stuff like that and. Last. But not the least I, think as you, move a process. To. Scale you know they were looking at upwards, of 150, thousand properties. Over. A calendar, to scale up to a million plus and stuff it's, very important. That, they go about doing this in a self-service, model so for example if there, are exceptions, happening if there are, things. That are you. Know something needs to be changed something needs to be done offline we, only put in a self service automation module, in place and this is where you. Know the ability of integration, with Google cloud and JIRA. And the RPA robots really, came to speed it, was not a custom project we could do that very quickly it, did not require you, know generating, custom API sand stuff so that's something that we did it and by. Doing all this like you see out there I talked about it it's 96%. Accuracy and 90% recall, is, what we came up with the training model so this is now being implemented across all countries and, the. Future state is we are going to increase, it occurs multiple communities. So. That you know as you go across using these properties you will, see that this is this. Is going in a much faster way than you can this. Is one of the use cases that we have seen with our customers, and you. Know Google's object detection here I was very useful to it I'm, just gonna turn it on to a, budget to talk about other cases, that we can come across. This. Is amazing right I mean what can bots do next I think what I like about this is it's. It's it's the application, of, computer vision.
But. It's also the. Realization. Of quick, results given. That this entire project was done in about eight weeks it. Really prepared, the. Customer. To, be -, -. To scale their, number, of transactions, with. Bots. And that's one of the key value props that RPA, brings to the table is that, our PA plus AI will. Give you a, completely. New, level of ROI because, our PA helps ai by, bringing in. Three, ways it brings data, it. Brings training. To AI as well, as it brings the business context, to. Any. Of these services so that's. Just one of the use cases we, have plenty more or that are actually scattered across different. Verticals, so, if you want to learn more about this please visit. Us, at our, booth we, also have a video, off. Of this process, with the bots in action where you know how they actually go from that, beginning, of the property listing all the way to you. Know recognizing, all of the different objects in there and being. Able to, provide. A response and, then human-in-the-loop, right, so the same concepts, of having. The confidence, threshold, and all specified, and you. Know if it is below a certain threshold a human, needs to be in the loop to be able to review and, train. The bots further right so this is basically you. Know AI, as, it as, it learns you know becomes better and the, bots can actually manage more and more by themselves, requiring, you. Know little or no supervision, eventually, so. Like. I said visit us in in, our booth it's boot, 1660. In Moscow. Nice out down here and, we can talk to you about several, other such use cases all, right thank you guys for for, attending and Cisco if you wanted to say. Thanks. Thank. You very much. Just, to wrap up I wanted. To invite you to go to cloud, the Google come slash. Document. - understanding. - AI if you. Want to learn more about these capabilities sign, up and be connected to one of our prime partners. Thank. You.
2019-04-17