Not long ago, tennis was impossible to imagine without disputes with line judges. One gesture, one pause, one challenge - and the match turned into a small drama.
\nToday, that scene is becoming history. Since 2025, the ATP Tour has fully switched to electronic line calling, and Wimbledon eliminated line judges across all courts in the same year.
\nTennis hasn’t become less intense - it has simply become more precise, faster, and significantly more technological.
\nProfessional tennis has quietly turned into one of the most technology-driven sports - a true testing ground where AI, computer vision, wearables, and biomechanical sensors are being tested. And all of this is rapidly moving from ATP and Grand Slam tournaments down to academies, clubs, and even amateur players.
\nLet’s break down seven technologies that are already changing the game.
\nThis is perhaps the most visible transformation in recent years.
\nSince 2025, the ATP has fully transitioned to Electronic Line Calling Live, and Wimbledon removed line judges after 147 years of service, implementing electronic officiating across all courts.
\nHow it works: cameras track the ball in real time, calculate the exact landing point, and instantly deliver a call. No delays. No fatigue. No human factor. Accuracy exceeds what the human eye can perceive.
\nBut the key point is not just officiating. The system generates a massive volume of data that can be used further - for analytics, broadcasting, and building new AI tools.
\nPreviously, match data lived somewhere in reports and spreadsheets. Now it works in real time during play.
\nIn 2025, IBM introduced Match Chat and updated AI-powered win probability graphs for Wimbledon, turning the broadcast into an interactive analytics layer.
\nThe ATP had earlier launched Tennis IQ - a platform that opened access to advanced match analytics and historical data for players.
\nIn the broader tennis ecosystem, AI insights like Microsoft Match Insights help analyze performance and generate recommendations based on gameplay data.
\nA notable example is Microsoft Match Insights at the 2025 Billie Jean King Cup final. The system was powered by Hawk-Eye and Azure cloud services, processing match data in real time and delivering tactical recommendations during play.
\nIn practice, this means not just attractive statistics, but deeper game analysis:
\nTennis has always been a sport of repetition. Now every repetition becomes training data.
\nWearable devices used to be exclusively for training. Now they are increasingly entering competitive play.
\nThe WTA approved WHOOP for in-match use back in 2021, and in 2024 the ATP and ITF allowed certain wearable solutions in competition, including devices from Catapult Sports and STATSports. All data is centrally fed into ATP Tennis IQ.
\nWhat is measured
\nFor coaches, this is especially valuable: fatigue can be detected not after the match, but during play.
\nAlthough usage rules may vary across tournaments, the trend is clear - biometrics are becoming an integral part of professional tennis.
\nThis is where the most important layer begins.
\nBiomechanical and EMG sensors are already used to analyze muscle activation, asymmetry, and injury risk factors in tennis.
\nAI analyzes this data and helps detect deviations from a player’s normal movement patterns before they become a problem.
\nIn a sport where an injury can cost a season or a career, this is critical.
\nBut it is more accurate to speak not about “magical pain prediction,” but about precise load monitoring and early risk detection.
\nThis is exactly what we focus on at Inventale. Our platform combines flexible EMG sensors with AI analysis, allowing us to see not only stroke mechanics externally but also how muscles work in each phase of movement. The system detects deviations from an individual baseline - the micro-changes that precede overload.
\nThis does not replace doctors or coaches. It provides an additional layer of data that did not exist before.
\nIf we put everything together, the picture becomes clear:
\nTennis is becoming a fully digitized sport.
\nThe court has cameras. The player has sensors. The racket has sensors. Every shot is data. And that data is analyzed in real time.
\nWhat exists today at ATP and Grand Slam tournaments will become standard in regular clubs within a few years. Electronic line calling is already moving down through ITF levels. Wearables are becoming smaller and more affordable. AI platforms are opening up to a broader audience.
\nTennis is becoming a benchmark sport for AI analytics. Every landing point, every serve speed, every movement pattern can be precisely recorded. And this is why technologies developed in tennis will transfer to other racket sports - squash, padel, badminton.
\nThe core idea is simple: not to replace humans, but to give them what they cannot see.
\nAt Inventale, we operate exactly at this intersection - where data from the racket, the body, and the court come together and start to speak as one.
\nOur racket sensors are proprietary, available in two formats. For professional players - full customization: an ultra-light sensor embedded in the handle, preserving balance and feel, capturing every shot from within — angle at impact, swing speed, vibration patterns.
\nFor training and broader use - a removable sensor: attachable to any racket, removable after sessions, usable across multiple players.
\nFlexible EMG sensors add what racket sensors cannot see: how muscles work in each phase, where load accumulates, where compensations emerge before injury. This is not an external view - it is data from inside the body.
\nAI analysis and computer vision connect everything: spatial movement, stroke mechanics, muscle activation - into a unified player profile that evolves from session to session.
\nAnd finally - the Apple Watch scorekeeper as the foundational data layer: structured scoring, serve tracking, rollback. The base without which all analytics lose context.
\nMost systems on the market operate within a single layer: video, body sensors, or racket data. We combine all layers - and this is what enables insights unavailable to any single approach.
\nOn our website, we explore solutions for tennis and racket sports in more detail - from AI analytics and training assistants to EMG sensors and biomechanical assessment.
\nTennis has already become the laboratory of the future of sport.
\nAnd given the pace of development, that future will arrive much faster than it seems.
","fields":{"readingTime":{"minutes":8.81},"slug":"/en/blog/how-professional-tennis-became-a-testing-ground-for-ai-7-technologies-already-changing-the-game/"},"frontmatter":{"author":["tagaeva"],"description":null,"title":"How Professional Tennis Became a Testing Ground for AI: 7 Technologies Already Changing the Game","date":"2026-05-15T12:19:00.000Z","introduction":null,"formtitle":null,"formtext":null,"thumbnail":{"childImageSharp":{"fixed":{"src":"/static/8b93d26f075d13692b1842f9b985f8ba/eeb1b/img-tennis-3.png","width":1920,"height":1159}}}}}},"pageContext":{"slug":"/en/blog/how-professional-tennis-became-a-testing-ground-for-ai-7-technologies-already-changing-the-game/","type":"blog","externalLink":null,"language":"en","intl":{"language":"en","languages":["en","ar"],"messages":{"404.title":"Page not found","404.button":"Go back to main page","about.title":"Professional, Passionate & Results-Driven","about.title-experts":"Experts & Leadership","about.banner.title":"At Inventale, we build our processes around one goal — delivering outstanding results that drive client success.","about.banner.text1":"Our strength lies in our team: a carefully selected group of experienced engineers, PhDs, and data science specialists who bring deep domain knowledge and unwavering commitment to every project.","about.banner.text2":"Founded in 2015 by Dr. Igor Minakov — a data science expert, serial tech entrepreneur, and thought leader in AI — Inventale has grown into a leading custom software development company based in the UAE.","about.banner.text3":"We are proud to have multiple Doctors of Science and PhDs in technical and mathematical fields, all with hands-on experience in building scalable AI systems, business intelligence tools, and machine learning products.","about.banner.text4":"We don’t just write code — we solve real business problems with AI-powered innovation and precision.","about.leadership.card-1.description":"Dr. Igor Minakov is the founder and CEO of Inventale. \n\nHe has a master's degree in AI from George Washington University and a Ph.D. in Artificial Intelligence, and 25+ years of experience in software development and management. \n\nHe worked for 20 years as a part-time professor and is an author and co-author of more than 150 scientific papers in data mining, multi-agent technology, knowledge management, text understanding, software development toolsets, decision support systems, and business development. 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Share your project goals with Inventale, and our team will help you turn them into powerful AI-driven solutions.","footer.navigation.development":"Development","footer.navigation.consulting":"Technology & Business Consulting","footer.navigation.data-science":"Data Science","footer.navigation.cloud":"Cloud Services","footer.navigation.policy":"Privacy policy","footer.navigation.projects":"Our Projects","footer.navigation.expertise":"Expertise","footer.navigation.full-cycle-development":"Full-cycle development","footer.navigation.blog":"Blog","footer.navigation.about":"About","footer.navigation.company":"Company","footer.navigation.press":"Press","footer.navigation.career":"Career","footer.navigation.menu-title":"Menu","footer.navigation.services-title":"Services","footer.navigation.sitemap":"Sitemap","footer.navigation.main":"Main","footer.navigation.back-to-top":"Back to top","footer.info.first":" ","footer.info.second":" ","footer.info.third":" ","footer.info.address.text":"UAE, Fujairah - Creative Tower\nP.O. 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NexusMentis gives doctors personalized recommendations for each patient.","projects-new.list.ba.title":"Brand Affinity: Platform for Brand Compliance Monitoring, Analytics and Audit Automation","projects-new.list.ba.metatitle":"Brand Affinity: A Platform for Brand Compliance Monitoring, Analytics and Audit Automation","projects-new.list.ba.description":"Enhanced brand compliance control for Majid Al Futtaim: a comprehensive master platform for monitoring, analytics, and audit automation by Inventale.","projects-new.list.b2.title":"B2Connect: A Mobile Application For a Wi-Fi Provider in UAE","projects-new.list.b2.metatitle":"B2Connect: A mobile application for a Wi-Fi provider in UAE","projects-new.list.b2.description":"A mobile app for B2Connect to manage Wi-Fi packages and payments with an integrated e-shop. Revolutionized connectivity and enhanced ecommerce.","projects-new.list.convidi.title":"CONVIDI: Conversational Commerce for Increased Conversion","projects-new.list.convidi.metatitle":"CONVIDI: Conversational Commerce for Increased Conversion","projects-new.list.convidi.description":"CONVIDI: a Conversational Commerce solution that boosts conversion rates and enhances customer experience through AI-based personalized recommendations.","projects-new.list.telecom.title":"Ad Inventory and Audience Forecasting for Telecom","projects-new.list.telecom.metatitle":"Ad Inventory and Audience Forecasting for Telecom","projects-new.list.telecom.description":"Inventale's AI-powered platform improves ad campaign efficiency and increases revenue through enhanced targeting and minimized ad campaign delays.","projects-new.list.lm.title":"LoyaltyManager: Subscription Revenue Management System to Minimize Churn, Increase Loyalty, and Upsell Services","projects-new.list.lm.metatitle":"LoyaltyManager: Subscription Revenue Management System to Minimize Churn","projects-new.list.lm.description":"A subscription revenue management system that minimizes churn, increases loyalty and optimizes the client’s advertising budget.","projects-new.list.ds.title":"Data Sieve: Offline Visitors Data Analytics Platform","projects-new.list.ds.metatitle":"DataSort: Offline Visitors Data Analytics Platform","projects-new.list.ds.description":"A portal that allows analysis of history of users’ movements across specified territories, building user segments, and providing advanced analytics.","projects-new.list.mosaic.title":"Mosaic: Look-alike Modeling for Audience Expanding and Enrichment","projects-new.list.mosaic.metatitle":"Mosaic: Audience Data Expanding and Enrichment","projects-new.list.mosaic.description":"Based on look-alike modeling, Mosaic enriches customer data and enables more accurate audience segmentation and personalized ad targeting.","projects-new.list.sportex.title":"SPORTEX: Sports Statistics Using AI and Video Recognition","projects-new.list.sportex.metatitle":"Sports Statistics Using AI and Video Recognition","projects-new.list.sportex.description":"Sportex is an innovative solution for advanced sport analysis and betting. Based on AI, it provides near real-time sports statistics and game analytics.","projects-new.list.stylist.title":"Your Stylist: Personal Stylist, an AI-Based Mobile Application","projects-new.list.stylist.metatitle":"YourStylist: Personal Stylist, an AI-Based Mobile App","projects-new.list.stylist.description":"Personal pocket stylist that helps both with an everyday choice of user’s clothes from their wardrobe and getting trendy ideas from online shops.","projects-new.list.video.title":"Video Parser: Analysis of Video for Retail","projects-new.list.video.metatitle":"VideoSift: Analysis of Video for Retail","projects-new.list.video.description":"A system that analyzes IP-camera videos from shopping malls in real-time to recognize every visitor in view; collect statistics and build visits heat map.","projects-new.list.mt.title":"Movie-Teller: Predicting Movies’ and TV Shows’ Popularity at the Planning Stage","projects-new.list.mt.metatitle":"Predicting Movies’ and TV Shows’ Popularity","projects-new.list.mt.description":"Movie-Teller: a system that predicts TV series’ popularity at the stage of the idea and enables one to choose the TV series with the highest potential.","projects-new.banner.paragraph-1":"Inventale has proven its efficiency and reliability for clients and partners all over the world. We have been delivering software solutions for companies from the USA, the UK, Europe, and the MENA region","projects-new.banner.title":"Projects","projects-new.banner.full-title":"Featured Projects","projects-new.seo.description":"Over the last 15 years Inventale has implemented more than 50 custom projects for various businesses all over the world - web, mobile and desktop.","projects-new.seo.title":"Inventale’s Custom Software Solutions","projects.banner.paragraph-1":"Inventale has proven its efficiency and reliability for clients and partners all over the world. We have been delivering software solutions for companies from the USA, the UK, Europe, and the MENA region","projects.banner.paragraph-2":"Below you can see the brightest and most interesting examples, but there are many more. If you do not see the solution you are looking for on this list, please do not hesitate to contact us - we’ll be delighted to answer all your questions and discuss how we can help your business","projects.banner.title":"Projects","projects.projects.title":"Featured Projects","projects.learn-more-section.text":"If you want to learn more about this project, leave an email, and we will send you our presentation.","projects.learn-more-section.modal-text":"Get a detailed presentation about the project","projects.learn-more-section.learn-more-text":"Learn More","projects.learn-more-section.thank-you-text":"Thank you!","projects.learn-more-section.promise-text":"We will send you the presentation about the project shortly","projects.seo.description":"Over the last 15 years Inventale has implemented more than 50 custom projects for various businesses all over the world - web, mobile and desktop.","projects.seo.title":"Inventale’s Custom Software Solutions","projects.backlite-media.banner.title":"Backlite Media: Offline Visitors Data Analytics Platform","projects.backlite-media.banner.client-company":"Client Company","projects.backlite-media.banner.client-name":"Major UAE outdoor advertising company","projects.backlite-media.challenge.title":"Challenge","projects.backlite-media.challenge.paragraph-1":"Backlite Media is the leading outdoor advertising company in Dubai that provides premium billboards and out-of-home advertising. Backlite Media was looking for a software solution that would enable them to analyze mobile, Wi-Fi, and GPS data on the users passing by their advertising billboards and address the following business needs:","projects.backlite-media.challenge.paragraph-1-list.item-1":"Segmenting the users based on their routes and digital fingerprints, points of interest;","projects.backlite-media.challenge.paragraph-1-list.item-2":"Choosing the best placements for their advertising stands;","projects.backlite-media.challenge.paragraph-1-list.item-3":"Adjusting advertising policy based on users' interests and demographics;","projects.backlite-media.challenge.paragraph-1-list.item-4":"Collecting and analysing statistics on the ads’ view rates.","projects.backlite-media.solution-description.title":"Solution Description","projects.backlite-media.solution-description.text":"A portal that allows analysis of history of users’ movements across specified territories, building user segments, and providing advanced analytics like:","projects.backlite-media.solution-description.list.first-text":"Places with users’ concentrations","projects.backlite-media.solution-description.list.second-text":"User interests and preferences based on the places they visit","projects.backlite-media.solution-description.list.third-text":"Customers’ loyalty","projects.backlite-media.solution-description.list.forth-text":"Trends and variances","projects.backlite-media.solution-description.list.fifth-text":"Which competitors their users visit, and how often","projects.backlite-media.solution-description.list.sixth-text":"Other business specific tasks","projects.backlite-media.technology-background.title":"Technology background","projects.backlite-media.technology-background.paragraph-1":"AI algorithms to identify a user's routes between venues;","projects.backlite-media.technology-background.paragraph-2":"API/AWS/FTP integration for exporting audience segments to DDP systems like Google Ad Manager, AdForm, Lotame, etc.;","projects.backlite-media.technology-background.paragraph-3":"Big Data processing cloud for:","projects.backlite-media.technology-background.paragraph-3-list.first-text":"building audience segments based on user interests and places they visit (e.g., young parents, style & fashion lovers). Interest profiles development;","projects.backlite-media.technology-background.paragraph-3-list.second-text":"customer loyalty analytics: how often the customers return, the number of new/loyal customers;","projects.backlite-media.technology-background.paragraph-3-list.third-text":"identification of customer resident/working areas","projects.backlite-media.technology-background.paragraph-4":"Competitors’ analytics & visualization in a high-performance web application: number of their visitors/customers; which client’s customers attend competitors; their other places of interest","projects.backlite-media.results.title":"Results and Current Status","projects.backlite-media.results.text":"DataSort is now a full-scale platform, used by many clients including:","projects.backlite-media.results.list.first-text":"Outdoor advertising companies","projects.backlite-media.results.list.second-text":"Private schools","projects.backlite-media.results.list.third-text":"Shopping malls","projects.backlite-media.results.list.forth-text":"Premium car dealers","projects.backlite-media.results.first-image-alt":"DataSort dashboard","projects.backlite-media.results.second-image-alt":"DataSort detailed analytics","projects.backlite-media.results.third-image-alt":"DataSort geo","projects.backlite-media.results.forth-image-alt":"DataSort audience analytics ","projects.backlite-media.seo.description":"A portal that allows analysis of history of users’ movements across specified territories, building user segments, and providing advanced analytics.","projects.backlite-media.seo.title":"DataSort: Offline Visitors Data Analytics Platform","projects.your-stylist.banner.title":"YourStylist: Personal Stylist, an AI-Based Mobile Application","projects.your-stylist.client-company.title":"Client Company","projects.your-stylist.client-company.text":"An independent investor and Inventale’s partner","projects.your-stylist.challenge.title":"Challenge","projects.your-stylist.challenge.paragraph-1":"A frequent problem for many people is one of clothes choice, both everyday and party outfits. In some countries, there is a trend to apply to stylists that guide clients through shopping routines and help to clear their wardrobes. Stylist training courses have also gained popularity. However, both stylist services and the classes are expensive and time-consuming. Our partner thought that Artificial Intelligence could come to help, and a handy mobile application would save users both time and money.","projects.your-stylist.contact-popup.title":"We're seeing great interest in this application from potential users.","projects.your-stylist.contact-popup.paragraph-1":"According to Google Analytics, YourStylist webpage is in 2nd-3rd position on the list, gets 105 views on average per month, and a user spends 3+ minutes on this webpage. Additionally, we get 2-3 requests for the presentation each month - no other project receives such a high level of interest.","projects.your-stylist.contact-popup.paragraph-2":"A sure thing to invest in! Contact us to develop this project together.","projects.your-stylist.solution-description.title":"Solution Description","projects.your-stylist.solution-description.text":"Personal pocket stylist that is always at hand and helps both with an everyday choice of user’s clothes from their wardrobe as well as getting trendy ideas from online shops. The application enables one to find their style considering age, body type, personal color analysis, user preferences, current trends, etc.","projects.your-stylist.technology-background.title":"Technology background","projects.your-stylist.technology-background.text":"Al-algorithms and Machine Learning as the basis of the App’s recommendation engine. The expert system analyses billions of clothes combinations and offers the best matching items from the user’s clothes assortment and online shops. \n\nThe system’s primary characteristics are:\n","projects.your-stylist.technology-background.list.first-item.title":"Personal","projects.your-stylist.technology-background.list.first-item.text":"considers the user’s appearance, wardrobe, occasion, preferences","projects.your-stylist.technology-background.list.second-item.title":"Real-time","projects.your-stylist.technology-background.list.second-item.text":"analyzes photos to give recommendations as the user visits shops or pictures clothing anywhere","projects.your-stylist.technology-background.list.third-item.title":"Professional","projects.your-stylist.technology-background.list.third-item.text":"all recommendations are made by AI based on expertise of professional stylists","projects.your-stylist.technology-background.list.forth-item.title":"Virtual Fitting Room","projects.your-stylist.technology-background.list.forth-item.text":"shows how the user will look in the chosen clothes, both with the items from the wardrobe or a shop","projects.your-stylist.technology-background.list.fifth-item.title":"Universal","projects.your-stylist.technology-background.list.fifth-item.text":"connected with online retailers, selects the options most suitable by style and price","projects.your-stylist.technology-background.list.sixth-item.title":"Self-Learning","projects.your-stylist.technology-background.list.sixth-item.text":"likes and dislikes are the basis for adjusting recommendations","projects.your-stylist.results.title":"Results and Current Status","projects.your-stylist.results.paragraph-1":"The pilot application supports English and Russian languages, has been successfully tested in Russia and the USA and received positive feedback from the test group of hundreds of users.","projects.your-stylist.results.paragraph-2":"We and our partner are now seeking investment to complete the implementation and release the product into the world market.","projects.your-stylist.results.first-image-alt":"YourStylist ","projects.your-stylist.results.second-image-alt":"YourStylist ","projects.your-stylist.results.third-image-alt":"YourStylist ","projects.your-stylist.results.forth-image-alt":"YourStylist ","projects.your-stylist.seo.description":"Personal pocket stylist that helps both with an everyday choice of user’s clothes from their wardrobe and getting trendy ideas from online shops.","projects.your-stylist.seo.title":"YourStylist: Personal Stylist, an AI-Based Mobile App","projects.video-sift.banner.title":"VideoSift: Analysis of Video for Retail","projects.video-sift.banner.client-company":"Client Company","projects.video-sift.banner.client-name":"Shopping Malls in Russia and Algeria","projects.video-sift.challenge.title":"Challenge","projects.video-sift.challenge.paragraph-1":"One of our clients, a shopping mall in Algeria, was looking for visual statistics to find the best spots to place tea and coffee terminals, ATMs, and OOH billboards.","projects.video-sift.challenge.paragraph-2":"Another shopping mall we worked with later had a more challenging task: they wanted to track users not only visually but also process their mobile data and purchase history to offer personalized mobile ads when clients entered the mall.","projects.video-sift.solution-description.title":"Solution Description","projects.video-sift.solution-description.paragraph-1":"A solution that analyzes IP-camera videos from shopping malls in real-time to recognize and count every visitor in view; collect statistics on the visitors for further analysis and reporting, and build visits heat map.","projects.video-sift.solution-description.paragraph-2":"VideoSift addresses the clients’ daily needs by providing:","projects.video-sift.solution-description.list.first-text":"Visualization of visitors’ movements across the mall","projects.video-sift.solution-description.list.second-text":"Statistics on visitors entry hours, identification of peak hours and conversions into buyers at any moment","projects.video-sift.solution-description.list.third-text":"Statistics on all clients for any timeframe for making the comparative study to achieve the best traffic monetization","projects.video-sift.technology-background.title":"Technology background","projects.video-sift.technology-background.text":"The system is based on Neural Networks and Machine Learning, the main functions include","projects.video-sift.technology-background.list.first-item.title":"Video Processing","projects.video-sift.technology-background.list.first-item.text":"based on an open-cv library (a classical pipeline for video processing)","projects.video-sift.technology-background.list.second-item.title":"Object Detection","projects.video-sift.technology-background.list.second-item.text":"a fast open-source solution built on http://dlib.net/","projects.video-sift.technology-background.list.third-item.title":"Person Recognition","projects.video-sift.technology-background.list.third-item.text":"use of machine-learning models based on TensorFlow framework","projects.video-sift.technology-background.list.forth-item.title":"Unique Counting","projects.video-sift.technology-background.list.forth-item.text":"proprietary hash function algorithm for further advanced identification","projects.video-sift.technology-background.list.fifth-item.title":"Gender and Age Identification","projects.video-sift.technology-background.list.fifth-item.text":"based on neural networks","projects.video-sift.results.title":"Results and Current Status","projects.video-sift.results.text":"The application has proven its reliability for two retail clients, and Inventale is currently looking for business opportunities for it in the following fields:","projects.video-sift.results.list.first-text":"Shop assistant work evaluation","projects.video-sift.results.list.second-text":"Analysis of merchandising and product display effectiveness","projects.video-sift.results.list.third-text":"Control over queues and cash-desks zones","projects.video-sift.results.list.forth-text":"Promo effectiveness analysis","projects.video-sift.results.list.fifth-text":"Optimization of business hours schedule","projects.video-sift.results.first-image-alt":"VideoSift ","projects.video-sift.seo.description":"A system that analyzes IP-camera videos from shopping malls in real-time to recognize every visitor in view; collect statistics and build visits heat map.","projects.video-sift.seo.title":"VideoSift: Analysis of Video for Retail","projects.pubmatic.banner.title":"Forecasting Engine \nfor Direct-Deals \nAd Server","projects.pubmatic.banner.client-company":"Client Company","projects.pubmatic.banner.client-name":"PubMatic, a major US digital advertising technology platform for publishers, ad agencies, and advertisers. ","projects.pubmatic.solution-description.title":"Solution Description","projects.pubmatic.solution-description.paragraph-1":"Inventale forecasting engine built-in into PubMatic's Unified Ad Server (UAS) to provide stable high-quality forecasts by any slice of inventory with results immediately available to customers.","projects.pubmatic.solution-description.paragraph-2":"The forecasted values include:","projects.pubmatic.solution-description.list.first-text":"Total inventory","projects.pubmatic.solution-description.list.second-text":"Booked inventory","projects.pubmatic.solution-description.list.third-text":"Available inventory","projects.pubmatic.solution-description.list.fourth-text":"Unique users","projects.pubmatic.solution-description.paragraph-3":"The forecasted values include:","projects.pubmatic.solution-description.list-2.first-title":"30b+","projects.pubmatic.solution-description.list-2.first-text":"processing and analyzing big data volumes","projects.pubmatic.solution-description.list-2.second-title":"<5 sec.","projects.pubmatic.solution-description.list-2.second-text":"quick response time to each request (at least 99% of all synchronous requests to be processed in <5 sec.)","projects.pubmatic.solution-description.list-2.third-title":"~100","projects.pubmatic.solution-description.list-2.third-text":"multiple simultaneous forecast requests (up to 100 concurrent users accessing the system)","projects.pubmatic.solution-description.list-2.fourth-title":"24/7","projects.pubmatic.solution-description.list-2.fourth-text":"availability of service","projects.pubmatic.technology-background.title":"Technology background","projects.pubmatic.technology-background.list.first-item.text":"Custom forecasting solution through custom REST API's over secured protocol. The JSON format for API communications. On-premises setup","projects.pubmatic.technology-background.list.second-item.text":"REST APIs supports both synchronous and asynchronous calls/requests and is backward compatible","projects.pubmatic.technology-background.list.third-item.text":"Hadoop cluster for parsing and forecasting with a load balancer to process high load","projects.pubmatic.technology-background.list.forth-item.text":"Constant monitoring and internal testing to guarantee performance and quality as the network changes over time","projects.pubmatic.forecasting-accuracy.title":"Forecasting Accuracy Analysis","projects.pubmatic.forecasting-accuracy.paragraph-1":"For one of the key publishers","projects.pubmatic.forecasting-accuracy.paragraph-2":"The biggest error was for the Ad Units with incomplete data/unstable traffic, while for the Ad Units with >250k of impressions per week the forecasting error did not exceed 15%","projects.pubmatic.forecasting-accuracy.image-alt":"Normal distribution chart for Ad Units number to Errors percentage","projects.pubmatic.results.title":"Results and Current Status","projects.pubmatic.results.text":"Inventale's Forecasting Engine has successfully met all the criteria set by the client and demonstrated the following results:","projects.pubmatic.results.list.first-text":"3-5 sec per request, 7000+ requests daily","projects.pubmatic.results.list.second-text":"Balance between forecast accuracy and response speed determined by the client","projects.pubmatic.results.list.third-text":"Forecast level accuracy 85-90%+, even for the most complex targeting requests","projects.pubmatic.seo.description":"Inventale forecasting engine built-in into PubMatic's Unified Ad Server (UAS) to provide stable high-quality forecasts by any slice of inventory with results immediately available to customers.","projects.pubmatic.seo.title":"Forecasting Engine for Direct-Deals Ad Server","projects.mosaic.banner.title":"Mosaic: Look-alike Modeling for Audience Expansion and Enrichment","projects.mosaic.banner.client-company":"Client Company","projects.mosaic.banner.client-name":"IQ Data offers an analytical platform for influencers, agencies, and brands.","projects.mosaic.challenge.title":"Challenge","projects.mosaic.challenge.paragraph-1":"The client provides accurate data for efficient advertising and marketing campaigns. They wanted to increase sales volume and revenue due to enriched user data, more accurate audience segmentation, and user interest analytics.","projects.mosaic.solution-description.title":"Solution Description","projects.mosaic.solution-description.text":"Look-alike modeling techniques were the best for filling in the gaps and reconstructing user profiles. The system provides clients’ data enrichment: identification of people like the target audience and customer’s user segments augmentation for more personalized ad targeting.","projects.mosaic.technology-background.title":"Technology background","projects.mosaic.technology-background.paragraph-1":"Primary functionality includes:","projects.mosaic.technology-background.list.first-text":"Reconstruction of users’ gender and age based on their routes and points of interest - shops, sports clubs, beauty salons, etc.","projects.mosaic.technology-background.list.second-text":"Application of data processing and AI algorithms to historical data to create 2D and 3D modeling. Laying the results over a map for better visualization","projects.mosaic.technology-background.list.third-text":"Complementation of the solution with tools analyzing and providing additional data: devices people use, browsers, languages they speak, their interests, and others","projects.mosaic.technology-background.list.forth-text":"Identification of behaviour and patterns that are most common among people taking specific actions (like making a purchase) for more granular ad targeting and higher engagement rates","projects.mosaic.technology-background.paragraph-2":"Application of machine learning model enabled achievement of results with +/- 80% accuracy.","projects.mosaic.results.title":"Results and Current Status","projects.mosaic.results.paragraph-1":"The solution and used methodology have shown their efficiency and expanded the targeted audience by 30%. The applied techniques have been successfully used in other projects that required data analytics and enrichment. ","projects.mosaic.seo.description":"Based on look-alike modeling, Mosaic enriches customer data and enables more accurate audience segmentation and personalized ad targeting.","projects.mosaic.seo.title":"Mosaic: Audience Data Expanding and Enrichment","projects.movie-teller.banner.title":"Movie-Teller: Predicting Movies’ and TV Shows’ Popularity at the Planning Stage","projects.movie-teller.banner.client-company":"Client Company","projects.movie-teller.banner.client-name":"Major Russian media channel producing TV and video content","projects.movie-teller.challenge.title":"Challenge","projects.movie-teller.challenge.paragraph-1":"The client had several project ideas and wanted to assess them to choose the one that would be most successful and bring the highest revenue.","projects.movie-teller.solution-description.title":"Solution Description","projects.movie-teller.solution-description.text":"A system that predicts TV series’ popularity at the idea stage and enables one to choose the TV series with the highest potential.","projects.movie-teller.technology-background.title":"Technology background","projects.movie-teller.technology-background.text-1":"Inventale’s massive experience in working with Big Data and accounting numerous events and adjustments is the basis of this project (apart from data mining and machine learning).","projects.movie-teller.technology-background.text-2":"The steps made to implement the project include:","projects.movie-teller.technology-background.paragraph-1":"Compilation and processing of data on movies and series from IMDb website","projects.movie-teller.technology-background.paragraph-2":"Training of various Random Forest combinations","projects.movie-teller.technology-background.paragraph-3":"Analysis of the resulting hundreds of parameters such as:","projects.movie-teller.technology-background.paragraph-3-list.first-text":"genres and settings;","projects.movie-teller.technology-background.paragraph-3-list.second-text":"cast and film directors;","projects.movie-teller.technology-background.paragraph-3-list.third-text":"the correlation between the year of release and the film or series rating;","projects.movie-teller.technology-background.paragraph-3-list.forth-text":"the temporal genre popularity;","projects.movie-teller.technology-background.paragraph-3-list.fifth-text":"actors, screenwriters, directors ratings, depending on the genre;","projects.movie-teller.technology-background.paragraph-3-list.sixth-text":"socio-demographic perception, etc.","projects.movie-teller.technology-background.paragraph-4":"Inclusion of external factors into the forecast:","projects.movie-teller.technology-background.paragraph-4-list.first-text":"seasonality;","projects.movie-teller.technology-background.paragraph-4-list.second-text":"the competition of major projects and TV shows between channels;","projects.movie-teller.technology-background.paragraph-4-list.third-text":"competition with significant scheduled events (like Super Bowl or the World Cup), etc.;","projects.movie-teller.results.title":"Results and Current Status","projects.movie-teller.results.text":"A back-test was conducted on more than 80,000 cinema projects with more than 85% accuracy. As a result, the client chose the most successful project and filmed this TV series which went down very well with the public.","projects.movie-teller.seo.description":"Movie-Teller: a system that predicts TV series’ popularity at the stage of the idea and enables one to choose the TV series with the highest potential.","projects.movie-teller.seo.title":"Predicting Movies’ and TV Shows’ Popularity","projects.sportex.banner.title":"SPORTEX: Sports Statistics Using AI and Video Recognition","projects.sportex.banner.client-company":"Client Company","projects.sportex.banner.client-name":"UK sports-betting operator","projects.sportex.solution-description.title":"Solution Description","projects.sportex.solution-description.text":"A POC project that can benefit gaming/sports betting, both platform operators and odds-making teams. The customer needed the solution for football match analysis. The platform gives an additional stream of statistics in near real-time for game analysis purposes using video to provide positional and relative statistics. Its advanced functionality offers performance predictions based on historical data.","projects.sportex.solution-description.table.header.first-column":"Basic version covers","projects.sportex.solution-description.table.header.second-column":"Advanced capabilities are","projects.sportex.solution-description.table.first-row.first-column":"Player Recognition (team, number, appearance, position, gate), their pace/speed","projects.sportex.solution-description.table.first-row.second-column":"Player and team performance - current vs. historical (player pace and activity, team position and ball possession)","projects.sportex.solution-description.table.second-row.first-column":"Player contacts and outputs (tackle, tackle break, foul, turnover)","projects.sportex.solution-description.table.second-row.second-column":"Player actions with the ball vs. historical performance","projects.sportex.solution-description.table.third-row.first-column":"Set play actions: throw-in, corner, free kick, goal kick, penalty / start location / end location","projects.sportex.solution-description.table.third-row.second-column":"Potential Injury Identification: sudden flag changes in pace or performance","projects.sportex.solution-description.table.forth-row.first-column":"Set play results: Initiation / Action Result / Success / Failure","projects.sportex.solution-description.table.forth-row.second-column":"All performance stats relative to grounds (home, new field) and pitch conditions (weather, time of day)","projects.sportex.solution-description.table.fifth-row.first-column":"Ball movements, positions, action types and success rates (shot / pass / success / failure)","projects.sportex.solution-description.table.fifth-row.second-column":"Modeling of performance predictions (player, team) based on historical data with real-time adjustments","projects.sportex.technology-background.title":"Technology background","projects.sportex.technology-background.text":"Neural networks and machine learning algorithms are the core of this solution:","projects.sportex.technology-background.list.first-item.title":"Video Processing","projects.sportex.technology-background.list.first-item.text":"based on an open-cv library (a classical pipeline for video processing)","projects.sportex.technology-background.list.second-item.title":"Object Detection","projects.sportex.technology-background.list.second-item.text":"a fast open-source solution based on dlib C++ Library","projects.sportex.technology-background.list.third-item.title":"Person Recognition","projects.sportex.technology-background.list.third-item.text":"usage of machine learning models based on Tensorflow framework","projects.sportex.technology-background.list.forth-item.title":"Unique Counting","projects.sportex.technology-background.list.forth-item.text":"a proprietary hashing algorithm for future re-identification","projects.sportex.technology-background.list.fifth-item.title":"Demographics","projects.sportex.technology-background.list.fifth-item.text":"checking is performed via services that provide data on BSSID and its location (eg., GPS data)","projects.sportex.results.title":"Results and Current Status","projects.sportex.results.paragraph-1":"The POC proved successful, and the applied technologies and algorithms are now being tested for horse-racing analytics and forecasting. Since there is a clear market-movement towards automated video statistics generation, reporting, and data-mining, this technology has several use cases in different fields.","projects.sportex.results.first-image-alt":"Sportex ","projects.sportex.results.second-image-alt":"Sportex ","projects.sportex.seo.description":"Sportex is an innovative solution for advanced sport analysis and betting. Based on AI, it provides near real-time sports statistics and game analytics.","projects.sportex.seo.title":"Sports Statistics Using AI and Video Recognition ","projects.loyalty-manager.banner.title":"LoyaltyManager: Subscription Revenue Management System to Minimize Churn, Increase Loyalty, and Upsell Services","projects.loyalty-manager.banner.client-company":"Client Company","projects.loyalty-manager.banner.client-company-description":"a European video streaming platform offering 50.000+ films, series, and cartoons, and providing their content by a subscription model","projects.loyalty-manager.challenge.title":"Challenge","projects.loyalty-manager.challenge.paragraph-1":"The problems the client faced were the following:","projects.loyalty-manager.challenge.paragraph-1-list.item-1":"60-70% of new subscribers left in the first three months'","projects.loyalty-manager.challenge.paragraph-1-list.item-2":"The monthly loss of regular subscribers was up to 20%;","projects.loyalty-manager.challenge.paragraph-1-list.item-3":"Monthly revenue loss of 15-20%;","projects.loyalty-manager.challenge.paragraph-1-list.item-4":"Low response rate to the advertising campaigns.","projects.loyalty-manager.solution-description.title":"Solution Description","projects.loyalty-manager.solution-description.paragraph-1":"A subscription revenue management system that minimizes churn, increases loyalty and optimizes the client’s advertising budget.","projects.loyalty-manager.solution-description.paragraph-2":"Inventale used ML direct and indirect uplift modeling techniques as the basis for LoyaltyManager. Uplift modeling predicts the influence of treatment (like a direct marketing campaign) on a person’s behaviour and helps to answer questions like:","projects.loyalty-manager.solution-description.list.first-text":"Will my marketing campaign upset or inspire the clients about to churn?","projects.loyalty-manager.solution-description.list.second-text":"What clients will buy anyway, so is it necessary to waste money advertising to them?","projects.loyalty-manager.solution-description.list.third-text":"Has this client purchased it because of my ad?","projects.loyalty-manager.solution-description.list.forth-text":"How do we use our marketing budget effectively?","projects.loyalty-manager.how-it-works.title":"How It Works","projects.loyalty-manager.how-it-works.churn-minimization.title":"Churn Minimization","projects.loyalty-manager.how-it-works.churn-minimization.first-text":"