{"id":4280,"date":"2022-09-20T14:18:22","date_gmt":"2022-09-20T12:18:22","guid":{"rendered":"https:\/\/atgartificialintelligence.com\/ai-case-studies\/"},"modified":"2024-03-21T11:35:24","modified_gmt":"2024-03-21T10:35:24","slug":"ai-case-studies","status":"publish","type":"page","link":"https:\/\/atgartificialintelligence.com\/en\/ai-case-studies\/","title":{"rendered":"Case Study"},"content":{"rendered":"

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Retail and large-scale distribution<\/h4><\/div>
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Catalog navigation<\/strong>
\nOur PANGenius software allows you to revolutionize the way of accessing point of sale information through the natural language querying of article catalogues, product sheets and management systems.<\/p>\n

Sales employees can significantly reduce the time needed to find information, check stocks, anticipate upcoming arrivals, allowing them to streamline customer service. Customers can use a self-service search tool to quickly find the information they need, enhancing their shopping experience.<\/p>\n<\/div><\/div><\/div><\/div><\/div>

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Industry 4.0<\/h4><\/div>
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Data correlation system<\/strong>
\nOur client is a company that builds recycling plants.
\nThis project focuses on analyzing data from sensors associated with specific components in order to understand the correlation between different types of system failures and the values \u200b\u200brecorded by those sensors.
\nUsing a data-driven approach, the project aims to identify patterns or anomalies in sensor data that can be related to specific machinery failures.<\/p>\n

Data Querystrong,
\n<\/strong> Our product, PANGenius, provides users with the ability to perform natural language queries on data originating from MES systems. This functionality allows you to explore detailed or aggregate information on specific jobs or products. Additionally, PANGenius facilitates the generation of analytical graphs, improving the understanding and interpretation of information to support data-driven business decisions.<\/p>\n<\/div><\/div><\/div><\/div><\/div>

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Finance<\/h4><\/div>
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Fraud Detectionstrong,
\n<\/strong> Development of an artificial intelligence system for a major entity in the banking sector, to identify situations such as:<\/p>\n

    \n
  • Anomalous trading days for a given stock, based on its history.<\/li>\n
  • Unusual financial movements by a client, compared to their historical profile.<\/li>\n<\/ul>\n

    These scenarios fall into the category of anomaly detection problems, which focus on identifying unusual activity in the context of data normally considered regular.<\/p>\n<\/div><\/div><\/div><\/div><\/div>

    <\/span><\/div><\/div><\/div>

    Services<\/h4><\/div>
    <\/div><\/div>

    Professional Studies<\/strong>
    \nOur solution, PANGenius, has been implemented in the knowledge base (made up of thousands of documents) of a renowned professional firm.
    \nThanks to it, internal users now have access to an advanced natural language search system to quickly locate documents of interest. This innovation allows us to enhance the firm’s information assets and improve efficiency in managing client requests.<\/p>\n

    Consultancy services<\/strong>
    \nWe revolutionized the data extraction process for one of our clients, who previously manually analyzed a large number of press releases. Through PANGenius, we have automated the extraction of data from texts, their storage in a database and the generation of relevant reporting. This automation not only reduced processing times and minimized errors but also allowed resources to be reallocated to higher value-added tasks.<\/p>\n<\/div><\/div><\/div><\/div><\/div>

    <\/span><\/div><\/div><\/div>

    Media & Entertainment<\/h4><\/div>
    <\/div><\/div>

    Recommendation System<\/strong>
    \nImplementation of a recommendation system for a streaming platform, based on various criteria such as personal preferences, past behavior and demographic characteristics of users. The objective is to present the user with content that is highly relevant to their preferences, optimizing selection times.<\/p>\n<\/div><\/div><\/div><\/div><\/div><\/p>\n","protected":false},"excerpt":{"rendered":"","protected":false},"author":4,"featured_media":0,"parent":0,"menu_order":0,"comment_status":"closed","ping_status":"closed","template":"100-width.php","meta":{"footnotes":""},"class_list":["post-4280","page","type-page","status-publish","hentry"],"yoast_head":"\nCase Study - ATG Artificial Intelligence<\/title>\n<meta name=\"robots\" content=\"index, follow, max-snippet:-1, max-image-preview:large, max-video-preview:-1\" \/>\n<link rel=\"canonical\" href=\"https:\/\/atgartificialintelligence.com\/en\/ai-case-studies\/\" \/>\n<meta property=\"og:locale\" content=\"en_US\" \/>\n<meta property=\"og:type\" content=\"article\" \/>\n<meta property=\"og:title\" content=\"Case Study - ATG Artificial Intelligence\" \/>\n<meta property=\"og:url\" content=\"https:\/\/atgartificialintelligence.com\/en\/ai-case-studies\/\" \/>\n<meta property=\"og:site_name\" content=\"ATG Artificial Intelligence\" \/>\n<meta property=\"article:modified_time\" content=\"2024-03-21T10:35:24+00:00\" \/>\n<meta property=\"og:image\" content=\"https:\/\/atgartificialintelligence.com\/wp-content\/uploads\/2020\/05\/3.jpg\" \/>\n\t<meta property=\"og:image:width\" content=\"1200\" \/>\n\t<meta property=\"og:image:height\" content=\"628\" \/>\n\t<meta property=\"og:image:type\" content=\"image\/jpeg\" \/>\n<meta name=\"twitter:card\" content=\"summary_large_image\" \/>\n<meta name=\"twitter:label1\" content=\"Est. reading time\" \/>\n\t<meta name=\"twitter:data1\" content=\"56 minutes\" \/>\n<script type=\"application\/ld+json\" class=\"yoast-schema-graph\">{\"@context\":\"https:\\\/\\\/schema.org\",\"@graph\":[{\"@type\":\"WebPage\",\"@id\":\"https:\\\/\\\/atgartificialintelligence.com\\\/en\\\/ai-case-studies\\\/\",\"url\":\"https:\\\/\\\/atgartificialintelligence.com\\\/en\\\/ai-case-studies\\\/\",\"name\":\"Case Study - 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