{"id":28824,"date":"2025-08-12T05:13:05","date_gmt":"2025-08-12T12:13:05","guid":{"rendered":"https:\/\/www.pingcap.com\/?post_type=ebook-whitepaper&#038;p=28824"},"modified":"2025-11-11T05:20:17","modified_gmt":"2025-11-11T13:20:17","slug":"4-genai-use-cases-that-power-intelligent-scalable-and-context-awaresystems","status":"publish","type":"ebook-whitepaper","link":"https:\/\/www.pingcap.com\/ko\/ebook-whitepaper\/4-genai-use-cases-that-power-intelligent-scalable-and-context-awaresystems\/","title":{"rendered":"4 GenAI Use Cases That Power Intelligent, Scalable, and Context-Aware Systems"},"content":{"rendered":"<p>GenAI isn\u2019t stuck because of models. It\u2019s stuck because of the data layer. Teams juggle an OLTP database, a separate vector store, caches, and sync jobs, creating a \u201cFrankenstack\u201d that slows delivery and serves stale context.<\/p>\n\n\n\n<p>This ebook reveals how four companies\u2014Dify.AI, TOPRISM, APTSell, and a major retailer\u2014replaced that sprawl with a unified data layer that handles transactions, vectors, and even graph data. The result: simpler architectures, fresher context, and AI features that actually ship.<\/p>\n\n\n\n<p>You\u2019ll discover:<\/p>\n\n\n\n<ul class=\"wp-block-list\">\n<li>How<strong> Dify.AI <\/strong>consolidated eight data containers into one unified database cluster and scaled horizontally without re-architecture.<\/li>\n\n\n\n<li>Why<strong> TOPRISM <\/strong>mixed vector similarity and SQL filters in a single query to power fast, accurate conversational BI.<\/li>\n\n\n\n<li>How<strong> APTSell <\/strong>built a \u201cdigital sales employee\u201d that orchestrates tasks over unified SQL, vector, and graph data.<\/li>\n\n\n\n<li>How a <strong>major retailer<\/strong> delivered GraphRAG at scale for 70% faster IT ticket resolution, 45% lower documentation costs, and 40% shorter training.<\/li>\n<\/ul>\n\n\n\n<p>If you\u2019re scaling agentic AI, start with the data foundation. Check out this ebook to see how a unified database turns GenAI from slideware into shipped software.<\/p>\n\n\n\n<p><\/p>\n\n\n\n<p><\/p>","protected":false},"excerpt":{"rendered":"<p>GenAI isn\u2019t stuck because of models. It\u2019s stuck because of the data layer. Teams juggle an OLTP database, a separate vector store, caches, and sync jobs, creating a \u201cFrankenstack\u201d that slows delivery and serves stale context. This ebook reveals how four companies\u2014Dify.AI, TOPRISM, APTSell, and a major retailer\u2014replaced that sprawl with a unified data layer [&hellip;]<\/p>\n","protected":false},"featured_media":29139,"template":"","categories":[199],"tags":[147,9,31,297],"class_list":["post-28824","ebook-whitepaper","type-ebook-whitepaper","status-publish","has-post-thumbnail","hentry","category-customer-stories","tag-distributed-sql","tag-scalability","tag-tidb-cloud","tag-vector-search"],"acf":[],"yoast_head":"<!-- This site is optimized with the Yoast SEO plugin v26.9 - https:\/\/yoast.com\/product\/yoast-seo-wordpress\/ -->\n<title>GenAI at Scale: Unified SQL + Vector + Graph Database<\/title>\n<meta name=\"description\" content=\"Explore how Dify.AI, TOPRISM, and other companies ship GenAI at scale using TiDB as a unified data layer to cut costs and time to value.\" \/>\n<meta name=\"robots\" content=\"index, follow, max-snippet:-1, max-image-preview:large, max-video-preview:-1\" \/>\n<link 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class=\"card-resource bg-white\" href=\"https:\/\/www.pingcap.com\/ko\/ebook-whitepaper\/4-genai-use-cases-that-power-intelligent-scalable-and-context-awaresystems\/\"><div class=\"card-resource__image-container\"><img class=\"card-resource__image\" alt=\"4 GenAI Use Cases_3-1\" src=\"https:\/\/static.pingcap.com\/files\/2025\/08\/21181638\/4-GenAI-Use-Cases_3-1-1.png\" loading=\"lazy\" width=3750 height=1250 \/><\/div><div class=\"card-resource__content-container\"><div class=\"card-resource__content-head\"><div class=\"card-resource__category\">Customer Story<\/div><\/div><h5 class=\"card-resource__title\">4 GenAI Use Cases That Power Intelligent, Scalable, and Context-Aware 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