{"id":30727,"date":"2025-09-20T05:36:43","date_gmt":"2025-09-20T12:36:43","guid":{"rendered":"https:\/\/www.pingcap.com\/?post_type=case-study&#038;p=30727"},"modified":"2025-11-28T03:28:04","modified_gmt":"2025-11-28T11:28:04","slug":"rengage-autonomous-marketing-tidb","status":"publish","type":"case-study","link":"https:\/\/www.pingcap.com\/ko\/case-study\/rengage-autonomous-marketing-tidb\/","title":{"rendered":"How Rengage Power the Future of Marketing Automation with TiDB"},"content":{"rendered":"<p>In the world of marketing technology, &#8220;real-time personalization&#8221; has been the holy grail for years. But the team at Rengage, founded by ex-Uber growth platform veterans, is aiming for something far more ambitious: a future where marketing is not just personalized, but fully autonomous, driven by AI agents that think, act, and optimize on their own.<\/p>\n\n\n\n<p>This vision, however, collided with a fundamental industry problem. The team knew from experience that the intelligence of any AI system is ultimately constrained by the accessibility and integrity of its data. To build a truly autonomous system, they first had to deconstruct the traditional marketing stack and rebuild it on a more logical and efficient foundation.<\/p>\n\n\n\n<h2 class=\"wp-block-heading\"><span class=\"ez-toc-section\" id=\"The_Architectural_Challenge_A_System_Divided_by_Design\"><\/span>The Architectural Challenge: A System Divided by Design<span class=\"ez-toc-section-end\"><\/span><\/h2>\n\n\n\n<p>The primary architectural challenge in any AI-native application is data fragmentation. High-throughput marketing systems must process two distinct categories of data, which are almost always managed in isolated environments:<\/p>\n\n\n\n<ol start=\"1\" class=\"wp-block-list\">\n<li><strong>Hot Data (Transactional):<\/strong> High-velocity, real-time event streams such as user clicks, opens, and purchases.This data requires low-latency writes and reads for immediate decision-making.<\/li>\n\n\n\n<li><strong>Cold Data (Analytical):<\/strong> Aggregated historical data, including engagement metrics, conversion funnels, and user journey analysis. This data requires complex, ad-hoc queries for strategic optimization.<\/li>\n<\/ol>\n\n\n\n<p>Rengage&#8217;s initial architecture, like many others, utilized a multi-system approach. This conventional design produced predictable and unacceptable inefficiencies for a company with their ambitions:<\/p>\n\n\n\n<ul class=\"wp-block-list\">\n<li><strong>Query Latency at Scale:<\/strong> Hybrid queries that required both hot and cold data were slow and computationally expensive, creating a bottleneck that made true real-time responses impossible.<\/li>\n\n\n\n<li><strong>High Operational Overhead:<\/strong> Managing, scaling, and monitoring multiple, disparate database systems consumed significant engineering resources, diverting focus from core product innovation.<\/li>\n\n\n\n<li><strong>Data Synchronization Failures:<\/strong> The requisite ETL pipelines between the transactional and analytical systems introduced latency and created a critical point of failure, compromising the data integrity required for AI decision-making.<\/li>\n<\/ul>\n\n\n\n<p>This bifurcated model was fundamentally illogical for a system designed for <strong>AI-driven autonomy<\/strong>. The agents&#8217; decision-making capabilities were being throttled by the inherent latency and complexity of the underlying data infrastructure.<\/p>\n\n\n\n<h2 class=\"wp-block-heading\"><span class=\"ez-toc-section\" id=\"The_Strategic_Solution_A_Unified_Data_Core\"><\/span>The Strategic Solution: A Unified Data Core<span class=\"ez-toc-section-end\"><\/span><\/h2>\n\n\n\n<p>The logical solution was to eliminate the data silos. Rengage\u2019s engineering team, led by CTO Shujun Liu, made the strategic decision to migrate to a single, unified data platform capable of handling both transactional and analytical workloads. They <a href=\"https:\/\/docs.pingcap.com\/tidb\/stable\/tidb-architecture\" target=\"_blank\" rel=\"noreferrer noopener\">selected TiDB<\/a> based on a clear set of technical and operational criteria.<\/p>\n\n\n\n<p>The decision was predicated on three core requirements:<\/p>\n\n\n\n<ol start=\"1\" class=\"wp-block-list\">\n<li><strong>Mixed Workload Processing Capability (<a href=\"https:\/\/docs.pingcap.com\/tidb\/stable\/htap\">Tran<\/a><a href=\"https:\/\/docs.pingcap.com\/tidb\/stable\/explore-htap\" target=\"_blank\" rel=\"noreferrer noopener\">sactional + Analytical<\/a>):<\/strong> This was the non-negotiable prerequisite. The ability to collapse the hot and cold data pipelines into a single system was the primary architectural goal.<\/li>\n\n\n\n<li><strong>Proven <a href=\"https:\/\/docs.pingcap.com\/tidbcloud\/scalability-concepts\/\">Hor<\/a><a href=\"https:\/\/docs.pingcap.com\/tidbcloud\/scalability-concepts\/\" target=\"_blank\" rel=\"noreferrer noopener\">izontal Scalability<\/a>:<\/strong> The system had to scale out to handle the massive data ingestion and query throughput required by enterprise-level marketing campaigns.<\/li>\n\n\n\n<li><strong>Developer Efficiency via <a href=\"https:\/\/docs.pingcap.com\/tidb\/stable\/mysql-compatibility\" target=\"_blank\" rel=\"noreferrer noopener\">MySQL \ud638\ud658\uc131<\/a>:<\/strong> Leveraging the existing MySQL protocol minimized the need for retraining and retooling, thereby accelerating the migration and future development cycles.<\/li>\n<\/ol>\n\n\n\n<p><em>&#8220;This unified architecture is the logical prerequisite for enabling our AI agents to act in near real-time,&#8221; states Liu.<\/em><\/p>\n\n\n\n<h2 class=\"wp-block-heading\"><span class=\"ez-toc-section\" id=\"Unleashing_AI_Agents\"><\/span>Unleashing AI Agents<span class=\"ez-toc-section-end\"><\/span><\/h2>\n\n\n\n<p>With a unified data core provided by TiDB, Rengage\u2019s vision for autonomous marketing was finally unlocked. Their AI agents could now access a complete, up-to-the-millisecond view of every user and campaign. This empowers them to operate as an integrated, autonomous system.<\/p>\n\n\n\n<figure class=\"wp-block-image size-large\"><img loading=\"lazy\" decoding=\"async\" width=\"1024\" height=\"755\" src=\"https:\/\/static.pingcap.com\/files\/2025\/11\/27031442\/rengage-architecture-diagram-1024x755.png\" alt=\"\" class=\"wp-image-30728\" srcset=\"https:\/\/static.pingcap.com\/files\/2025\/11\/27031442\/rengage-architecture-diagram-1024x755.png 1024w, https:\/\/static.pingcap.com\/files\/2025\/11\/27031442\/rengage-architecture-diagram-300x221.png 300w, https:\/\/static.pingcap.com\/files\/2025\/11\/27031442\/rengage-architecture-diagram-768x567.png 768w, https:\/\/static.pingcap.com\/files\/2025\/11\/27031442\/rengage-architecture-diagram-1536x1133.png 1536w, https:\/\/static.pingcap.com\/files\/2025\/11\/27031442\/rengage-architecture-diagram-2048x1511.png 2048w\" sizes=\"auto, (max-width: 1024px) 100vw, 1024px\" \/><\/figure>\n\n\n\n<p class=\"has-text-align-center\"><em>Rengage shared its unified architecture on SCailE Summit, 2025.<\/em><\/p>\n\n\n\n<p>As shown in the above diagram, user actions and data flow into TiDB. It serves as a single data core for both real-time analytics and immediate action by the AI agents, powering a continuous loop of personalized engagement. Additional benefits include:<\/p>\n\n\n\n<ul class=\"wp-block-list\">\n<li><strong>Autonomous Segmentation:<\/strong> Creating dynamic user segments based on both real-time behaviors and historical patterns.<\/li>\n\n\n\n<li><strong>Intelligent Journey Orchestration:<\/strong> Moving users through personalized journeys, adapting the path based on live insights.<\/li>\n\n\n\n<li><strong>Automated Content Generation:<\/strong> Crafting and tailoring messages for individual users.<\/li>\n\n\n\n<li><strong>Real-Time Performance Evaluation:<\/strong> Continuously monitoring campaign performance and making adjustments on the fly.<\/li>\n\n\n\n<li><strong>Smart Traffic Adjustment:<\/strong> Reallocating campaign resources to maximize outcomes without human intervention.<\/li>\n<\/ul>\n\n\n\n<p>The entire workflow, from event ingestion to multi-channel message delivery, now flows through a single, coherent data environment. This represents a fundamental leap forward in system efficiency and capability.<\/p>\n\n\n\n<h2 class=\"wp-block-heading\"><span class=\"ez-toc-section\" id=\"Lessons_for_Building_the_Next_Generation_of_AI\"><\/span>Lessons for Building the Next Generation of AI<span class=\"ez-toc-section-end\"><\/span><\/h2>\n\n\n\n<p><strong>Rengage\u2019s architectural evolution provides a powerful blueprint for engineering the next generation of <\/strong>AI-native applications<strong>. The core lesson is that <\/strong>architectural simplicity is a strategic advantage.<\/p>\n\n\n\n<p>By unifying their data architecture, Rengage not only solved their immediate latency and complexity issues. They also constructed a more scalable andfuture-proof foundation. Their journey demonstrates that the most effective path to building highly autonomous AI systems is to design a data infrastructure predicated on logic, efficiency, and the aggressive elimination of systemic complexity.<\/p>\n\n\n\n<p><\/p>\n\n\n\n<p><em><strong><strong>Ready to Power Your Autonomous Future?<\/strong><\/strong><\/em><\/p>\n\n\n\n<p><em>Discover the architectural patterns behind the next wave of AI-native applications in our<em> <strong><a href=\"https:\/\/www.pingcap.com\/ko\/ai\/use-case\/\"><\/a><em><strong><a href=\"https:\/\/www.pingcap.com\/ko\/ai\/use-case\/\">AI Use Case Gallery<\/a><\/strong><\/em><\/strong><\/em>.<\/em><\/p>\n\n\n\n<p><\/p>","protected":false},"excerpt":{"rendered":"<p>In the world of marketing technology, &#8220;real-time personalization&#8221; has been the holy grail for years. But the team at Rengage, founded by ex-Uber growth platform veterans, is aiming for something far more ambitious: a future where marketing is not just personalized, but fully autonomous, driven by AI agents that think, act, and optimize on their [&hellip;]<\/p>\n","protected":false},"author":264,"featured_media":30730,"template":"","tags":[147,11,31,297],"customer":[439],"industry":[183],"class_list":["post-30727","case-study","type-case-study","status-publish","has-post-thumbnail","hentry","tag-distributed-sql","tag-real-time-analytics","tag-tidb-cloud","tag-vector-search","customer-rengage","industry-saas"],"acf":[],"yoast_head":"<!-- This site is optimized with the Yoast SEO plugin v26.9 - https:\/\/yoast.com\/product\/yoast-seo-wordpress\/ -->\n<title>Rengage Powers Autonomous Marketing with TiDB | Case Study<\/title>\n<meta name=\"description\" content=\"Learn how Rengage built a fully autonomous marketing platform by unifying data foundation on TiDB. 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