## Overview DFL / Bundesliga represents one of the most comprehensive implementations of generative AI and LLMOps in the sports media industry, demonstrating production-scale deployment across multiple use cases that directly impact over 1 billion fans globally. This case study, presented at an AWS Summit by representatives from both AWS and Bundesliga, showcases how a major sports league has moved beyond experimentation to deliver multiple generative AI solutions in production environments. The partnership, which began in 2020, emphasizes Bundesliga's commitment to innovation, with the organization notably being among the first to deploy generative AI solutions to production while competitors were still in testing phases. The case study is particularly valuable from an LLMOps perspective because it demonstrates the full stack of considerations necessary for production deployment: from data infrastructure and model selection to real-time inference requirements, multilingual capabilities, and continuous improvement through feedback loops. The solutions address fundamental challenges in media and entertainment: personalizing content at massive scale, automating labor-intensive manual processes, making historical content discoverable, and reaching global audiences in their preferred languages and formats. ## Technical Architecture and Data Foundation At the foundation of Bundesliga's AI initiatives is a robust data infrastructure that serves as the springboard for all downstream AI applications. The organization collects match data through 16-20 specialized cameras (not broadcast cameras) installed in every stadium that track player and ball positions 25 times per second. This generates approximately 3.6 million data points per game, with plans to increase to 150 million data points per game through skeletal tracking data. Additionally, the system captures 1,600 event data points per game representing significant occurrences like goals, yellow cards, and other key moments. This data flows automatically into the Bundesliga data hub in near real-time using AWS serverless architecture. The architecture's performance is impressive: within one second of an event occurring, Bundesliga match facts appear in both live broadcasts and the Bundesliga mobile app. This near-real-time processing capability is fundamental to enabling responsive generative AI applications downstream. The data infrastructure demonstrates several LLMOps best practices. First, the separation of data collection from broadcast operations ensures consistent, high-quality training and inference data. Second, the serverless architecture provides the scalability necessary to handle variable loads across match days. Third, the standardization of data formats and metadata schemas creates a foundation that multiple AI systems can consume reliably. This data-first approach is essential for production LLM deployments, as the quality and accessibility of underlying data often determines the success or failure of AI initiatives. ## Intelligent Generation of Metadata (IGM) One of the most technically sophisticated implementations is the Intelligent Generation of Metadata (IGM) system, which addresses a critical challenge: Bundesliga possesses over 9 petabytes of archival footage, much of which lacks the detailed metadata necessary for automated content discovery and production. While recent videos contain comprehensive metadata including weather conditions, exact timestamps of events, player identification, and match facts, historical footage from the 1960s-1980s typically contains only basic information like which teams played and the location. The IGM system represents a multimodal AI pipeline that combines several AWS services in a coordinated workflow. Video files stored in Amazon S3 are processed through multiple parallel streams. Amazon Transcribe extracts and transcribes commentary audio, converting spoken words into text. Amazon Bedrock then processes these transcriptions using foundation models (specifically mentioned are Nova models and Claude 3.5) to generate coherent, contextual descriptions from what commentators are saying, transforming raw transcription into meaningful metadata. Simultaneously, Amazon Rekognition analyzes the visual content to identify sequence boundaries by detecting camera angle changes. This segmentation is crucial because it breaks long video files into semantically meaningful chunks that can be individually tagged and searched. The system then employs 12 Labs technology (a computer vision service) to extract detailed contextual information from each video sequence, identifying elements like weather conditions (rain, snow), fan activities (holding up scarves with club names), player states (sitting on the ground, celebrating), and other visual details that were previously unsearchable. What makes this particularly sophisticated from an LLMOps perspective is the integration with existing metadata. The system doesn't redundantly generate information that already exists; instead, it matches newly generated metadata against official match data and existing logging feeds, creating a comprehensive metadata layer that combines historical records with AI-generated insights. This approach demonstrates production-ready thinking: efficient use of computational resources, avoidance of data duplication, and creation of a unified metadata schema that applications can reliably query. The IGM system architecture follows a pattern common in production LLM systems: event-driven processing with clear separation of concerns. The use of Amazon OpenSearch for querying and Amazon Bedrock for natural language query generation means that end users can search the entire archive using conversational language, with the LLM translating intent into appropriate search queries. This represents a practical implementation of retrieval-augmented generation (RAG) patterns, though the case study doesn't explicitly use that terminology. ## AI-Powered Live Ticker The AI Live Ticker represents a real-time generative AI application that demonstrates several important LLMOps considerations: latency requirements, prompt engineering, localization, and personalization at scale. The challenge addressed is straightforward but difficult: fans demand real-time match updates on mobile devices, but manual creation of these updates is neither scalable nor easily personalized. The technical implementation follows a serverless event-driven architecture. When live event data (goals, yellow cards, shots, etc.) arrives at the Bundesliga data hub from the stadium, it triggers an AWS Lambda function. This function constructs a prompt incorporating the event details and relevant context, then makes an API call to Amazon Bedrock to generate natural language commentary on the event. The generated commentary is delivered to Bundesliga ticker editors who are watching the game in real-time, providing them with AI-generated draft content that they can approve, modify, or enhance. The performance metrics are impressive from an LLMOps perspective: the system generates contextual commentary within 7 seconds of an event occurring. This is faster than the broadcast signal delay, which is critical for maintaining the value proposition of the service. The speed requirement likely influenced architectural decisions around model selection, prompt complexity, and caching strategies, though these details aren't fully elaborated in the presentation. What's particularly noteworthy is the approach to personalization and localization. The same event can generate commentary in multiple languages simultaneously, addressing Bundesliga's challenge of reaching fans across 200+ countries. Beyond translation, the system supports style customization through prompt engineering. The presentation shows two examples: a "sports journalist style" that provides professional, factual commentary, and a "bro code" or "brawl style" designed to appeal to younger audiences with more casual, energetic language. The example shown includes automatic generation of goal cards with player pictures and contextual information about the goal, all within that 7-second window. This demonstrates sophisticated prompt engineering in production. The system likely maintains multiple prompt templates optimized for different audiences and languages, with the appropriate template selected based on user preferences or audience segmentation. The presenters acknowledge uncertainty about how different styles resonate with target demographics, showing appropriate humility about the experimental aspects while maintaining confidence in the technical execution. This reflects a mature LLMOps approach: deploy production systems while continuing to learn and iterate based on user feedback. The integration of human oversight—ticker editors reviewing AI-generated content before publication—represents a common pattern in production LLM deployments where stakes are high. This human-in-the-loop approach provides quality assurance while still achieving the primary goal of reducing manual workload and enabling editors to focus on creativity and personalization rather than routine content generation. ## Content Localization Engine The Content OS localization engine addresses another critical scaling challenge: producing content in multiple languages for Bundesliga's geographically diverse fanbase. The system supports speech-to-speech, speech-to-text, and text-to-text localization, with a primary focus on video content. The reported results are significant: 75% reduction in processing time and 5x increase in content production volume. The architecture follows a workflow-based approach. When a user requests a video through the DFL media portal in a language different from the original (typically German or English), this triggers an automated localization workflow. The system uses Amazon Transcribe to generate transcriptions of the original audio, then employs Amazon Bedrock to create a properly formatted script that accounts for context and natural language flow rather than just literal translation. Finally, DeepDub AI (a specialized voice synthesis service) generates voice-over in the target language, maintaining appropriate tone and pacing for sports content. The presentation includes a compelling demonstration showing the same video with subtitles versus voice-over generation in another language. The presenters acknowledge that quality differences remain between the original and generated content, showing appropriate transparency about current limitations. They frame this honestly: while not yet perfect, the technology has advanced dramatically compared to just a few years ago, and model improvements continue to enhance output quality. This represents sound LLMOps thinking: deploy solutions that provide value today while maintaining realistic expectations and planning for continuous improvement. An interesting technical detail is the feedback mechanism built into the system. When customers or media partners identify errors in localized content, they can make corrections, and the system learns from these corrections to avoid repeating the same mistakes. This demonstrates implementation of a continuous learning loop, a critical component of production LLM systems. The exact mechanism for this learning (whether it's fine-tuning, few-shot example accumulation, or prompt refinement) isn't detailed, but the existence of such a mechanism shows mature operational thinking. The choice to give media partners the option to use generated content as-is or adjust it reflects understanding of different use case requirements. Some content may require perfect accuracy and human polish, while other content benefits more from speed and volume. This flexibility demonstrates production-ready thinking about serving diverse customer needs rather than forcing a one-size-fits-all approach. ## AI-Generated Stories for Mobile The Bundesliga Stories feature demonstrates another production LLM application: repurposing existing content into formats optimized for different consumption patterns and devices. Bundesliga produces approximately 4,000 articles per season (around 800 words each) primarily for their website and SEO purposes. These articles represent valuable content, but the format isn't optimal for mobile consumption, particularly for younger audiences who prefer faster, more visual content experiences similar to Instagram or Facebook Stories. The technical implementation transforms long-form articles into mobile-optimized story cards using a multi-step pipeline. First, the system normalizes articles by removing extraneous information like navigation elements, ads, and other non-content components. This preprocessing step is crucial for LLM effectiveness, as it focuses the model's attention on relevant content rather than webpage artifacts. Next comes sophisticated prompt engineering—the presenters specifically mention using 200-300 line prompts, which is substantially more complex than simple text summarization requests. These prompts likely include detailed instructions about story structure, tone, length constraints, image selection criteria, and formatting requirements. The complexity suggests careful engineering and testing to achieve consistent, high-quality outputs. The system combines Amazon Bedrock for content generation with Amazon Rekognition for image analysis. Rekognition identifies players in photos (for example, automatically recognizing a specific player like Haaland), enabling intelligent image selection that matches the story content. This multimodal approach—coordinating text generation with visual understanding—demonstrates sophisticated LLMOps implementation beyond simple text-to-text transformations. The pipeline includes image upscaling, suggesting attention to presentation quality, then assembles the final story and pushes it to the Bundesliga mobile app. The production volume is substantial: 400 AI-generated stories per month, representing significant automation of what would otherwise require considerable manual effort. The results demonstrate clear value delivery: 2x increase in time spent in the app, 67% story retention (users who start a story continue through it), and 75% increase in efficiency. These metrics suggest the AI-generated format successfully matches user preferences for mobile content consumption while maintaining engagement quality. From an LLMOps perspective, this use case demonstrates several important patterns. The repurposing of existing content reduces the need for net-new content creation while maximizing value from existing assets. The multi-step pipeline with specialized processing at each stage (normalization, generation, image analysis, assembly) represents good separation of concerns and likely facilitates debugging and improvement of individual components. The integration into the existing app distribution mechanism shows proper end-to-end thinking about deployment rather than creating AI in isolation. ## Personalization at Scale with Amazon Personalize While not strictly a generative AI application, the use of Amazon Personalize demonstrates important complementary LLMOps infrastructure. Bundesliga faces a combinatorial explosion of personalization requirements: 36 clubs across two leagues, with fans following an average of four clubs each, yielding 1.4 million possible club combinations before even considering geographic preferences, language preferences, and individual behavioral patterns. Amazon Personalize addresses this by analyzing user behavior and preferences to create personalized content feeds in the Bundesliga app. The results—20% increase in overall app usage and 67% increase in articles read—demonstrate significant impact. While the technical details of the Personalize implementation aren't elaborated, the integration with generative AI applications is clear: Personalize determines what content to show which users, while the generative AI systems create that content in appropriate formats, languages, and styles. This represents mature thinking about the LLM application stack. Personalization and recommendation engines work alongside generative AI rather than competing with it, with each technology applied to the problems it solves best. The combination creates more powerful user experiences than either technology alone could deliver. ## Commentary Support Systems The data story finder and commentary live system represent another production AI application, though less explicitly generative. This rule-based intelligent engine analyzes live match statistics and compares them against historical data from past seasons, players, and teams to identify extraordinary moments. For example, it might identify that a player's speed in a particular play represents the fastest recorded all season, not just in that game. When such moments are identified, the system automatically alerts commentators and broadcast partners through the commentary live system, enabling richer, more contextual storytelling. The testimonial from Derek Ray, a prominent football commentator, emphasizes how access to these AI-powered insights helps commentators combine their personality with deep information to enhance fan understanding and love of the game. While this system appears more rules-based than LLM-driven based on the description, it demonstrates important infrastructure for AI applications: real-time data processing, historical comparison capabilities, and integration into professional workflows. It's likely that future iterations could incorporate LLMs for generating suggested commentary angles or automatically crafting story narratives around identified moments, building on the existing infrastructure. ## Production Deployment Considerations Several aspects of these implementations demonstrate mature LLMOps practices worth highlighting. First, the emphasis on serverless architectures (AWS Lambda, event-driven processing) provides the scalability necessary for variable workloads that spike during match days. Second, the consistent use of AWS-managed AI services (Bedrock, Rekognition, Transcribe, Personalize) rather than self-hosted models reflects pragmatic decisions to focus on application logic and user value rather than model operations. Third, the integration of human oversight at critical points (ticker editors reviewing AI-generated commentary, media partners having the option to adjust localized content) demonstrates appropriate risk management for public-facing content. Fourth, the feedback mechanisms for continuous improvement (the localization system learning from corrections) show operational maturity beyond initial deployment. Fifth, the multi-year partnership timeline (starting in 2020) and progressive expansion of use cases demonstrates incremental value delivery rather than attempting to solve everything at once. The foundation of robust data infrastructure enabled subsequent AI applications, showing proper architectural sequencing. ## Limitations and Honest Assessment The presenters demonstrate appropriate transparency about limitations. They acknowledge that voice-to-voice localization, while dramatically improved, still has noticeable quality differences from original content. They admit uncertainty about whether the "bro code" style commentary will resonate with younger audiences, framing it as an experiment worth pursuing. They note that comprehensive metadata for historical footage is an ongoing process that will take time to complete across decades of content. This honesty is refreshing in vendor presentations and suggests genuine operational experience rather than marketing hype. From an LLMOps perspective, this realistic assessment of current capabilities and ongoing challenges reflects the reality of production AI systems: they deliver value while having clear limitations, and continuous improvement is ongoing rather than complete. ## Business Impact and Scale The scale of impact is substantial: reaching 1 billion fans across 200+ countries through 75+ media partners, processing 9+ petabytes of archival content, generating 400 AI stories monthly, and delivering real-time AI-generated commentary within 7 seconds. The measured results—significant increases in app usage, content consumption, processing efficiency, and user engagement—demonstrate that these aren't experimental systems but production applications delivering clear business value. The emphasis throughout on personalization, localization, and scaling to serve diverse global audiences reflects appropriate application of AI to genuine business challenges. The alternative—manual creation of personalized, localized content at this scale—would be economically infeasible, making AI not just an optimization but an enabler of entirely new capabilities. ## Broader Context and Strategic Implications Bundesliga's "glass strategy"—owning and controlling the entire media value chain from stadium cameras to fan devices—creates unique opportunities to apply AI at every step. This vertical integration means they can experiment and deploy AI technologies without depending on external partners' technical capabilities or data sharing agreements. This strategic positioning enables the kind of comprehensive AI implementation described in this case study. The partnership with AWS Professional Services is emphasized multiple times, suggesting that AWS expertise and support played significant roles in design and implementation. This reflects a common pattern in successful enterprise AI deployments: combining internal domain expertise with external technical expertise accelerates time-to-value and reduces risk. ## Conclusion This case study represents one of the most comprehensive production deployments of generative AI and LLMOps in the sports media industry. The combination of real-time inference, multimodal AI processing, sophisticated prompt engineering, automated localization, and personalization at scale demonstrates mature operational capabilities. The honest assessment of limitations, emphasis on continuous improvement, and clear business results make this a valuable reference for organizations considering production LLM deployments, particularly in media and entertainment contexts where content personalization, localization, and automated generation at scale are critical business requirements.