The Blueprint: Orchestrating 5,000+ AI assets for a World Cup TV spot

Mito built a serverless, asynchronous architecture on Google Cloud to produce a 120-second Movistar Plus+ World Cup TV spot that aired across every major Spanish television channel, generating more than 5,000 assets — including 1,000 videos — across roughly 65 iteration rounds. Mito runs its API, AI Director and background workers as Java services on Cloud Run, decouples long-running video generation through Pub/Sub, and calls models including Omni, Veo and Nano Banana via Vertex AI; the production team describes the campaign as 95% AI-generated. Mito says the setup addressed rendering latency of four to five minutes per short clip, rate limits on public pay-as-you-go model APIs, and character consistency for real public figures such as chef Ferran Adrià and journalist Juanma Castaño.

Welcome back to The Blueprint, a regular feature where we highlight how Google Cloud customers are tackling unique and common challenges across industries using the latest AI and cloud technologies. We hope to inspire others looking to innovate in their work. AI is rapidly transforming video production at virtually every step of the process, from ideation and storyboarding to filming, editing, and release. At Mito , we’re striving to democratize AI-powered video creation, enabling everyone from solo artists to large enterprises to get the most out of the best creative technologies available. When we founded Mito, our goal was to eliminate the complex technical friction that stops creators from telling long-form, narrative stories with generative AI. However, transitioning from a single five-second clip to a cohesive campaign — such as a 120-second national TV spot for Movistar Plus+, a part of Telefónica, to broadcast across Spain during the World Cup this summer. This effort revealed massive infrastructure and workflow bottlenecks: Asset & workflow overhead: Managing local media libraries across individual workstations was unscalable. For Movistar alone, our production required generating a massive "iceberg" of over 5000 assets 1000 of those videos generated in around 65 iteration rounds. Zero real-time collaboration: Traditional feedback loops required manual downloads, Google Drive uploads, and fragmented communication across chat apps and email. Rendering latency: Standard AI video rendering takes four to five minutes per short clip. Running these processes synchronously froze our user interface and stalled creative velocity. Model disparity & consistency: Every generative model requires distinct prompting logic. Maintaining character persistence, specific lighting, and camera angles across dozens of shots — while preserving the likeness of real public figures like chef Ferran Adrià or journalist Juanma Castaño — was nearly impossible without central orchestration. Capacity constraints: Operating on public pay-as-you-go model APIs left us vulnerable to rate limits and regional throughput throttling during peak traffic. To deliver an infinite, real-time creative canvas for filmmakers, we designed a serverless, asynchronous architecture built on Google Cloud. We run our API, AI Director, and background workers as Java services on Cloud Run, which scales with request volume. In order to address message routing challenges, we decoupled job execution using Google Cloud Pub/Sub as our central event bus. Long-running video generations are offloaded to background Cloud Run workers, allowing creators to keep working uninterrupted on the UI canvas. To minimize latency, media assets are stored in Google Cloud Storage and delivered via Cloud CDN. Expiring key signatures keep pre-release client assets strictly private. Keeping our storage alongside the models we call through Vertex AI reduces network transfer delays. An isolated database architecture helps with the heavy business intelligence and analytics workloads while mitigating potential negative impacts on production performance through Cloud SQL and PostgreSQL. We rely heavily on foundational models like Omni, Veo and Nano Banana, which we access through Vertex AI alongside our custom system prompts. Finally, our structured canvas frontend enforces a "Rule of 6" limiting prompt variables across World, Camera, Light, Texture, Actions, and Character and prioritizes still-frame generation before video rendering to maximize cost efficiency. By pairing our creative orchestration platform with Google Cloud’s infrastructure, we successfully delivered a landmark national broadcast campaign that the production team describes as 95% AI-generated: Prime-time scale: The 120-second Movistar campaign for the Spanish National Team aired across every major television channel in Spain, with 1,441 assets live on a single shared Canvas near launch. AI Director copilot: Our conversational agent translates natural language instructions into precise model parameters while maintaining context, lighting, and visual style across every scene. Seamless collaboration: Distributed teams and brand clients can work on the same canvas in real time. Because new generations are saved as separate assets instead of overwriting earlier ones, stakeholders can duplicate projects to explore parallel creative ideas. Continuous dogfooding: Through our internal studio, Mito Films, we put our tools to the test on high-stakes client projects — whether that's global television spots or keynotes for top robotics startups — ensuring direct feedback flows back into engineering. Future-proof foundation: Our architecture is primed for rendering times to collapse from minutes to seconds, positioning Mito to handle upcoming world models, deeper memory states, and personalized content delivery. Building an enterprise-grade platform for AI storytelling required solving several backend and creative challenges. This includes our creation of a virtual queuing system that mitigates third-party API rate limiting during usage spikes for smooth request concurrency, with bounded retries and a clear outcome for every job. Because creative output cannot be fully validated with standard unit tests, we built an automated evaluation harness powered by Gemini. Acting as an independent judge, it evaluates AI Director outputs against original user intents across an automated test suite, which we run alongside standard tests for high-impact Director changes. We knew early on that we would need multi-model intelligence routing. As such, we built the AI Director to dynamically coordinate tasks across multiple specialized models — including Sonnet, Gemini, and ElevenLabs for audio — treating the spatial canvas like a live code repository to infer adjacent contextual cues. We also needed to embrace an “imperfect aesthetic” to arrive at a more authentic outcome. In our Movistar spot, the story follows Tommy, a 17-year-old student making an AI video project. At the climax, the AI misunderstands a prompt, making coach Luis de la Fuente say "In the same milkshake" while holding a drink. Tommy edits the prompt live to "In the same heartbeat," and the video recalibrates instantly. By incorporating surreal visual elements — like stadium stands built into Galician cliffs or a neighborhood bar covered in pitch grass — we showcased how generative AI can elevate human storytelling rather than obscure it.