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.
The Challenge
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.
The Solution
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.
