Dev to Dev Q&A: How Pixel Lab tuned Azure AI to remix Coldplay’s fan experience

Coldplay sought to create a global interactive fan experience for its “A Film For The Future” project. So Pixel Lab needed to develop a solution to spur fans to engage with content in a personalized way at scale, while protecting artistic integrity.

Pixel Lab unlocked a creative development approach that uses tools in Microsoft Azure AI Foundry and Microsoft 365 Copilot. While fans interact with ideas and imagery from the film and Coldplay’s music, they can recontextualize and remix the content.

The ability to create a personalized video of Coldplay’s music and visuals was a hit. The solution inspired significant fan engagement while paving the way for new kinds of interactive digital storytelling.

Pixel Lab’s developer team is pushing the boundaries of digital storytelling, building an AI-powered remix experience that lets Coldplay fans generate personalized video creations. This fusion of artist visuals, music, and emotion transforms Coldplay’s “A Film For The Future” into something interactive. The resulting experience at afftf.coldplay.com allows fans to blend their own thoughts with ideas and imagery from the film, along with music from Coldplay’s latest album, MOON MUSiC. In this way, fans can create their own custom emotional narrative through the use of Microsoft 365 Copilot and the Azure AI stack.

By pairing open-source tools with Azure AI Foundry, the team created a dynamic remix engine based on emotion and visual resonance. Pixel Lab drew on the idea of a “mood ring,” where colors are associated with emotions to simplify the complex, creating something unique to how each fan engages with the band’s music.

“We‘re very proud of ‘A Film For The Future,’ which was created by 151 amazing artists from around the world,” says Coldplay. “The film is a glorious, kaleidoscopic patchwork quilt of individuality, and we love that Microsoft’s technology has helped everyone to make their own unique version of it.”

Pixel Lab developer Josh Wagoner highlights some of the Azure AI technologies used to process the imagery and ideas from “A Film For The Future,” including Azure AI Video Indexer and Azure AI Vision, and create the video playback experience, showcasing WebGL, Canvas, and the Web Audio API.

In this Q&A, Pixel Lab developers Robby Ingebretsen, Josh Wagoner, and Joel Fillmore take us behind the scenes. They share how they brought one of the most forward-thinking AI-powered music experiences to life—where fans, technology, and creativity all remix together.

How did you approach building an AI-driven creative platform from the ground up?

Robby Ingebretsen: We first set out to define what an AI-driven remix experience should feel like. We weren’t just building a tool—we were creating an artistic collaboration between Coldplay’s visuals, their music, and the fan’s own input. That meant designing AI to feel like a creative partner, not just an algorithm. Azure AI Foundry let us try out different models and evolve the interaction until it felt right.

Josh Wagoner: The key was mapping user inputs to the visuals in an organic way. The film is a collection of artist-submitted video clips, and we needed a method to match fan-generated text with the videos. Our “mood ring” system helped us match emotional tone to imagery. We connected color to emotion, and we then used Azure AI Foundry as a gateway to a wide variety of products, including Azure OpenAI APIs, Azure AI Vision, and Azure AI Video Indexer, to process the film and align fan-submitted text with specific clips to feel personal and real.

How did you use the Azure AI stack to analyze and categorize Coldplay’s artist-created visuals?

“We weren’t just building a tool—we were creating an artistic collaboration between Coldplay’s visuals, their music, and the fan’s own input. That meant designing AI to feel like a creative partner, not just an algorithm. Azure AI Foundry let us try out different models and evolve the interaction until it felt right.”

Robby Ingebretsen, Founder, Pixel Lab

Josh Wagoner: We started by running the 151 video clips through Azure AI Video Indexer to segment scenes and identify transitions. We then fed those clips through Azure AI Vision to extract color, object recognition, and movement patterns. That gave us a rich set of attributes to work with. We combined those with GPT-generated summaries to turn the visuals into a format the AI could match to fan-submitted text.

Robby Ingebretsen: Once we had a structured dataset, the next step was making sure it aligned with fan inputs. We used the latest GPT-series model to generate textual descriptions of each video clip, essentially translating visual information into something our AI matching system could process. We built a framework where fan-submitted text could be analyzed, categorized, and matched to corresponding video moments in real time.

How did you build the AI system that matches text to video content organically?

Josh Wagoner: The system had to analyze user inputs, determine the sentiment and emotional tone, and find the best-matching video content. We used Azure OpenAI to classify text into predefined emotional categories. Then we developed a ranking system that scored potential video matches based on sentiment analysis, visual coherence, and overall thematic relevance.

Joel Fillmore: The AI pipeline involved multiple steps. First, user text was processed through sentiment analysis. Then, the system queried our database of video segments and compared them based on emotional similarity. Finally, the AI ranked the best-matching clips and dynamically assembled a remix experience for the user.

Robby Ingebretsen: One of the most rewarding aspects of this project was seeing how the AI-driven matching felt organic. We weren’t just throwing random video clips together—we were building a system that let fans cocreate something emotional and unique, within Coldplay’s world.

How did you ensure seamless synchronization, scalability, and security for a global audience?

“We designed the back end to handle traffic spikes by caching AI results and scaling compute dynamically. Using Azure Functions (Flex Consumption plan), we ensured back-end processes could handle thousands of simultaneous requests without breaking.”

Josh Wagoner, Developer, Pixel Lab

Joel Fillmore: We wanted every user worldwide to see the same experience at the same moment. Each remix is synced to a global timeline, so fans can see when their personalized version will play in the continuously streaming song. We used Azure Front Door and Azure Content Delivery Network to deliver content quickly across geographies, and Static Web Apps made it possible to scale seamlessly based on demand.

Josh Wagoner: We designed the back end to handle traffic spikes by caching AI results and scaling compute dynamically. Using Azure Functions (Flex Consumption plan), we ensured back-end processes could handle thousands of simultaneous requests without breaking. And security was just as critical. We implemented encrypted user submissions, granular access controls, and protected endpoints through secure API gateways.

How did you build the user interface for the application?

Josh Wagoner: The remix plays on a video canvas rendered with WebGL, with real-time effects driven by the Web Audio API. When the audio plays, the outer ring pulses with the beat, using frequency analysis to animate color shifts and glow. We used Canvas and Three.js to apply a soft blur and animate the mask dynamically, giving the visuals a subtle, breathing quality. The whole interface is immersive, like the experience is reacting to the music in real time.

What advice would you give to developers looking to build AI-powered interactive experiences?

“And finally, always design for scale. Even if it starts as an experiment, think through concurrency, caching, and how your services will respond under load. Azure gives you flexibility—but only if you plan for growth from the start.”

Joel Fillmore, Developer, Pixel Lab

Robby Ingebretsen: AI is more accessible than many developers think. The most important thing is learning how to frame problems in a way that AI can help solve. You don’t need to be a machine learning expert to build something meaningful—you just need a clear vision and the right tools.

Josh Wagoner: Experimentation is key. AI doesn’t always behave predictably, so you need to iterate quickly and test different approaches. We spent a lot of time testing the system with real-world fan input and refining how matching worked over time.

Joel Fillmore: And finally, always design for scale. Even if it starts as an experiment, think through concurrency, caching, and how your services will respond under load. Azure gives you flexibility—but only if you plan for growth from the start.

Delivering AI-powered engagement for fans around the world, these developers used multiple Azure AI solutions to pave the way for a new kind of interactive storytelling. With Azure AI Foundry, Pixel Lab built a remix engine that’s scalable, customizable, and emotionally resonant, proving that with the right tools, developers can lead the next era of creative innovation.

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