As a two-year-old German startup, Mirelo has captured the industry’s attention by raising an incredible $41 million seed round from giants like Index Ventures and Andreessen Horowitz. This move signals a significant shift in the generative AI landscape, focusing on a problem many have overlooked: the silent world of AI-generated video. The company’s strategy revolves around its specialized sound effects model, a calculated GTM approach, and a plan for rapid, strategic growth. This interview explores the deeper thinking behind Mirelo’s plan to build a defensible moat in a field of giants, its ethical approach to data, and its vision for the future of audiovisual media.
Your recent $41 million seed round is a huge milestone. Beyond scaling, how will this capital specifically help you evolve your Mirelo SFX v1.5 model and execute your go-to-market strategy, which balances API usage with building out the Mirelo Studio workspace for creators?
This capital is about executing a two-pronged offensive with precision. On one hand, a significant portion is being poured directly into our core R&D to push Mirelo SFX v1.5 far beyond its current capabilities. We’re talking about enhancing its ability to interpret subtle visual cues and generate incredibly nuanced, high-fidelity sound. On the other hand, we’re building the ecosystem. The API-first approach, distributing through platforms like Fal.ai and Replicate, gives us immediate revenue and, more importantly, market validation from developers. Simultaneously, we’re investing heavily in the Mirelo Studio. This isn’t just a side project; it’s our long-term vision to build a dedicated, sticky workspace that moves from serving amateurs and prosumers to becoming an indispensable tool for professional creators. The funding allows us to pursue both of these critical paths aggressively without having to sacrifice one for the other.
You’re entering a field with major players like Sony, Tencent, and ElevenLabs. The article mentions your “narrower focus” on sound effects. Can you walk us through the specific technical or strategic advantages this focus gives you to build a “real moat” against such well-resourced competitors?
It’s a classic case of strategic depth over breadth. The giants you mentioned are building massive, all-encompassing generative models, where audio is often just one feature among many. We see sound effects as the entire kingdom. This “narrower focus” is our greatest strength. It allows us to channel all of our $41 million in capital and the brainpower of our entire team into solving one incredibly complex problem better than anyone else. This means we can curate highly specialized training datasets from purchased libraries and partnerships, fine-tune our models exclusively for the physics and texture of sound, and build a product that deeply understands the workflow of sound designers and video creators. Our moat isn’t built from having more resources; it’s built from having more expertise and passion for this specific craft. We aim to be so good at SFX that integrating our solution becomes a no-brainer for the bigger platforms.
The plan to double or triple your team of 10 is ambitious. What key roles are you prioritizing for these new hires, and how will they specifically contribute to advancing your R&D while simultaneously building out a robust product and go-to-market strategy?
That growth is ambitious but essential, and it’s targeted. We’re not just hiring bodies; we’re surgically adding specialists. A large part of the new team will be on the technical side: AI research scientists and ML engineers who live and breathe audio generation. They are the ones who will advance the core technology. But great tech is useless if no one can use it, so we’re hiring product managers and UX/UI designers to build out the Mirelo Studio, transforming it from a tool into a seamless creative experience. Finally, to fuel our go-to-market, we need developer relations experts to support our API users and build a strong community, ensuring our technology is not only powerful but also accessible. It’s a balanced expansion across R&D, product, and outreach to create a self-reinforcing cycle of innovation and adoption.
The article notes your use of purchased sound libraries and revenue-sharing partnerships to address data concerns. Could you share a step-by-step example of how one of these partnerships works in practice to ensure artists are fairly compensated while you build your models?
Absolutely, this is core to our philosophy. Imagine we partner with an independent sound design studio. First, we establish a licensing agreement where they provide their high-quality, ethically-sourced sound library for training our models. Critically, this isn’t a one-time purchase. The second step is ingestion and training, where our model learns the unique characteristics of their sounds. The third and most important step is the revenue-sharing mechanism. For every API call or video generated in our Studio that utilizes sound profiles derived from that partner’s data, they receive a percentage of the revenue. This creates a continuous, symbiotic relationship. The artists’ work directly contributes to the model’s quality, and they, in turn, get a sustained income stream based on the model’s success. It’s about building a foundation of trust and proving that AI can augment and reward human creativity, not just replace it.
As both musicians and AI researchers, you have AI music on your roadmap but started with sound effects. Citing the quote that “sound is 50% of the movie-going experience,” could you elaborate on why you saw more immediate pull and a greater business opportunity in SFX?
That quote from George Lucas really captures the essence of our strategy. While the founders’ passion for music is undeniable, they identified a more acute and underserved pain point in the market first. The explosion of AI video tools created a massive, immediate problem: a flood of silent, lifeless content. The “pull” we saw was from creators everywhere desperately trying to bridge that sensory gap. Sound effects are fundamentally tied to action and context in a video—a door creaking, a car speeding by. This makes it a more defined technical challenge than music, which is deeply subjective and already a crowded field for AI research. By focusing on SFX, we’re solving a universal problem for a booming market and, as the article states, entering a space with “less research happening.” This allowed us to establish a strong technical lead and build that “real moat” before tackling the grander, more complex ambition of generative music.
What is your forecast for the AI-generated media landscape over the next five years, especially regarding the integration of high-quality, synchronized audio moving from a novelty to an industry standard?
I believe we’re at an inflection point, very similar to the transition from silent films to “talkies.” Right now, AI-generated audio is a novelty, an impressive add-on. But over the next five years, it will become an absolute, non-negotiable standard. Video generation platforms that cannot produce high-fidelity, perfectly synchronized, and contextually aware audio will be considered fundamentally incomplete. We will move beyond just adding generic background noise. The expectation will be for AI that can generate a complete, emotionally resonant soundscape in tandem with the visuals—every footstep, every whisper, every atmospheric detail. The companies that are building the specialized, foundational models for this today, like Mirelo, are positioning themselves to become the essential audio infrastructure for the entire next generation of digital content creation. It will cease to be a feature and will simply be part of the definition of AI media.
