We give AI an ear.
There are AIs that see. AIs that speak. AIs that write. Scarleta is the AI that listens, built not to fill the world with more sound, but to understand the sound already in it.
Scarleta is defined as much by what it refuses to be.
Not a talker
An AI that listens, not one that talks. Communication is about listening, not filling the room with more noise.
Not a generator
An AI that helps you understand sound, not one that makes more of it. We chose not to be a sound-generation platform.
Not a replacement
An AI that supports the people who create sound, not one that tries to replace them. The credit stays yours.
Background, not foreground.
A lot of AI wants the spotlight: to be the author, the voice, the thing that speaks over everything else. We chose the opposite seat. Scarleta works in the background. It listens closely, tells you what it heard, and hands the room back to you.
For the people who make sound, the last thing they need is another voice competing for the mix. What they need is an ear that never gets tired. Something that can hear the key, the tempo, the noise, the structure, the words, and give it all back clearly so they can keep doing the part only a human can.
An AI ear.
AI is learning the human senses one at a time. There are AIs that specialize in vision, so machines can see. AIs that specialize in language, so machines can talk and read. We are the AI that specializes in listening.
We are an ear. We give AI an ear.
What that looks like today.
The vision is big, but the product is real and here now. Scarleta is an audio-AI platform where you store, analyze, process, build on, and collaborate on your sound. Give it audio or video and it does something useful with the sound: separate a song into stems, isolate or remove an element, transcribe speech, detect chords, key and tempo, tag sounds, reduce noise, apply effects. It gives you back files, structured analysis, or interactive charts and tables.
You keep your whole audio library in one place, compose steps into reusable recipes, and reach all of it two ways: a conversational chat product for people, and a REST API for developers who want to run it at scale.
Where we’re going. Our aspiration, not a shipped feature.
The ambition.
Our aspiration is to become the default way software understands sound. And beyond the tools, we intend to build the place where your entire sound collection lives: not just a library you upload files to, but a place for all your sound that you can ask across, find relationships in, and navigate the way you think about music and audio. The AI that listens should also be the AI that remembers, connects, and helps you make sense of everything you’ve ever worked with.
That is where we intend to go. What we can do right now is laid out plainly on our product pages, and that is the only thing we ask you to judge us on today.