Demystifying AI music search

If you've spent any time in a sync team, you know how loaded the word "AI" can be. It promises speed and scale, but it also raises a lot of fair questions about what the technology actually does, what it replaces, and where it leaves the people who've spent years mastering the work.

When we explain AIMS to clients and prospects, the same questions come up over and over. How does it actually find music? What happens to tracks that aren't tagged well? Does it replace what we already have? Is it going to start writing music next?

Here are the four things worth clearing up.

1. It analyzes audio, not just tags

The most common assumption is that AIMS is a smarter keyword search. It isn't. AIMS analyzes the audio itself, then compares what you're looking for against the actual sound of every track in the catalog.

That has a consequence worth pausing on. Because the analysis happens at the audio level, AIMS scans 100% of the catalog on every search, including alt versions, instrumentals, and submixes. A track with poor, missing, or incorrect tags is no longer a track that disappears from results.

This is also why audio search opens up the deep catalog. Most catalogs earn around 90% of their sync revenue from the same 10% of their tracks, largely because the other 90% is hard to surface. Audio analysis changes the math by making the rest of the catalog reachable in a brief response.

2. It understands lyrical sentiment

Lyrics Search isn't keyword matching against a transcript. AIMS reads what the lyrics are actually saying, so a search for "feeling lost in a big city" can surface tracks that capture that idea without needing the exact words.

Some briefs ask for specific words. Many ask for a feeling: "loneliness in a crowd, late twenties, low-stakes melancholy." Lyrics Search reads for that.

3. It works alongside metadata

This one trips people up. AIMS doesn't replace your existing tagging. It works with it.

Metadata still does a lot of work in refining results: filtering by tempo, genre, language, era, key, mood. Audio analysis handles the parts metadata can't, like surfacing tracks that were never tagged for the use case at hand. In practice, sync teams use both. Audio search to open up the catalog, metadata filters to narrow the result.

4. It assists, it doesn't generate

AIMS is assistive AI. It helps you find music. It doesn't make music.

Generative tools have their place, but that place isn't AIMS. Our founders come from sync and production music. Most of our team are working musicians, label and publishing people, voting members of The Recording Academy. The technology is built to give music professionals more time on the parts of the job that need them: the taste calls, the artist relationships, the feel for what's right for the cultural moment. The search part is what the tech is for.

Why this matters

None of this is theoretical. If you work with a catalog, you want confidence that every relevant track has a chance of surfacing, not just the ones that happened to get tagged well. That's the problem AIMS is built to solve.