How We Automated Song Tagging for a Music Publisher

If you run a sync licensing company or manage a music catalog, you already know the problem: every song that comes in needs metadata before it’s useful. Genre, mood, instrumentation, tempo, energy, vocal style — the tags that make a track findable when a music supervisor needs something specific on a deadline.

Tagging is tedious. It’s time-consuming. And when it falls behind, untagged songs don’t show up in searches, don’t get pitched, and don’t generate revenue. They just sit.

We recently worked with a music publisher that was dealing with exactly this. Here’s what was happening, what we built, and what changed.

The backlog problem

This company had 500 songs sitting in a backlog, all waiting to be tagged. Each song took about three minutes to tag manually — listening, categorizing, entering metadata into their system. That’s 25 hours of pure tagging work just to clear what had already piled up, not counting the new songs coming in every week.

The real cost wasn’t just the labor. Every untagged song was invisible to the team doing placements. A music supervisor could be looking for exactly what was sitting in that backlog, and they’d never find it because it wasn’t in the searchable database yet. That’s lost revenue hiding in plain sight.

Why off-the-shelf AI tagging doesn’t work here

There are AI music tagging tools available. The problem is they use their own taxonomies. A generic tool might tag a song as “upbeat pop” when the company’s internal system uses a specific set of mood, genre, and descriptor tags that their team and their clients already know how to search.

Adopting a new taxonomy means retraining the team, rebuilding search habits, and potentially retagging the entire existing catalog for consistency. That’s not a solution — it’s a migration project. The right approach has to work with the system already in place.

What we built

We built two things, designed as a pair.

First, an automated tagging pipeline. Songs get dropped into a folder. They’re analyzed and tagged automatically — using the company’s exact tag taxonomy, not a generic one. The output feeds directly into their existing database in the format their system already expects. No reformatting, no migration, no disruption.

Second, a custom review tool. AI tagging is good, but it’s not infallible — especially with subjective categories like mood. So we built a lightweight web interface where the team can pull up any song, see its tags, click to remove one, stage changes, and confirm. It’s designed to make the human-in-the-loop step fast instead of painful. No more navigating through a clunky system to change a single tag.

Together, these two tools turned tagging from a three-minute-per-song manual grind into something that mostly runs in the background, with a clean interface for the cases where a human needs to weigh in.

The design principle that made it work

This company has been running their catalog a certain way for years. Their team knows the system. Their clients know the search behavior. Introducing new workflows or a new taxonomy would have created friction and resistance — even if the underlying technology was better.

So we built everything to slot into what was already there. The automated tags use their format. The review tool integrates with their existing application. From the team’s perspective, the painful parts of their job got faster. Nothing else changed. That’s the difference between an AI project and an AI deployment that actually sticks.

What’s next

With tagging automated and the catalog enriched, the roadmap gets interesting:

• Catalog health dashboard (already live) — a bird’s-eye view of catalog completeness, flagging gaps and quality issues at a glance
• LLM-assisted search — moving beyond keyword matching to semantic search across the enriched metadata
• Brief-to-song matching — paste a creative brief and get ranked song suggestions from the catalog, cutting the time between a request and a pitch

The takeaway for other businesses

This isn’t a music-specific story. It’s a pattern we see across industries: a repetitive, time-consuming process that’s blocking something valuable from happening. The backlog isn’t always songs — it’s leads that aren’t followed up, intake forms that aren’t processed, content that isn’t published, data that isn’t organized.

If your team has a process where the bottleneck is time and tedium rather than judgment, that’s almost always automatable. And if you can automate it without asking anyone to change how they work, adoption isn’t a problem — it’s invisible.

See the full case study →

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