The AI search engine
for music & sound catalogues

Next-generation deep learning makes finding the right track easy and intuitive, and management of descriptive metadata is fast and robust.

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Figaro
KEY FEATURE

Autotagging

Automatically tag tracks using your own taxonomy. Takes feedback from your team, & augments & improves your taxonomy.

Autotagging
KEY FEATURE

Related Tracks

Find related tracks from any track or playlist within your catalogue. Great for continuous search results & personalised playlists.

Related Tracks
KEY FEATURE

Search by Audio

Search using any audio track or playlist so your users can express themselves without words. Give control over which elements of the music are important.

Search by Audio
KEY FEATURE

Metadata Health Check

Our easy-to-use interface tells you everything you need to know about your descriptive metadata and the searchability of your catalogue.

Metadata Health Check

For audio catalogues

Audio Catalogues
Tags
Save time on tagging new tracks
Increase sales
Increase sales by making your deep catalogue searchable and discoverable
Health check
Run a Metadata Health Check to monitor the searchability of your catalogue
Understand intent
Understand the intent of complex user search terms
Audio search
Enable intuitive search from audio
Find tracks faster
Make it faster for your users to find a track that fulfils their needs
Consistent metadata
Make your descriptive metadata more consistent and usable in third party platforms

For audio catalogue users

Music Supervisors
Music Supervisors

Find tracks that fulfil a sync brief faster.

Easily create playlists from a reference track.

Videographers
Videographers

Speed up finding tracks that work for your content.

Use search terms that are meaningful for your content.

Music Producers
Music Producers

Find sounds that you want to work with.

Use reference tracks to quickly find similar sounds.

Developers
Developers

Use the Figaro API to make world-class search a reality in your platform.

Figaro for metadata management

When catalogues grow quickly, it is hard to apply descriptive metadata consistently across the whole catalogue. We understand that the application of words to music is subjective - you probably don’t want an “AI” suggesting tags that don’t match your taxonomy and semantics.

Instead, Figaro gets to know your catalogue and existing descriptive metadata taxonomy and then learns to predict tags. It predicts tags for your existing catalogue to significantly improve the consistency, depth and quality of the descriptive metadata. Figaro takes feedback from your team to improve its predictions and the quality of your descriptive metadata.

If your taxonomy is a bit thin or inconsistent, Figaro makes suggestions to add tags and improve it. But it always starts with the knowledge you have built up in-house.