Cyanite

Overview

Cyanite.ai is an AI-driven platform purpose-built for the music industry, offering automated music tagging and similarity search capabilities. It enables music companies to efficiently process, categorize, and navigate large-scale audio catalogs using machine learning models that analyze audio content and generate descriptive tags. Trusted by over 200 companies worldwide, Cyanite positions itself as a practical infrastructure layer for catalog management and music discovery.

At its core, Cyanite leverages AI to automatically extract musical attributes from audio files, creating rich metadata that describes genre, mood, instrumentation, tempo, energy, and other relevant characteristics. This eliminates the need for manual tagging, which is often time-consuming and inconsistent. The platform also includes a similarity search engine that allows users to find tracks that are acoustically or stylistically comparable to a reference track, enabling intuitive discovery beyond keyword-based retrieval.

Cyanite is designed to integrate into professional music workflows, helping organizations streamline how they search, organize, and activate their catalogs. The system supports large-scale ingestion and provides a centralized interface for exploring music assets. Whether it is for internal catalog organization, content research, or client-facing music libraries, the AI-powered tagging and search capabilities are intended to accelerate workflows and reduce operational overhead.

Given its focus on catalog management and discovery, Cyanite is particularly valuable for entities that handle extensive music libraries and need accurate, searchable metadata. Its AI-driven approach aims to make music catalogs more accessible, searchable, and commercially usable, while continuously improving through the underlying machine learning models.

Core capabilities

AI Music Tagging

Automatically generates descriptive metadata for audio tracks, covering musical attributes such as genre, mood, instrumentation, and tempo.

Similarity Search

Enables users to find tracks that are acoustically or stylistically similar to a reference track, supporting intuitive music discovery.

Catalog Organization

Helps music companies structure and manage large music libraries in a centralized, searchable environment.

Full-Text Search

Provides fast, keyword-based retrieval across tagged catalog content, allowing users to locate tracks by descriptive terms.

Scalable Processing

Designed to handle large volumes of audio, making it suitable for comprehensive catalog ingestion and ongoing updates.

Built for Music Companies

Tailored to the needs of labels, publishers, and other music industry stakeholders that require efficient catalog management.

Trusted by 200+ Companies

Widely adopted across the industry, indicating practical reliability and relevance for professional use.

Use cases

Automating metadata tagging for large music catalogs

Finding similar tracks for playlist curation or A&R

Searching music libraries by descriptive attributes like genre or mood

Organizing and deduplicating audio assets

Licensing and sync placement research

Enhancing internal searchability for music teams