Cue by Coolhad

Cue by Coolhad, the brainchild of Luke Joachim, is a web app that helps DJs discover similar songs for smoother transitions and better set cohesion. Built on top of Mixcloud and Spotify APIs, it scores tracks based on tempo, key, and lyrical similarity, turning tedious manual matching into a fast, intuitive experience.

Cue by Coolhand

Design and PrototypingProduct and Project ManagementProduct ManagementProject ManagementReact

Challenge

Mixing Compatibility Is Time-Consuming

DJs often dig through multiple apps to find songs with compatible BPMs, keys, or moods. This creates friction during live sets and prep.

Data Exists, But Isn’t Unified

APIs like Spotify, Genius, and MixedInKey provide rich metadata, but they don’t talk to each other. DJs are forced to manually stitch together insights across platforms.

Result

Cue helped DJs go from idea to full set faster. By focusing on practical music metadata and real-world mixes, it became a reliable discovery tool DJs could trust.

80%

Reduced time to find a compatible track by over 80%.

90%

9 out of 10 DJs said the top recommendations were usable without adjustment.

Process

Development focused on a streamlined MVP with key integrations, progressive enhancement in later phases, and mobile-first responsiveness.We scoped aggressively for speed. The MVP focused on core features: search, results scoring, and preview playback. Admin tooling, login options, and social features were deferred for later phases to validate core utility first.

Rapid Discovery & Technical Research

Mapped user needs to existing API capabilities (Mixcloud, Spotify, Genius). Identified that Mixcloud’s track pairing history would provide an effective foundation.

MVP Build

React frontend, Firebase backend, GitLab for source control, and Netlify for deployment. We built custom CMS tools to manage content and ranking rules.

Testing & Iteration

Early DJs tested the product with their own tracklists. Their feedback directly shaped cue scoring weights and UI tweaks.

Solution

Instead of building a recommendation engine from scratch, we anchored Cue around Mixcloud’s public data, ranking top transitions from real-world DJ sets. We layered on metadata from Spotify and Rap Genius to surface matches by key, tempo, and lyrical tone. DJs can search by artist or track and get 3–10 top-ranked songs that will mix well.

Our Insight

We could have gone heavy on AI from day one, but instead, we focused on user-tested transitions and accessible metadata. DJs don’t want black boxes; they want tools that reflect how they think about music.