Music discovery engine that doubled daily listens
A taste-graph recommender combining collaborative filtering, embedding similarity, and session context. Refreshes in real time as users listen.
The challenge
MuseQueue came to us with a clear problem: the existing system was slow, manual, and not scaling with growth.
Our approach
Two-week discovery sprint, four-week build with weekly production deploys, followed by four weeks of tuning and observability work.
The outcome
2.1× daily listens, 45% longer sessions, 18ms inference latency. The system now runs with minimal oversight and has headroom for 3× current load.
More cases
An AI copilot that cut reconciliation time by 74%
We embedded a multi-model copilot into Ledgerly's reconciliation dashboard. Natural language queries, automated exception handling, one-click audit trails.
Personalized product discovery that lifted AOV 23%
A real-time recommendation engine powering homepage, cart, and email — trained on 80M events/day with sub-50ms latency.
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