INDUSTRY // MUSIC & AUDIO
Software for music products, where creative ambition meets GPU bills.
We re-architected an AI-assisted music production platform, splitting a GPU-heavy monolith into services that cut latency and cost. And we hardened a music game's vibe-coded prototype until state grant committees backed it. Music tech is real software engineering wearing headphones.
Book a thirty-minute call“Creative-first teams build magical prototypes and fragile products. Our job is keeping the magic while replacing the fragility.”
What we build in this industry
Hyperstate AI ran an AI-assisted music production platform where creators worked with a producer-style assistant that kept full context across sessions. The engineering problem was infrastructure: a GPU-heavy monolith with painful deploys and a bill that scaled badly. We split it into orchestrated services and swapped self-hosted ML libraries for scalable alternatives. Latency down, cost down, deployments boring. The startup later ran out of funding after launch, which the case study states plainly.
Soundromeda is a music action-adventure game from a creative-first studio: small team, big ambition, vibe-coded prototype. Our QA-led hardening took it to a state grant committees were willing to back, unlocking further funding.
Music and audio products combine real-time expectations, heavy compute, and users with taste. The compute and the deadlines are engineering problems. We make sure the taste survives them.
AI/ML infrastructure for audio products
Model serving, GPU cost control, and latency work for products where the model is the product. The Hyperstate shape.
Prototype hardening for creative studios
Your prototype convinced people. Now it has to convince a grant committee, a publisher, or a store review. QA-led hardening without flattening what made it special.
Full product build
From mechanics to backend to release, for music products that need an engineering partner rather than another creative hire.
What makes this industry hard
Audio compute is expensive by default
Generation and processing workloads eat GPUs. The difference between a viable product and a burn-rate problem is architecture: what runs where, what is cached, what is replaced by a managed service.
Latency is part of the art
Creators feel delay the way users feel downtime. Real-time expectations force infrastructure decisions most CRUD-app playbooks never face.
Creative codebases resist production
Prototypes from creative-first teams encode taste and shortcuts in equal measure. The skill is triaging which is which before a rewrite destroys both.
Shipped work in this industry
Two music products: an AI production platform re-architected, and a game prototype hardened until grants followed.
Split a GPU-heavy monolith into orchestrated services and swapped self-hosted ML libs for scalable alternatives. Latency and cost …
QA-led hardening took a vibe-coded game prototype to a state grant committees backed.