Maastr

Maastr is an AI online mastering tool tuned for loud genres, returning a balanced master in seconds from a WAV or MP3 upload. It also bundles version management and feedback sharing so artists and engineers can collaborate on revisions in one place.

Maastr is an online mastering service built around a specific idea: loud music should stay loud without turning muddy. The platform runs mixes through an AI engine that applies the mastering process of producer Jay Maas, then returns a finished master in seconds. Uploads are accepted as WAV or MP3, so the workflow starts from a finished mix rather than individual stems. The pitch is aimed at genres where intensity and pressure matter (rock, metal, punk and adjacent loud styles), and the engine is tuned to preserve that aggression rather than smooth it away. Beyond the audio engine, Maastr positions itself as a working environment for the studio. Sharing tracks, tracking revisions and collecting feedback all happen inside the product, which addresses the usual mess of renamed files, scattered chat threads and unclear version histories. In practice a producer or band uploads a mix, listens to the mastered result next to the original, and then routes notes and revisions to the right people without leaving the browser. The intended users are independent artists, mixing engineers and small studios that want competitive loudness without buying plugins or learning a mastering chain. Because the process is described as zero learning curve and AI-executed, the barrier is low for people who have never touched a limiter. Testimonials on the site come from engineers with credits on major rock releases, which signals who the product is courting. Where it fits: Maastr competes with automated mastering services and with hiring a mastering engineer. It sits closer to the automated camp on speed and cost, but adds collaboration features that most mastering tools leave out, which matters for teams that iterate often. Sign-up is advertised as not requiring a credit card.