Spoken practice, scoring, and review in one platform.
Record against a reference track, review submissions fast, and keep the workflow live across manual, CPU, or GPU deployments.
Built for school constraints, not demo-day assumptions
The recorder, review workflow, and deployment tiers were shaped around what a school can actually run, not around a requirement that every deployment start with a live GPU stack.
Multiple takes, master-track practice, chunked upload, and a browser path that was chosen after the WaveSurfer spike failed on iPhone.
Manual-tier schools still get a usable submission and review pipeline instead of an outage disguised as a product limitation.
RLS-enforced school isolation, canonical storage paths, and append-only grading history are core to the platform, not hidden implementation details.
a missing AI service is a Tuesday, not a 500
Deployment tiers
Start with the tier your school can actually support
Manual review, CPU-assisted scoring, and GPU-accelerated grading all preserve the same product shape. The school chooses the infrastructure threshold, not the other way around.
Manual
Teacher-ready fallback
Use the recorder, upload pipeline, and grade review flow before provisioning AI infrastructure.
CPU-assisted
Low-cost automation
Add transcription and baseline scoring for smaller classrooms where throughput is modest and hardware budgets are tight.
GPU-accelerated
Full inference path
Run the complete pronunciation and fluency stack with the worker queue constrained to safe GPU concurrency.