Use cases
Aero is most compelling where ML work is fragmented across data sources, compute surfaces, metadata systems, and production controls.
Realistic applications
No invented customer stories. No fake metrics. Each use case pairs a concrete bottleneck with how Aero should address it.
Teams collecting video across labs, devices, or field locations can segment bounded preprocessing work to local workers before cloud training begins.
Mechanism
Aero uses independent edge workers for transcoding, quantized inference, annotation support, and metadata enrichment.
Outcome
Data becomes useful earlier without giving local devices unrestricted production access.
Teams with evolving datasets need to know what changed, what prior data still matters, and which relationships should influence the next run.
Mechanism
Aero records datapoint relationships and training context through the Ontology / Relationship Layer.
Outcome
Retraining decisions can reason from retained context instead of starting with a static dataset snapshot.
Infrastructure teams should not hand-select regions and instances for every workload when placement can follow demand, geography, and cost policy.
Mechanism
Aero's latency rig turns cloud allocation into engine behavior rather than recurring operator work.
Outcome
Platform teams keep placement explainable while reducing repeated manual routing.
Organizations need model behavior adapted to proprietary datasets without moving into opaque vendor-controlled workflows.
Mechanism
Aero supports the lifecycle: train on BYOC, test in Aero, approve, then deploy to production under customer rules.
Outcome
Model customization stays connected to the training context and customer deployment boundary.
Teams with distributed data, customer-controlled infrastructure, evolving datasets, and production approval requirements.
Aero should not claim to replace GPUs, remove all infrastructure work, or solve production governance by hiding it.
Show that work moves faster because context survives across training, edge preprocessing, allocation, testing, and deployment.
