Data-aware training
Treat workloads as events that create reusable metadata, not isolated jobs that end at a checkpoint.
About
Aero is building the distributed engine for enterprise ML.
The project focuses on the infrastructure layer between training and production: where data gains context, compute is allocated, and models move forward under customer control.
Project status
Architecture and prototype development.
Aero is currently in architecture/prototype development. The website avoids claims about production customers, revenue, certifications, or benchmarks.
Aero explores how enterprise ML infrastructure can retain useful training memory, distribute execution across cloud and edge, and move models into production without breaking customer control. The engine is designed for teams that care about data context, compute placement, deployment boundaries, and adaptation speed.
The public product language stays intentionally abstract. It explains the vision and conceptual system boundaries without describing private repositories, production configs, credentials, customer data, or sensitive deployment details.
Focus areas
The infrastructure thesis.
Aero is being designed around reusable ontology metadata, distributed execution, and controlled model delivery.
Auditable decisions
Keep training context inspectable so future model decisions have a defensible trail.
Prototype discipline
Build with clear claims, explicit boundaries, and no hidden production assumptions.