FAQ
Aero's public story should explain the engine clearly without pretending every implementation detail belongs on a marketing page.
Questions
These answers are written to pass the engineer's eyebrow test: mechanism first, claim second.
Aero is positioned as an enterprise AI infrastructure engine. It coordinates training, metadata, compute placement, model testing, and production delivery rather than selling a single opaque model.
Aero's product narrative centers on BYOC execution. Training runs inside the customer's compute boundary while Aero organizes useful metadata around the run.
It analyzes datapoints during training and builds reusable relationships across features, classes, versions, and training context. The goal is to make data understanding reusable instead of trapped inside one run.
No. Edge workers are for bounded lightweight tasks such as video transcoding, quantized inference, preprocessing, annotation support, and metadata enrichment.
No. Each device receives segmented work that fits its own limits. Two 8GB devices are not treated as one 16GB machine.
Not for the public marketing site. Sign-in adds friction and creates privacy obligations before there is a product workspace to protect. The better near-term path is privacy-respecting analytics plus a waitlist form that asks the right qualification questions.
Use page-level analytics for aggregate behavior, track which product pages get interest, and ask waitlist visitors about their role, bottleneck, and execution surface. Avoid collecting sensitive infrastructure details.
Credentials, keys, exact schemas, production topology, customer data, private repository details, IPs, environment variables, and attack-relevant implementation specifics should stay out of the public site.
