Latent AI
Edge model optimization
Optimize, compile, and package models for constrained edge hardware.
SUNet use-case mapping
Use caseHow it supports itBenefit
Super Intelligence developmentCompiles and quantizes trained models for target edge hardwareShorter path from trained model to deployable artifact
ExperimentationPackages model variants for side-by-side hardware trialsFaster comparison of accuracy and latency tradeoffs
Mission partner integrationProduces portable packages partners can run on their own devicesConsistent artifacts across partner hardware
Maturity (TRL)
TRL 7 to 9 (vendor-reported range)
Deployment envelope
Cloud, On-premises, Air-gapped, Local-first, DDIL