Most AI platforms assume that inference happens in a public cloud. NativelyAI assumes the opposite.
Natively provide open source models where execution occurs inside infrastructure controlled by the user - private servers, enterprise VPCs, or air-gapped environments. Prompts, intermediate representations, and outputs remain within this boundary unless explicitly permitted.
This design removes the tradeoff between AI capability and data protection. Teams do not need to avoid sensitive use cases, redact inputs, or rely on opaque third-party guarantees.
AI becomes usable for high-value workloads precisely because it remains private.
