What stays on-device
- raw user text, images, audio, and sensor readings
- most inference requests and outputs
- local training batches and personal adaptation data
Octomil is built for teams that want to move AI closer to the device, reduce centralized handling of sensitive content, and review a deployment path with routing, auditability, and access controls in place.
Reducing centralized handling of sensitive data can shrink the surface area you need to justify during security, privacy, and procurement review.
Define when requests stay local, when cloud fallback is allowed, and where additional review is needed for specific workloads or cohorts.
Use organization-scoped access, audit logs, and review-ready documentation to explain who can change deployments and what signals are stored centrally.
Pair this commercial review page with the technical documentation in the security architecture guide for implementation detail and operational posture.
Technical detail on identity, key management, device authentication, and production hardening.
Open technical guide →Review the broader trust center for disclosure process, operating posture, and team contacts.
Open trust center →Use the AI inference cost calculator to model when on-device execution reduces both cost and centralized data exposure.
Estimate AI inference cost →Tell us about your device mix, routing requirements, and review constraints. We’ll help map what stays on-device, what reaches the control plane, and what needs a BAA or security review.