Product
From a sentence to a model running on your hardware
LokiAI removes the part of edge AI that stops most teams: choosing a model that will actually load, on a device that will actually run it, without wiring anything up.
- Android agent available
- QR pairing
- GGUF · TFLite
- Local inference
01The journey
One workflow, five inspectable stages
Every stage can be reviewed after the fact. If a deployment failed, the record says which check failed and why.
02Who it is for
Built for people who own hardware, not model formats
Product teams
You have devices in the field and want on-device intelligence without hiring a specialist AI team.
Solo builders
You have one Android phone and an idea, and you want a model on it this afternoon.
Hardware partners
You ship devices and want a deployment story customers can use on day one.
Operations
You need to know exactly which artifact is on which device, and when it changed.
03What is different
Deployment is treated as an engineering record, not a button
Most tooling stops at “here is a model.” LokiAI carries the decision through to a verified artifact on a specific device, and keeps the evidence.
- Compatibility is checked against the reported device profile before download begins.
- Deployments reference a pinned file, so the same deployment repeated gives the same bytes.
- Progress and failures are streamed, not polled from a black box.
- Nothing requires a cable, a developer-mode toggle or a workstation.
- Inference inputs never traverse LokiAI infrastructure.
04What you can do today
Current capability, stated plainly
- Device agent
- Android — available
- Pairing
- QR code over HTTPS/WSS
- Model source
- Hugging Face repositories, verified per file
- Formats
- GGUF, TFLite
- Discovery
- NIM-assisted candidate ranking
- Isolation
- Per-account tenancy, device-scoped credentials
Pair a device and deploy your first model
Start with the Android agent. The workflow is the same for every device class we add.