0
0 Edge Cost
Inference runs where the request already is. No per-call edge bill, no GPU fleet to babysit.
The Engines
∞
Continue Learning
The model keeps adapting on live traffic — no retraining sprints, no redeploy windows.
API
On-Cloud API (Learning Engine)
Fleet-wide signals distilled in the cloud and returned as compact weight deltas over one endpoint.
100%
100% On-Device Engine
The full learning engine runs locally on AMD, Intel and Rockchip silicon. Offline capable, zero egress.
Two engines, one contract
Keep the moment local, sharpen the fleet in the cloud.
100% On-Device Learning Engine
- Inference latency
- 11 ms p99
- Cost per 1M calls
- $0.00
- Data residency
- never leaves the device
- Offline
- fully capable
On-Cloud API Learning Engine
- Fleet sync
- 118 ms p99
- Weight delta
- 4.2 KB
- Learning scope
- fleet-wide
- Surface
- one REST endpoint
How it keeps learning
Observe
Every booking, order and search emits a compressed signal at the edge.
Adapt
On-device weights update in place — no batch job, no downtime, no reship.
Sync
Anonymized signals fold into a shared prior and return as tuned deltas.
Put continual learning on your silicon
We'll wire a reference endpoint against one of your integrations and show live latency and cost.