Private and edge AI compute for enterprises that can't — or won't — send their data to a distant cloud. INDORCE engineers the hardware and runs the infrastructure, so you get one accountable partner from the chip to the deployment.
Most vendors either build hardware or rent compute. We do both — on the same engineering team. That means your edge devices, your private cluster, and the models running on them are designed to work together, supported by people who understand the whole stack.
On-premises and air-gapped AI clusters for organizations that need full control of their data and models.
Inference on the factory floor, in the vehicle, or at the kiosk — on the rugged platforms we design ourselves.
On-demand GPU clusters in our own facilities — with regional data residency you can point to.
Build, deploy, and observe AI agents on infrastructure you control, with access and audit built in.
Bare-metal and on-demand compute instances for training and inference, sized to your workload.
From model selection to deployment architecture — advisory from a team that ships AI hardware in production.
With private or edge deployment, models run inside your own boundary. Sensitive data — production images, patient records, operational telemetry — is processed locally and never crosses to an external provider. You decide what, if anything, ever leaves.
The same team designs the device, runs the compute, and supports the deployment — no finger-pointing between vendors.
Private and edge options keep sensitive data inside your walls. Built for industries where sovereignty isn't optional.
Japanese-language sales and support, sized for small and mid-sized enterprises — not just global accounts.
Run the open models you choose, scale on your terms, and avoid the runaway metering of hyperscaler AI.
Tell us your constraints. We'll propose the deployment model that fits. 日本語でのご相談も承ります。
The AI Compute section explains Edge AI devices, private on-premises infrastructure and GPU cloud deployment approaches. Available configurations are determined through project review.
Edge AI runs inference at or near the data source. Private on-premises computing provides dedicated infrastructure at a facility. Workload, latency, connectivity, security and capacity requirements drive the choice.
No. Depending on the selected architecture, inference can run locally or on private infrastructure. Specific data flows and controls should be confirmed during solution design.