01
Data movement
Sending proprietary data to third-party APIs can expand your security, residency, and governance surface.
Private AI infrastructure
Run vision AI on your terms
From edge devices to GPU clusters, run AI on infrastructure selected around your data, security, and scale requirements.
What cloud-only processing changes
Cloud-only AI can create tradeoffs in privacy, control, resilience, and long-term economics.
02
Provider dependence
Relying on external providers introduces service availability, rate-limit, and vendor-governance dependencies.
03
Usage costs
Usage-based pricing can become difficult to forecast as workloads and retention requirements grow.
04
Governance work
Data protection, residency, and industry obligations require deliberate architecture and documented controls.
Three places the workload can run
Three deployment layers that can be combined around the camera estate, data boundaries, and operating workflow.
01
Edge systems
Run supported inference near the cameras when site-level response, connectivity, or bandwidth shapes the design.
- On-premise camera and sensor processing
- NVIDIA Jetson or other qualified edge systems
- Local inference sized to the workflow
02
Private GPU infrastructure
Use dedicated compute in a controlled environment when governance, integration, or capacity requirements call for it.
- On-premise or private-hosted GPU infrastructure
- Controlled data and model handling
- Capacity sized to streams and supported workloads
03
Hybrid deployment
Combine edge, on-premise, and cloud services where each is appropriate for the workflow.
- Defined software and model lifecycle
- Site-to-central coordination where configured
- Health and event visibility across integrated systems
Process
From assessment to production
A practical deployment path from infrastructure assessment through production operations.
Assess and design
We review infrastructure, data flows, and the target workflow to recommend a testable deployment topology.Deploy infrastructure
Provision the agreed compute, network paths, stream access, and supported models within the project scope.Operate and scale
Define monitoring, update, support, and capacity responsibilities before the deployment expands.
Private AI across industries
Deployment patterns for camera-driven workflows with deliberate processing, access, and retention boundaries.
01
Retail analytics
Footfall, flow, and loss-prevention workflows with processing placed around site requirements.
02
Video intelligence
Intelligent video analytics with local-processing options and configurable alert workflows.
03
Logistics operations
Yard, bay, queue, and asset-flow analysis on infrastructure matched to the site.
04
Banking and controlled access
Branch, ATM, and restricted-area video workflows with controlled retention and access.
05
Industrial operations
Configured safety, zone, and process-monitoring workflows near the physical operation.
Built around your environment
Deployment choices for teams that need control over processing location, integrations, and operations.
01
Keep processing in your environment
Choose an on-premise design that keeps video processing within your controlled infrastructure.
02
Reduce external dependencies
Run core inference on infrastructure you control and integrate external services only where your architecture requires them.
03
Support data-protection controls
Design for data residency, access control, auditability, and privacy workflows. Compliance remains specific to each deployment.
04
Infrastructure through workflow
Coordinate qualified compute, camera streams, supported models, and operational integrations within an agreed deployment scope.
Choose where your AI runs
Build an AI stack around infrastructure you control, with on-premise processing and integrations chosen for your security and scale requirements.