Industrial AI Deployment Model.
Industrial AI solutions designed to deploy where needed, within operational constraints, without compromising performance, security, or engineering rigor
On-premise industrial AI deployment
Ensure maximum data sovereignty and security.
Deploy AI applications entirely within your internal IT infrastructure to maintain full control over data, models, and system access.
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Ideal for sensitive engineering and manufacturing data, regulated industries, and environments requiring air-gapped or isolated operation.
Edge deployment of industrial AI
Get real-time intelligence close to the process.
Run AI directly at or near machines, sensors, or control systems to enable low-latency analytics and decision support.
- Well-suited for time-critical use cases, bandwidth-constrained environments, and distributed industrial assets. AI model training can be done on-premise or on cloud depending on constraints you have.
Cloud deployment of industrial AI
Benefit from scalable and centralized analytics.
Deploy AI solutions in secure cloud environments to support large-scale data processing, cross-site analytics, and centralized model management.
- Appropriate when data sensitivity and latency constraints allow cloud usage.
Hybrid deployment of industrial AI
Obtain a balanced mix of performance, security, and scalability.
The hybrid AI option combines on-premise, edge, and cloud deployments of AI to match operational needs.
- Sensitive data and real-time analytics remain local while aggregated insights, model training, or fleet-level analysis are handled centrally.