Solutions are designed to be deployed flexibly across on-premise, edge, and cloud environments, enabling enterprises to integrate AI systems into real-world operations without compromising control, security, or scalability.
Rather than enforcing a single deployment model, we provide an adaptable architecture that allows organizations to determine how data, models, and system components are deployed and managed—based on operational requirements, IT policies, and regulatory constraints.
Deployment supports long-term operation in complex industrial and enterprise environments where reliability, auditability, and governance are critical.
AI inference, learning workflows, and system updates can be configured to operate locally, at the edge, or through controlled cloud components, ensuring enterprises retain authority over system behavior while maintaining the ability to scale across sites and use cases.
Memorence AI supports cloud-based deployment architectures for centralized management, system coordination, and data aggregation.
Cloud deployment can be configured as private cloud or hybrid cloud environments, allowing enterprises to balance scalability with data governance and security requirements.
Depending on operational needs, cloud components may be used for model management, system monitoring, or cross-site coordination, while inference and sensitive data processing remain on-premise or at the edge.
Our solutions can be deployed entirely within the customer’s local environment, including on-site servers, industrial PCs, and production systems.
This deployment mode is suitable for applications requiring strict data isolation, low latency, or compliance with internal IT and OT governance policies.
All data, AI models, and system operations remain fully under customer control.
For real-time and low-latency applications, we supports edge deployment integrated with cameras, inspection systems, and production equipment.
AI models can be deployed on industrial PCs or edge computing devices, enabling stable operation even in environments with limited or no network connectivity.
Edge deployment ensures responsive performance for inspection, operational guidance, and safety-related scenarios.
Model updates and learning workflows can be managed locally or through controlled update mechanisms, depending on the selected deployment configuration.
Enterprises retain authority over when and how models are updated, ensuring operational stability, auditability, and controlled system evolution.
This approach supports both continuous learning and instant learning workflows without disrupting production operations.
Deployment options are designed to align with enterprise security policies and industrial governance requirements.
System access, data storage, and update processes can be configured to meet internal IT and OT standards, supporting secure operation across distributed environments.
Our deployment is not constrained to a single architecture.
Systems can be deployed independently or combined across on-premise, edge, and cloud environments to meet evolving enterprise needs.
Suitable for manufacturing site intensive real-time AI processing.
Deployable product: Memorence Suite, DataEagle, XMemor
Suitable for remote or light-weight applications.
Deployable product: Memorence Suite inference engine
Suitable for remote or light-weight applications.
Deployable product: Memorence Suite inference engine
Suitable for remote, light-weight, no-WIFI applications.
Deployable product: Memorence Suite inference engine
Suitable for human hand-held applications.
Deployable product: Memorence Suite inference engine
Suitable for human wear supporting applications. For erxample, helmet, smart glasses
Deployable product: Memorence Suite inference engine
Suitable for remote inspection applications.
Deployable product: Memorence Suite inference engine
Suitable for inspection applications on robot arms.
Deployable product: Memorence Suite inference engine