Connection to supported industrial or sensor data sources
Process Industrial Data and AI Workloads Near the Equipment
Connect industrial data sources, prepare information locally, and run supported analytics or AI workloads closer to the machines that generate the data.
EdgeData Processing
AIWorkloads
Near-MachineIntelligence
E1 Edge-AI Gateway
The MayilVeera E1 Edge-AI Gateway is intended for industrial connectivity, local data processing, and deployment of supported analytics or AI workloads at the edge.
It can provide a controlled bridge between equipment-level information and local or cloud applications while reducing the need to send every raw data point or media stream outside the site.
Logical Architecture
A website-level view of how the product connects sources, processing layers, diagnostics, and destination systems.
MachinesSensorsCamerasControllersI/O Modules
01
Industrial/Data Connectors
AcquisitionValidationPreprocessing
Local RulesAnalyticsAI Inference
Local Action / Alert
Local Data Storage
02
Secure Northbound Services
SCADAMESDashboardCloudAPIMaintenance Application
Key Capabilities
Local data preprocessing and filtering
Rules and event processing
Deployment of supported analytics or AI models
Local application and container hosting, if implemented
Local data storage and forwarding, if implemented
Northbound connection to enterprise or cloud applications
Device-health and resource monitoring
Remote configuration and update, where supported
Diagnostic logging
How It Works
The document flow is presented as an implementation-style process so visitors can understand the operating path quickly.
- 01
The E1 Gateway collects data from configured sources, preprocesses it locally, and supplies it to rules, analytics, or compatible AI inference applications. Results may trigger local notifications, be stored for later analysis, or be transmitted to authorised applications.
- 02
Any closed-loop equipment action requires a separate safety and control assessment.
Applications
Where It Fits
Machine-condition analytics
Local anomaly detection
Vision inspection, subject to camera, optics, lighting, model, and validation
Equipment-state classification
Local production-data processing
Energy and utility analytics
Alarm prioritisation
Data reduction before cloud transmission
Remote equipment monitoring
Edge deployment of customer-specific analytics
Benefits
Practical Benefits
Processes selected information close to the source
Reduces unnecessary raw-data transmission
Can continue defined local functions during cloud disconnection
Supports lower-latency local analysis
Separates equipment connectivity from enterprise/cloud applications
Provides a managed location for edge workloads
Integration and Deployment
The design must cover data sources, protocols, AI model, expected input rate, latency, storage, network, application runtime, cybersecurity, environmental conditions, power, and maintenance method.
For vision applications, camera selection, lens, lighting, mounting, field of view, target variation, dataset, model validation, and acceptable false-positive/false-negative rates must be defined.
Reliability, Security and Limitations
Application and device-health monitoring
Controlled software and model updates
Secure device identity
Encrypted management and application interfaces
Role-based access, where implemented
Storage-health monitoring
Recovery and rollback strategy
AI output should not be treated as a certified safety decision
Site-specific performance must be validated with representative data
Need Help Selecting the Right Product?
Equipment and controller make or model
Required interfaces, protocols, channels, or tag count
Operating environment and target platform
Update rate, cycle time, delivery schedule, and custom engineering needs
Do not publish price, availability, certification, warranty, protocol, electrical rating, capacity, performance, or compatibility statements until they are linked to the exact shipping SKU or software version.
Frequently Asked Questions
What AI models can run on E1?
Compatibility depends on the processor or accelerator, framework, operators, memory, model size, precision, and input rate.
Does the gateway guarantee a particular AI accuracy?
No universal accuracy should be claimed. Accuracy depends on the dataset, model, operating conditions, sensors, and acceptance criteria.
Can AI directly stop or control equipment?
Only after a separate control and safety assessment. General-purpose AI inference must not replace certified safety functions.
Validate Your Edge-AI Workload
Share the use case, data or camera inputs, model/framework, expected inference rate, latency, storage, interfaces, and installation environment.
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