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Vision AI
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Banking
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Intelligent Capabilities at the Edge
EdgeVision
Capture, analyze, and act on high-resolution video and image feeds in milliseconds—at the source, with no latency or data transfer delays
AutoAlerting
AI agents monitor live streams to instantly identify anomalies, safety violations, or process deviations, triggering alerts when it matters most
VisionPipelines
Run multiple AI tasks—such as object recognition, posture analysis, and zone monitoring, simultaneously using lightweight, containerized models
SmartRouting
Only meaningful insights, events, and metrics are shared upstream, keeping network loads light while ensuring enterprise-wide visibility
Why Our Edge Vision Solution Stands Apart
Designed for real-time environments, our solution delivers low-latency, high-accuracy visual intelligence at the source—enabling instant decision-making without reliance on cloud or data centers
01
Our agents process data on-device, reducing bandwidth and ensuring decisions happen where the action occurs
02
From AI cameras to industrial gateways, our platform supports NVIDIA Jetson, Intel Movidius, ARM Cortex, and other low-power chips
03
Built-in mechanisms ensure raw footage never leaves the device—supporting compliance for GDPR, HIPAA, and other standards
04
Edge devices continuously share learnings and tune their models across a federated network—boosting intelligence without centralizing data
05
Systems remain functional even in disconnected environments—guaranteeing safety, detection, and response without internet dependency
How AI Vision at the Edge Works Across Devices
Modular Agent Deployment
Assign different AI agents for tasks like thermal anomaly detection, human activity recognition, or visual asset tracking—each independently deployable
Self-Improving Vision Models
Feedback from field interactions and incident responses helps agents refine predictions and reduce false positives over time
Decentralized Agent Ecosystem
Agents across various edge devices coordinate via secure channels—enabling swarm intelligence across plants, sites, or vehicles
Unified Insight Layer
A centralized dashboard aggregates events, performance stats, and visual analytics—supporting OT, IT, and AI teams simultaneously
Outcomes Delivered by AI Vision at the Edge
Immediate Insight Generation
From worker safety violations to faulty product detection, catch issues the moment they occur—without waiting for cloud inference
Smarter Use of Network and Storage
Store only critical footage and metadata, dramatically reducing storage needs and network congestion
Faster Decision Loops
Local automation means faster incident response, better uptime, and improved operational control
AI Vision at the Edge – Use Cases Across Industries
Retail
Connected Retail Spaces
Monitor foot traffic, detect suspicious activity, and optimize display zones through real-time edge vision analytics
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Infrastructure
Critical Infrastructure and Utilities
Detect equipment anomalies, intrusions, and site hazards with vision agents deployed on perimeter and asset monitoring systems
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Manufacturing
Industrial and Factory Automation
Empower quality inspection, assembly line optimization, and tool wear detection with real-time camera insights
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Supply Chain
Smart Mobility and Automotive
Support driver assistance systems, cabin monitoring, and external hazard detection in real time with in-vehicle AI vision
Why Choose Our AI Vision at the Edge Platform
Engineered for Rugged Edge Environments
Rugged-ready agents operate in conditions with vibration, dust, and temperature extremes
Toolchain-Friendly Architecture
Plug into existing ML stacks—ONNX, TensorFlow Lite, OpenCV, and Edge Impulse—without re-architecting your system
Custom Vision Workflows
Develop and deploy use case–specific pipelines quickly using our SDKs and pre-trained visual agents
Scalable Intelligence Anywhere
From one edge node to thousands, scale agents horizontally with centralized orchestration and decentralized execution
Operator and Developer Friendly
From intuitive setup to detailed monitoring dashboards, we support both AI engineers and field teams
Competencies
