Service pillar 03
Computer Vision, Edge AI & IoT
Why it matters
Physical operations generate more visual and sensor data than any team can watch. Vision and edge AI systems detect what matters as it happens, close to where it happens, without streaming every frame to the cloud.
Business challenges we address
- Inspection and monitoring are manual and inconsistent
- There is no real time visibility of physical operations
- Physical workflows are inefficient and hard to measure
- Incidents are detected late, after the damage is done
- Monitoring costs scale with every camera and site
- Remote locations have limited bandwidth and compute
- Sensing infrastructure is disconnected from business systems
Technical capabilities
What we engineer
Capabilities our teams design, build, and operate within this pillar.
Computer vision
- Object detection
- Object tracking
- Image classification
- Semantic and instance segmentation
- Human activity recognition
- Pose estimation
- Facial and visual attribute analysis where legally appropriate
- Video analytics
- Counting and monitoring
- Industrial visual inspection
Video intelligence
- Real time video analytics
- Multi camera monitoring
- Human and vehicle detection
- Crowd analytics
- Traffic intelligence
- Behavior and activity recognition
- Safety monitoring
- Automated event detection
Edge AI
- NVIDIA Jetson deployment
- Edge inference optimization
- Embedded AI
- Model quantization
- Real time AI processing
- On device intelligence
- Low latency AI applications
IoT and intelligent sensing
- Sensor integration
- IoT device connectivity
- Real time data acquisition
- Intelligent monitoring
- IoT dashboards
- Predictive maintenance
- Smart infrastructure
- Edge to cloud architectures
Robotics and intelligent systems
- Intelligent robotic applications
- Autonomous sensing
- Multi agent systems
- Swarm intelligence
- Vision guided robotics
- Intelligent navigation and monitoring
Typical applications
How organizations use it
Pose and activity analysis
Pose estimation that follows several people at once, for example a group exercise class where each participant's form is compared against a reference and correct and incorrect positions are highlighted.
Visual quality inspection
Detection and segmentation models that flag surface defects or assembly errors on a production line, with images stored for traceability.
Traffic and crowd analytics
Vehicle and pedestrian detection, counting, and flow analysis across multiple cameras, processed at the edge to limit bandwidth.
Predictive maintenance
Vibration, temperature, and current sensors combined with anomaly detection to schedule maintenance before equipment fails.
Business benefits
What changes for your organization
- Earlier detection
- Events are flagged in seconds rather than discovered during the next review.
- Consistent inspection
- Models apply the same criteria on every shift and every unit.
- Lower bandwidth and latency
- Edge inference processes video locally and sends only events and metadata.
- Privacy by design
- Systems can be built to avoid storing identifiable imagery where it is not needed.
Industries and solutions
Where this pillar is applied
Questions
Common questions
What is edge AI?
Edge AI runs model inference on hardware located near the camera or sensor, such as an NVIDIA Jetson module, instead of in a distant data center. It reduces latency and bandwidth, keeps working with intermittent connectivity, and limits how much raw data leaves the site.
Can you work with our existing cameras?
Usually, yes. Most IP cameras that provide RTSP streams can be used. We assess resolution, frame rate, angles, and lighting during discovery, because these determine what a model can reliably detect.
How do you handle privacy in video analytics?
We design for the minimum data needed: processing on the edge, storing events instead of footage, blurring or discarding faces where identity is not required, and following the laws of the jurisdiction where the system operates.
How much training data do we need?
It depends on the task. Common objects can start from pretrained models with modest fine tuning, while specialized defects need labeled examples from your environment. We estimate data needs early and can set up the collection and labeling process.
Other service pillars
AI Engineering & Intelligent Systems
Generative AI, retrieval augmented generation, agentic systems, and machine learning engineered into software that runs reliably in production.
AI Automation & Enterprise Intelligence
Intelligent process automation, AI agents, voice AI, and system integration that remove manual work and connect the way your organization operates.
Build an intelligent vision system.
Share your objectives and constraints. We will come back with a recommended approach, architecture options, and next steps.