Live stream analysis
Processes continuous video from cameras watching flowing material or live production operations — generating per-frame analytics at industrial throughput without stopping the line.
Process & Video Analytics
Turn a live camera feed into a quality report
A camera watches your process — grain flowing over a tray, product moving under a light source, material passing through a line. AI analyses the live stream in real time and outputs grades, defect classifications, and batch reports automatically.
Not part inspection — process inspection
The camera watches the process, not a single part
Traditional machine vision looks at one part at a time — present or absent, pass or fail. Process & Video Analytics is different: the camera watches a continuous stream of material or an ongoing operation, and the AI produces analytics from everything it sees across time.
In the rice mill application, for instance, a camera mounted above a vibrating tray tracks every grain as it passes — measuring length, width, chalkiness, and colour on each one, classifying it as whole, broken, chalky, or discoloured, and generating a graded batch report at the end of the run. The mill operator gets objective quality data from every sample, not just a technician's estimate from a handful.
How it works
From live feed to quality report in real time
01
Camera watches the process
A camera is positioned over the material stream or production operation — grains on a vibrating tray, product moving under a light source, or a line being assembled.
02
AI analyses every frame
Deep learning models process the live feed in real time — detecting, segmenting, and classifying each unit or event as it appears, at the throughput the process runs at.
03
Analytics surface instantly
Quality grades, defect counts, and pass/fail verdicts are available immediately — on screen, fed to an MES, or compiled into a report — without waiting for a manual tally.
Capabilities
What this solution delivers
Multi-class quality grading
Classifies material attributes — grade, defect type, dimension, colour — on every unit as it appears in the frame. No sampling, no manual scoring, no subjective variance between operators.
Automated batch reporting
Generates per-batch, per-machine, and shift-level quality reports automatically — replacing manual inspection records with objective, timestamped data tied to every run.
Typical applications
Where it's used
Grain & seed quality grading
Food & agriculture material inspection
Pharmaceutical process monitoring
Packaging integrity & fill-level checks
Production line compliance monitoring
Real-time quality dashboards & MES integration
Proven in production
Case studies & app notes
Case studies
FMCGCASE STUDYAI-Powered OCR for Cigarette Packet Data Extraction
May 4, 2026READ →
FMCGCASE STUDYAI-Powered Grain Analyzer for Rice Mills
May 4, 2026READ →
App notes
SAFETYAPP NOTEReal-Time Video Analytics for Manufacturing Safety & Line Efficiency
AI-powered real-time video analytics for PPE compliance, zone intrusion detection, and line-efficiency monitoring — using your existing camera infrastructure.
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AUTOMOTIVEAPP NOTEPiston & Con-Rod Assembly Verification
4-camera inline assembly verification confirming circlip seating, ring presence, gudgeon pin depth, and crown orientation on every piston — within a 500ms index stop.
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FMCGAPP NOTEAI-Powered Cashew Kernel Inspection & Grading
Three-station AI vision architecture covering morphometry, multi-spectrum colour grading, and deep learning classification across all 33 export grades — with per-kernel digital traceability.
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AGRICULTUREAPP NOTEAI-Powered Seed Quality Inspection & Analysis
Multi-modal RGB + NIR hyperspectral + deep learning inspection for seed purity, viability, defect detection, and lot-level traceability — at up to 1,200 seeds per second.
READ →
SAFETYAPP NOTEAI Vision Monitoring for Fabrication Workshop Safety & Quality
Unified AI vision platform covering PPE compliance, zone intrusion, weld defect detection, and cut quality assessment — all from existing cameras, with < 3 second alert latency.
READ →
Have a similar application?
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