AI that keeps up with changing waste streams
Packaging changes. Materials evolve. Your AI should too. Build waste sorting models that continuously learn and adapt.
Your training data is already outdated
Static models don't work in waste management. New packaging hits shelves every week. Seasonal products come and go. Brand redesigns happen overnight. Your sorting AI needs to adapt — or accuracy plummets.
New packaging every week
Brands redesign, seasonal editions launch, new materials enter the market. Your model trained on last year's packaging is already outdated.
Regional variations
Products in France differ from Germany. Local brands, different recycling symbols, varying material compositions across regions.
Mixed & damaged items
Crushed cans, torn labels, multi-layer packaging. Real-world waste looks nothing like pristine training images.
Model drift over time
A model that worked great in January underperforms by June. Without monitoring, you won't know until sorting quality drops.
Continuous learning, not one-time training
Picsellia creates a closed loop: monitor production, catch drift, retrain, and redeploy — automatically.
Deploy & Monitor
Models run on your sorting line. Every prediction is logged with confidence scores.
Detect Drift
When confidence drops or new item types appear, the system flags them automatically.
Smart Labeling
Low-confidence samples are routed to human reviewers. AI pre-labels, experts validate.
Retrain & Redeploy
Updated models are trained on new data and deployed without downtime.
Built for sorting facilities
From MRFs to collection points, Picsellia powers waste classification across the recycling chain.
Automated Waste Sorting
Classify materials on conveyor belts in real-time. PET, HDPE, cardboard, aluminum — your model learns to distinguish them all, even as packaging evolves.
Contamination Detection
Spot contaminated recyclables before they ruin an entire batch. Identify food residue, mixed materials, and non-recyclable items in the stream.
Fill Level Monitoring
Track container fill levels across your network. Optimize collection routes based on actual need, not fixed schedules.
Material Composition
Analyze waste streams to understand what's coming through. Generate reports for regulatory compliance and recycling optimization.
Tools for continuous improvement
Every feature in Picsellia is designed for iteration. Collect new samples, retrain fast, and push to production.
Model Monitoring
Catch drift before it hurts
Track prediction confidence in real-time. When your model sees packaging it wasn't trained on, you'll know immediately — not weeks later when sorting quality drops.
Active Learning
Label smarter, not harder
Low-confidence predictions are automatically queued for review. Your team labels only the samples that matter — the edge cases your model struggles with.
Automated Pipelines
Retrain on schedule or trigger
Set up pipelines that retrain models weekly, or trigger retraining when enough new samples accumulate. Deploy updated models without stopping your line.
The result? Models that never go stale.
Keep 95%+ accuracy year-round, even as packaging changes weekly.
Connects to your sorting infrastructure
Picsellia integrates with existing camera systems, conveyor controls, and sorting robots. Deploy models to edge devices or process in the cloud — your choice.
Camera Integration
Works with industrial cameras, line-scan sensors, and hyperspectral imaging
Edge Deployment
Run inference on NVIDIA Jetson, industrial PCs, or embedded systems
PLC & SCADA
Send classification signals to sorting actuators and robotics
Data Pipelines
Automatic upload of production images for retraining datasets
MES Integration
Feed classification data into manufacturing execution systems
Edge Performance
Optimized for industrial throughput
Ready to future-proof your sorting AI?
See how Picsellia keeps waste classification accurate — even as packaging evolves every week.