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Sort products by type or grade, count items accurately, and support dimensional decisions with computer vision.
This application is built for manufacturers searching for image classification in production, automated counting systems, or AI-based quality sorting that can distinguish product types, grades, and countable features with high consistency.
Built For Production
Tuned for real line conditions, traceability requirements, and fast operator decisions.
Search Intent
Visual Classification And Counting
System Compatibility
Designed for production lines where vision has to fit into existing cameras, PLCs, controllers, and factory automation standards without forcing a complete hardware reset.
System Compatibility
Built to work with common machine vision camera ecosystems used across inspection and automation deployments.
Basler
Allied Vision
Baumer
Hikvision99%+
Sorting accuracy on stable trained classes
Real-time
Classification and counting decisions
Multi-class
Support for variants and grades
Scalable
Easy rollout across multiple lines
Production Challenge
Teams usually reach this application when operators are manually sorting parts, counting items in trays, or checking product variants under time pressure. Small counting mistakes or wrong-grade shipments can create inventory problems, rework, and customer dissatisfaction very quickly.
Solution Approach
Orama combines classification models, detection-based counting, and configurable decision logic so the same application can identify what the product is, how many objects are present, and whether the observed condition matches the expected production rule.
Typical Project Flow
Capabilities
Deployment Workflow
Use Cases
Business Outcomes
Frequently Asked Questions
Yes. We often combine these functions in the same workflow, for example to identify a product variant first and then verify whether the expected number of components or items is present.
It can be, but overlapping objects require the right imaging strategy and sometimes instance-level detection rather than simple classification. We evaluate the scene first to choose the right approach.
Yes. That is one of the main advantages of using an AI vision workflow instead of static hard-coded inspection logic. New variants and edge cases can be added through additional training cycles.
For strict tolerance measurement, dedicated metrology hardware may still be required. Vision classification and counting are strongest when the business need is fast operational decision-making rather than laboratory-grade measurement.
Next Step
We can adapt the same platform for your product geometry, lighting, line speed, traceability rules, and reporting needs without forcing your team into a generic workflow.
Project Fit