Ailee

Vision Intelligence for Non-Woven Bag Quality Control

year

2026

industry

INDUSTRIAL INTELLIGENCE

Client

Araliya Packaging Lanka (Pvt) Ltd

AI-powered strategic planning system developed by Xleron

Overview

We developed Ailee, a high-speed bag inspection system designed to identify more than 100 types of defects across 150+ bag designs. Using dual industrial cameras, it inspects up to four bags per second, identifies faulty products, and automatically separates defective bags without slowing down production. Ailee combines precise visual inspection, automatic defect sorting, flexible pattern recognition, and real-time production monitoring in one system. It helps packaging manufacturers reduce manual inspection, improve quality control, minimize errors, and maintain consistent product standards.

CHALLENGES

Manual inspection couldn't scale with production.

With production lines running past 1 bag per second, Araliya's manual quality process was already stretched thin, and it wasn't going to get easier as the catalog kept growing.

Slow Inspection Rates

Manual inspection took 10–15 seconds per bag, creating a hard bottleneck well below the line's actual output.

Labor-Intensive Process

A dedicated team of 15 staff was needed around the clock, driving up cost and exposing the process to fatigue-based errors.

Design & Defect Variability

Over 150 bag designs and 100+ defect categories, from print misalignment to fabric shading, made consistent manual inspection difficult to sustain.



150+

150+

Unique bag designs in production

100+

Known defect categories

15

Staff required for round-the-clock manual inspection

10–15 sec

Manual inspection time per bag, before Ailee

The Approach

Match the hardware to the line speed, and let the software adapt.

Ailee started from a hardware constraint: to inspect at the line's real speed, the system needed to capture both sides of four bags at once, 8 images per cycle, using synchronized high-speed cameras, custom lighting, and precision mechanical feeding.

The system was designed to adapt to changing bag designs without needing to retrain the entire model each time. Its two-stage process first detects unusual patterns, then identifies specific defects. This allows new product designs to be added more easily while keeping the inspection process fast, flexible, and ready to grow alongside the manufacturer’s expanding product range.



Match the hardware to the line speed, and let the software adapt.

Ailee started from a hardware constraint: to inspect at the line's real speed, the system needed to capture both sides of four bags at once, 8 images per cycle, using synchronized high-speed cameras, custom lighting, and precision mechanical feeding.

The system was designed to adapt to changing bag designs without needing to retrain the entire model each time. Its two-stage process first detects unusual patterns, then identifies specific defects. This allows new product designs to be added more easily while keeping the inspection process fast, flexible, and ready to grow alongside the manufacturer’s expanding product range.



Custom enterprise software and AI automation dashboard
Custom enterprise software and AI automation dashboard
Custom enterprise software and AI automation dashboard

Core Capabilities

01  Dual-Camera Imaging & Precision Mechanics

Captures front and back images of 4 bags simultaneously (8 images per cycle) using custom LED lighting tuned for matte, non-woven surfaces.

02  Two-Stage AI Pipeline

Anomaly detection (PaDiM) flags deviations from a reference design; Vision Transformer classification then categorizes the specific defect type.

03  Real-Time Sorting & Actuation

Pneumatic diverters route defective bags off the line within milliseconds, with a fail-safe default to reject on any uncertain read.

04  Modular Admin Interface

Lets operators register new bag designs, review flagged defects, and monitor system health without engineering support.

Core Capabilities

01  Dual-Camera Imaging & Precision Mechanics

Captures front and back images of 4 bags simultaneously (8 images per cycle) using custom LED lighting tuned for matte, non-woven surfaces.

02  Two-Stage AI Pipeline

Anomaly detection (PaDiM) flags deviations from a reference design; Vision Transformer classification then categorizes the specific defect type.

03  Real-Time Sorting & Actuation

Pneumatic diverters route defective bags off the line within milliseconds, with a fail-safe default to reject on any uncertain read.

04  Modular Admin Interface

Lets operators register new bag designs, review flagged defects, and monitor system health without engineering support.

Core Capabilities

01  Dual-Camera Imaging & Precision Mechanics

Captures front and back images of 4 bags simultaneously (8 images per cycle) using custom LED lighting tuned for matte, non-woven surfaces.

02  Two-Stage AI Pipeline

Anomaly detection (PaDiM) flags deviations from a reference design; Vision Transformer classification then categorizes the specific defect type.

03  Real-Time Sorting & Actuation

Pneumatic diverters route defective bags off the line within milliseconds, with a fail-safe default to reject on any uncertain read.

04  Modular Admin Interface

Lets operators register new bag designs, review flagged defects, and monitor system health without engineering support.

THE RESULTS

14,400/hr

14,400/hr

Bags inspected per hour (4/sec sustained)

Bags inspected per hour (4/sec sustained)

>95%

>95%

Defect detection and classification accuracy

Defect detection and classification accuracy

80%

80%

Reduction in manual quality inspection labor

Reduction in manual quality inspection labor

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