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BREAKING DOWN OUR AI

Here’s how the process behind the world’s most powerful packaging management solution looks like

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01

Data Pre-processing

Introduce utility and efficiency to raw data

02

ManageArtworks AI Framework

Deployment of AI for Image and Text Processing which is a hybrid approach

03

Feedback Mechanism

Error Analysis and feedback loop for accuracy improvement

Data Pre-processing

Introduce utility and efficiency to raw data

Data Curation

Organizes and annotates data from various sources such as Artworks, Pack inserts, Briefs, and Specification Documents

Data Segregation

Provides types of data and tasks are identified automatically and segregated for analysis

Data Augmentation

Provides context-based high-fidelity computer generated examples and augments existing samples

ManageArtworks AI Framework

Deployment of AI for Image and Text Processing which is a hybrid approach

Image Data

Image pre-processing, context based object detection, feature extraction from custom trained models and cognitive services (Azure, Google)

Text Data

Text pre-processing, representation learning from custom trained NLP models and machine learning services (Google)

Artwork domain context

Models based on regulatory inputs (FALCPA, FSSAI, FDA, USDA,  GS1)

Feedback Mechanism

Error Analysis and feedback loop for accuracy improvement

Error Analysis

Partial automation of error analysis by understanding  false positives and mis-detections based on artwork context

Feedback loop

Loop back error analysis inputs by re-training AI framework to improve accuracy across both image and text data