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GENIE: Intelligent Data Curation & Auto-Labeling for Efficient Data Labeling

Accelerating AI Training with Smart Data Labeling & Active Learning

Unlock the Unpredictable World

In the rapidly evolving landscape of machine learning (ML) and artificial intelligence (AI), GENIE stands as a transformative solution, designed by AvaWatz to streamline and optimize data preparation for AI training. By automating data labeling, active learning, and synthetic data generation, GENIE reduces manual effort, enhances dataset quality, and ensures AI models are trained on balanced, diverse, and high-fidelity data. This results in more accurate, reliable, and trustworthy ML models—significantly accelerating development cycles while improving performance across real-world applications.

Intelligent Data Processing Features

Active learning to prioritize critical data slices.

Automatic Labeling with Human Verification

Multi-Model Consensus & Weak Supervision

Synthetic Data Augmentation

Targeted Learning for Rare and Long-tail Classes

Data Filtering & Relevant Data Selection

CREATE HIGH CONFIDENCE AI MODELS

Relentless Refinement

Relentless Refinement

Selects uncertain and erroneous examples iteratively, ensuring your models learn from the most challenging scenarios.

Swift Automation

Swift Automation

GENIE performs automatic labeling and synthesis of examples, streamlining the process and boosting efficiency.

Precision Enhancement

Precision Enhancement

By targeting rare and complex data slices and classes, GENIE elevates accuracy to new heights.

Case Study 1

Enhancing Rare Class Detection for Autonomous Driving

Problem
Autonomous driving AI models struggle with detecting rare objects like motorcyclists at night or emergency vehicles in poor lighting due to imbalanced datasets
Solution
GENIE's active learning and synthetic data generation prioritized rare event samples, enhancing dataset diversity and reducing labeling effort.
Results
Model Performance: Achieved high accuracy with Mean Average Precision over 80% for FOD detection and classification.
  • 45% improvement in rare-class detection
  • Better performance in low-light and complex scenarios
  • Faster AI retraining with minimal manual labeling
Case Study 2

Improving Baggage Detection in Homeland Security

Problem
AI-powered CT baggage screening missed rare, high-risk objects due to limited labeled data.
Solution
GENIE automated data selection and weak supervision, ensuring better coverage of concealed weapons and prohibited items.
Results
Model Performance: Achieved high accuracy with Mean Average Precision over 80% for FOD detection and classification.
  • 30% increase in detection accuracy for rare threats
  • Lower false negatives, improving security screening
  • Faster AI retraining with automated dataset curation
Case Study 3

Detecting Debris on Runways for Airport Safety

Problem
Runway debris detection AI models often miss smaller objects, leading to safety risks and costly damages.
Solution
GENIE's targeted selection and synthetic augmentation improved AI's ability to recognize small debris across varying lighting and weather conditions.
Results
Model Performance: Achieved high accuracy with Mean Average Precision over 80% for FOD detection and classification.
  • 60% reduction in false negatives for debris detection
  • AI adapted to changing weather conditions
  • Faster, automated AI retraining for improved safety
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