Automatic & Semi-Automatic AI Annotations

Accelerate Your AI Training with SOTA Models

Up to 10x Faster Annotations by leveraging State-of-The-Art (SOTA) AI models.

We accelerate your data annotation workflow using cutting-edge automatic and semi-automatic annotation technologies.
Our SOTA AI models include SAM 2, YOLOv11, Florence-2, GroundingDINO, DEVA, RT-DETR, InternVL, SEEM and more.

Semi-automatic cow annotation demonstration
Semi-automatic car annotation demonstration

Why Choose Our Automatic Annotation Services?

10x Faster Processing

Dramatically reduce annotation time while maintaining high accuracy

Cost Effective

Significantly lower costs compared to manual annotation processes

SOTA Technology

Using the latest AI models for maximum accuracy and efficiency

Our Automatic Annotation Process

We leverage State-of-The-Art AI models to accelerate your data annotation workflow. Our process combines the power of automation with human expertise to deliver high-quality annotations efficiently.

Collaborative Model Development Approach

1Initial Batch & Delivery

We start with a small batch of high-quality manual annotations and deliver them to establish baseline accuracy standards.

2Customer Model Development

Using our initial annotations, you build and train your custom AI model tailored to your specific use case and requirements.

3Semi-Automatic Processing

We integrate your custom model into our workflow, using it for semi-automatic annotations with manual quality control and corrections.

4Iterative Improvement

We repeat steps 1-3, continuously improving model performance and annotation accuracy through iterative refinement.

5Final Delivery

Complete delivery of high-quality, accurately annotated datasets ready for your AI model training and deployment.

Supported Data Types

  • Image Annotation
  • Video Annotation
  • Audio Annotation
  • Text Annotation
  • 3D Point Cloud Annotation

SOTA Models We Use

SAM 2 YOLOv11 Florence-2 GroundingDINO DEVA RT-DETR InternVL SEEM

What Are Semi-Automatic Annotations?

The Perfect Balance of AI and Human Expertise

Semi-automatic annotations represent the optimal approach to data labeling, combining the speed and efficiency of AI models with the accuracy and quality control of human expertise. This hybrid methodology delivers the best of both worlds - dramatically reduced annotation time while maintaining the highest quality standards.

How Semi-Automatic Annotations Work:
Step 1: AI Pre-Processing

Our SOTA AI models automatically generate initial annotations across your dataset, providing a strong foundation with 85-95% accuracy.

Step 2: Human Review

Expert annotators review AI-generated annotations, correcting errors, refining boundaries, and ensuring quality standards are met.

Step 3: Iterative Refinement

Feedback from human corrections continuously improves AI model performance, creating a learning loop that enhances accuracy over time.

Step 4: Quality Assurance

Final quality checks ensure 99%+ accuracy before delivery, meeting the highest standards for production AI training datasets.

Key Advantages of Semi-Automatic Annotations:
  • Optimal Speed: Up to 10x faster than pure manual annotation while maintaining quality
  • Superior Accuracy: Human oversight ensures 99%+ accuracy for production datasets
  • Cost Effective: Significant cost savings compared to fully manual approaches
  • Scalable: Handles large datasets efficiently while maintaining consistent quality
  • Adaptable: Works across all data types and annotation tasks
  • Continuous Improvement: AI models get better with each iteration
When to Choose Semi-Automatic
Production Datasets

When you need the highest accuracy for training production AI models

Complex Annotations

For intricate labeling tasks requiring human judgment and expertise

Quality Critical Projects

When annotation errors could significantly impact model performance

Large Scale Projects

For datasets too large for manual annotation but requiring high accuracy

Accuracy Comparison
Automatic: 85-95%
Semi-Automatic: 99%+
Manual: 99%+
Real-World Applications
Autonomous Vehicles

Object detection and semantic segmentation for self-driving cars

Medical Imaging

Precise annotation of medical scans and diagnostic images

Retail & E-commerce

Product categorization and visual search optimization

Security & Surveillance

Person and object tracking in security applications

Frequently Asked Questions

Automatic annotations are generated entirely by AI models without human intervention, ideal for large-scale data processing. Semi-automatic annotations combine AI-generated annotations with human review and correction, ensuring higher accuracy while maintaining speed benefits.
Our automatic annotations achieve 85-95% accuracy depending on the data type and complexity. We always recommend semi-automatic annotation for production datasets, where our expert annotators review and correct AI-generated annotations to achieve 99%+ accuracy.
We use cutting-edge SOTA models including SAM 2 (Segment Anything Model 2), YOLOv11 for object detection, Florence-2 for unified vision-language understanding, GroundingDINO for open-vocabulary detection, DEVA for video object segmentation, RT-DETR for real-time detection, InternVL for multimodal understanding, and SEEM for interactive segmentation.
Yes! We specialize in collaborative model development. We start with initial manual annotations, help you build your custom model, then integrate it into our semi-automatic workflow. This iterative process continuously improves both annotation quality and model performance.
We support automatic and semi-automatic annotation for images, videos, audio files, text documents, and 3D point cloud data. Our services cover object detection, semantic segmentation, instance segmentation, classification, transcription, NER, and more.
Our automatic annotation services can reduce annotation time by up to 90% and costs by 60-80% compared to purely manual annotation. We use our in-house GPUs to run the inference, which significantly cuts down customer costs by eliminating the need for expensive GPU infrastructure. The exact savings depend on your data complexity, annotation requirements, and quality standards.
No, you don't need any GPU infrastructure. We use our powerful in-house GPU cluster to run all AI model inference for automatic annotations. This eliminates your need to invest in expensive hardware, reduces operational costs, and ensures optimal performance with the latest GPU technology for all SOTA models.

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