Introduction
High-quality AI depends on high-quality annotated data. But how does raw data turn into structured, labeled datasets ready for machine learning? At [Your Company Name], we provide a seamless end-to-end annotation workflow — from project creation with datasets to final export.

Here’s how it works:

🔹 Step 1: Create a Project & Add Your Dataset

The workflow begins by creating a new annotation project.

• Upload or link your dataset directly within the project setup.

• Add multiple datasets if needed for comparison or multi-source training.

• Ensure secure and organized dataset management right from the start.

🔹 Step 2: Define Classes & Annotation Types

During project creation, you can also set up the annotation schema:

• Define class labels (e.g., Car, Person, Building, Tumor).

• Choose annotation types such as bounding boxes, polygons, keypoints, or segmentation masks.

• Standardizing classes at this stage ensures annotation consistency.

🔹 Step 3: Assign Annotators & Reviewers

Once the project is ready, tasks are distributed:

• Annotators label the images or scans.

• Reviewers validate annotations for quality.

• Role-based workflows ensure accountability and collaboration.

🔹 Step 4: Annotate with AI-Assist + Human Precision

This is where the real annotation happens:

• AI-Assist tools pre-label objects, speeding up repetitive tasks.

• Annotators refine results with manual adjustments for accuracy.

• Works across all domains: medical scans, satellite images, LiDAR point clouds, and general images.

🔹 Step 5: Review & Quality Assurance

Before finalizing, annotations pass through review:

• Reviewers check for label accuracy and consistency.

• Built-in validation metrics (e.g., IoU, Dice coefficient) can highlight discrepancies.

• Feedback loops ensure continuous improvement.

🔹 Step 6: Export the Dataset

The final output is an AI-ready dataset, exported in your required format:

• Supported formats: COCO, YOLO, Pascal VOC, JSON, and more.

• Flexible export options allow selecting full or partial datasets.

• Seamless integration into training pipelines accelerates AI model development.

Conclusion
From project creation to dataset export, our platform streamlines the annotation workflow into a single, efficient process. With AI-Assist, collaborative roles, and flexible export options, you can build high-quality datasets faster and at scale — ready to power the next generation of AI.