Category: End to End Workflow

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.