Data Annotation That Feeds AI Success

A machine learning model is only as good as the data it's trained on — and the quality of that data depends on how it's labeled. At WosoM, we specialize in data annotation workflows that strike a balance between automation speed and human precision.

1. Hybrid Annotation Strategy

We use AI-assisted tools to accelerate labeling — bounding boxes, segmentation, NER, OCR overlays — followed by human-in-the-loop verification. This reduces cost while maintaining high-quality ground truth labels.

2. Custom Taxonomies & Guidelines

We don't use generic labels. Every project begins with a tailored taxonomy and annotation manual co-created with the client. This includes edge case handling, label hierarchy, and context rules.

• Named entity disambiguation
• Domain-specific tagging (e.g., legal clauses, medical symptoms)
• Multi-label and span overlaps

3. QA and Reviewer Layer

Every label goes through at least two passes — annotator and reviewer. We also apply programmatic checks (consistency, density, agreement scores) before shipping final datasets.

{
  "text": "Patient has persistent cough and fatigue.",
  "entities": [
    {"label": "symptom", "start": 14, "end": 29},
    {"label": "symptom", "start": 34, "end": 41}
  ]
}

4. Supported Formats & Tools

We support industry-standard formats (COCO, Pascal VOC, spaCy, CSV, JSONL) and tools like Label Studio, CVAT, and Roboflow. Need integration with your system? We’ll build a plugin for it.

“A 95% accurate label set is good. A 99% consistent one — that’s production-grade.”

“Annotation isn’t just labeling — it’s knowledge encoding.”