Data Labeling & Annotation

High-Precision Annotations for High-Performance AI

At Digital Bricks, we deliver accurate, scalable, and context-aware data labeling services to help you train and fine-tune reliable AI systems. Whether you're building models for vision, language, or tabular tasks, high-quality annotated data is the foundation.

We work with organizations to transform raw, unlabeled data into machine-learning-ready datasets—tagged, structured, and aligned with your domain.

Why It Matters

No model performs well without the right training data. Poorly labeled data introduces:

• Noise and confusion into the training process
• Misclassifications and inconsistent model behavior
• Reduced accuracy, especially in edge cases
• Bias or gaps in data representation

Precise annotation directly impacts your model’s ability to generalize, adapt, and perform reliably in production.

What We Do

We offer full-service data labeling pipelines, tailored to your data type, model architecture, and task requirements.

1. Task Definition & Ontology Design

We collaborate with you to define the labeling schema, taxonomy, and annotation instructions. This includes:

• Class labels and hierarchies
• Annotation types (bounding boxes, polygons, entities, sentiment, etc.)
• Edge case handling guidelines
• Quality thresholds and validation logic

2. Multi-Modal Labeling

We support a wide range of data modalities:

• Image & Video: object detection, segmentation, classification
• Text & Documents: NER, sentiment, keyphrase tagging, language classification
• Audio: speech tagging, transcription segmentation
• Tabular/Structured Data: label engineering and feature annotation for supervised ML

3. Tooling & Platform Integration

We use both custom pipelines and enterprise-grade tools (Labelbox, CVAT, Azure Machine Teaching, Doccano) depending on scale and security requirements.

We also support human-in-the-loop workflows, version-controlled datasets, and direct integration with MLOps stacks (Azure ML, Databricks, Hugging Face datasets).

4. Quality Control & Iteration

Labeling accuracy is monitored via:

• Inter-annotator agreement scoring
• Gold standard validation sets
• Automated consistency checks
• Human review with issue tagging

What You Get

• Clean, annotated datasets in standard formats (COCO, Pascal VOC, JSONL, CSV, custom)
• Task-specific labeling guidelines
• Audit logs for each annotation run
• Iteration-ready pipelines to refine labels as models evolve
• Optional delivery into your training pipelines or labeling platform of choice

Why Digital Bricks?

We combine domain expertise, precision workflows, and tooling flexibility to deliver annotation services that are accurate, secure, and production-ready.

Whether you’re training a foundational LLM, fine-tuning a Copilot, or building vertical-specific models, our labels give your AI the context it needs to succeed.