What is data annotation?

Data annotation is the process of labeling data such as text, images, audio, and video with metadata so machine learning models can learn from it.

How do you ensure quality?

We follow detailed guidelines, run a pilot batch, perform multi-step QA reviews, and apply a correction loop to ensure high-quality annotations.

Can you start with a small pilot?

Yes, we recommend starting with a small pilot project to confirm label definitions and quality targets before scaling.

What formats can you deliver?

We can deliver in COCO, YOLO, VOC, JSON, or CSV formats based on your training pipeline, finalized during the pilot stage.

Data Annotation

Data Annotation Services for AI Training Data

Q: Which annotation types do you support?

We support bounding boxes, polygons, segmentation masks, keypoints/pose, tracking, and more depending on the dataset.

Q: Do you work with CVAT?

Yes, we work with CVAT for image and video annotation including detection, segmentation, tracking, and classification workflows.

Better AI starts with well-labeled data

Data annotation is the process of adding clear labels and metadata to raw data so machine learning models can learn patterns correctly—especially in supervised learning. With the right labeling strategy, you reduce noise, improve model accuracy, and speed up iteration from prototype to production.

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Data Annotation Delivery Essentials

Image & Video Annotation

Bounding boxes, polygons, masks, keypoints, tracking—built for real-world computer vision use cases.

Quality Assurance

Multi-step QA with sampling, reviewer checks, and feedback loops to keep labels consistent across teams.

Scalable Teams

Start small with a pilot batch, then scale volume while keeping the same guidelines and audit trail.

What we annotate

We provide high-accuracy annotation services across multiple data types to support AI, machine learning, and autonomous systems.

Image Annotation

Bounding boxes, Polygons
Semantic/Instance Segmentation
Keypoints / Pose

Video Annotation

Tracking & Keyframes
Activity / Action Labels
Frame-level Masks where required

Text Annotation (NLP)

Entity Tagging
Intent / Sentiment Labels
Classification Datasets

Audio Annotation

Speech Transcription
Speaker Labels
Timestamps (when required)

LiDAR / Point Cloud

3D Cuboids
Lane / Road Elements
Object Classes for Autonomy Workflows

Document / OCR Annotation

Layout Detection Labels
Table Structure Tagging
Key Value Extraction

Why choose Skaro for data annotation?

High annotation consistency
Pilot-first approach (prove quality early)
Bias-aware labeling guidelines
Dedicated QA + reviewer workflow
Secure handling (NDA + access control)
Works across industries & use cases

Why data annotation matters

Without properly tagged data, AI systems can’t reach their full potential—good labels are the foundation for accurate model learning and evaluation.

Higher Model Accuracy

Consistent labels help models learn correct patterns, improving precision/recall and reducing false detections in production.

Faster Iteration Cycles

Clean, structured datasets reduce training rework—so your team can test, tune, and ship faster.

Better Real-World Performance

QA-backed labeling makes models more reliable across lighting, angles, occlusions, and real-world edge cases.

Our Process

Requirements & Label Taxonomy

Defining project needs

Labeling Guidelines & Examples

Instruction & samples

Pilot Batch (Quality Benchmark)

Initial quality assessment

Production Labeling

Full-scale annotation

QA Review & Corrections

Ensuring accuracy

Final Export & Delivery Report

Completed data delivery

Get a quick estimate & pilot plan

Share your dataset type, label classes, and target format. We’ll reply with a pilot timeline, QA approach, and pricing options.

FAQ

Everything You Need to Know About Our Services

Labeling data with metadata (text, images, audio, video) so ML models can learn from it.

Bounding boxes, polygons, segmentation masks, keypoints/pose, tracking, and more depending on the dataset.

Yes—CVAT is a common workflow for image/video annotation including detection, segmentation, tracking, and classification.

Guidelines + pilot batch + multi-step QA review + corrections loop.

Yes—recommended to confirm label definitions and quality targets before scaling.

COCO/YOLO/VOC/JSON/CSV based on your training pipeline (finalized during the pilot).