Image Annotation
Bounding boxes, semantic and instance segmentation, polygons, keypoints and image classification.
Data Annotation & Labeling Services for AI and Machine Learning
eQOURSE turns raw image, video, text, audio, document and LLM-feedback data into model-ready training data through written guidelines, trained annotators and multi-tier quality review.
500+ annotation and QA specialists · 30+ languages · ISO 9001 and ISO 27001 certified processes
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What Is Data Annotation and Labeling?
Data annotation adds labels, tags, boundaries or structure to raw data so a machine learning model can learn from it. The quality ceiling of a supervised model is set by its labels, so annotation is a specification problem before it is a labour problem.
Data Annotation vs. Data Labeling
Labeling often assigns a class to a whole item; annotation can also describe regions, relationships and attributes.
Data Collection vs. Data Annotation
Collection creates raw data. Annotation adds meaning to existing data.
Explore AI Data Collection ServicesData Annotation Services Across Every Data Type
Video Annotation
Persistent object tracking, frame interpolation, action recognition, event boundaries and multi-camera association.
Text and NLP Annotation
Named entities, sentiment, text classification, relation extraction, intent and machine-translation review.
Audio and Speech Annotation
Transcription, speaker diarization, emotion, acoustic events, phonetics and wake-word tagging.
LLM and RLHF Data
Response ranking, instruction following, safety, factual verification, red teaming and grounding checks.
3D Point Cloud and LiDAR
3D cuboids, point segmentation, sensor fusion, tracking and drivable-space labels.
Document and OCR Annotation
Layout regions, form fields, table structure, handwriting, invoice parsing and KYC labels.
Content Moderation and Trust and Safety
Policy violations, severity tiers, hate and harassment, NSFW, spam and fraud review.
Annotation Task Types We Support
Computer vision: bounding boxes, polygons, segmentation, keypoints and cuboids. Video: tracking, interpolation, action and event boundaries. Language: NER, relations, intent, sentiment and relevance. Speech: transcription, diarization and acoustic events. Documents: layout, forms, tables and handwriting. Generative AI: preference ranking, rubric evaluation, factuality, citations and agent trajectories.
See annotation samplesOur Data Annotation Process
- Scope and sample review
- Guideline authoring
- Annotator onboarding and calibration
- Pilot batch
- Production annotation
- Multi-tier quality review
- Delivery and iteration
Multi-Tier QA Framework
Gold-standard sets, inter-annotator agreement, consensus and adjudication, multi-pass review, automated validation, documented escalation paths and acceptance criteria agreed during the pilot.
Annotation Guidelines and Edge-Case Handling
Guidelines are stress-tested against real samples. New rulings are versioned, dated, distributed to the full team and back-propagated where consistency requires relabeling.
Annotation by Subject-Matter Experts, Not Just Annotators
STEM, medical, life-sciences, language, assessment and curriculum experts review tasks where domain judgement—not throughput—sets quality.
Multilingual Annotation Across 30+ Languages, With Deep Indic Coverage
Hindi, Bengali, Tamil, Telugu, Marathi, Gujarati, Kannada, Malayalam, Punjabi, Odia, Assamese and Urdu, including code-mixed and transliterated content.
Tools, Platforms and Output Formats
COCO JSON, YOLO, Pascal VOC, CVAT XML, masks, JSONL, CSV, CoNLL, BIO, SRT, VTT, RTTM, Parquet and custom schemas.
Data Security, Privacy and Compliance
ISO-certified processes, NDAs, role-based access, controlled environments, PII workflows, audit trails and contract-defined retention and deletion.
Engagement Models
Managed projects, dedicated teams, overflow support, and QA or relabeling engagements.
In-House vs. Crowdsourced vs. Managed Annotation
eQOURSE combines a retained trained team, expert reviewers, versioned guidelines, gold sets, agreement measurement and scalable delivery.
What Determines Data Annotation Pricing?
Task complexity, objects per item, volume, quality tier, language and domain expertise, turnaround, security and guideline maturity.
Data Annotation for Every Industry
Automotive, healthcare, retail, robotics, agriculture, finance, media and education.
One AI Data Workflow, From Collection to Model Testing
Collect → Annotate → Clean and Validate → Test → Improve
Robotics Training Data ServicesSee the Work
Why Choose eQOURSE for Data Annotation
Subject-matter experts, complete AI data workflows, guideline-first delivery, Indic language depth, 500+ specialists, ISO-certified processes and a free pilot.
Frequently Asked Questions About Data Annotation
What is data annotation?
Data annotation adds labels, tags, boundaries or structure to raw data so a machine learning model can learn from it.
What is the difference between data annotation and data labeling?
The terms are often interchangeable. Labeling may assign a class to a whole item, while annotation can include richer regions, relationships and attributes.
What is the difference between data collection and data annotation?
Collection sources or captures raw data. Annotation adds labels and structure to data that already exists.
What types of data can eQOURSE annotate?
Images, video, text, audio and speech, documents, 3D point clouds and human-feedback data for large language models.
How do you ensure annotation quality?
Written guidelines, qualification tests, gold sets, agreement measurement, consensus, senior adjudication, multi-pass review and automated validation.
Do you provide subject-matter experts?
Yes. Qualified reviewers cover STEM, medical and life sciences, legal, linguistics and education.
Can you work in our annotation tool?
Yes. Projects can run in your platform or on tooling provided by eQOURSE.
What output formats do you deliver?
COCO, YOLO, Pascal VOC, CVAT XML, JSON, JSONL, CSV, CoNLL, BIO, SRT, VTT, RTTM, Parquet and custom schemas.
Do you support multilingual annotation?
Yes. Native-speaker review is available across 30+ languages, including deep Indic-language coverage.
How much do data annotation services cost?
Cost depends on task complexity, objects per item, volume, quality tier, language, expertise, turnaround and security.
How long does a project take?
A pilot typically runs within the first week; production timing depends on volume, complexity and quality tier.
How is data kept secure?
Work runs under ISO 27001 certified processes with NDAs, role-based access, audit trails and controlled retention.
Can you fix an existing labeled dataset?
Yes. eQOURSE can audit, quantify errors, repair labels or relabel against a corrected guideline.
Can collection and annotation run together?
Yes. Data can move through collection, annotation, cleaning, validation and testing in one workflow.
Do you support RLHF and LLM evaluation?
Yes. Work includes preference ranking, instruction following, safety, factuality, citation checks, red teaming and agent trajectory review.