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Data Annotation and Labeling Services for ML

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Data annotation is labeling raw data with tags or metadata, making it interpretable for ML algorithms. Unidata provides expert annotation services that turn raw data into accurate training datasets, using trained human annotators and advanced tooling to improve model performance across industries.

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95%+ annotation accuracy
1,000+ domain-matched annotators
Pilot launched within days

Industries

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Healthcare

AI-powered medical imaging, pathology analysis, and EHR predictions for better patient care.

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Automotive Systems

Training self-driving cars to detect objects, navigate roads, and avoid pedestrians safely.

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Retail & E-commerce

Product tagging, pricing optimization, and customer sentiment analysis for better sales.

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Customer Service & Support

Training chatbots and improving transcription accuracy for better customer interactions.

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Agriculture

Crop health monitoring, pest detection, and livestock tracking via satellite and drone imagery.

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Security & Surveillance

Facial recognition, threat detection, and license plate tracking for law enforcement.

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Manufacturing

Quality control, defect detection, and predictive maintenance on assembly lines.

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Entertainment & Media

Content moderation, captioning, and sentiment analysis for safer, accessible platforms.

Data Annotation Vs Labeling Tasks

Data AnnotationData Labeling
DefinitionComprehensive process of adding metadata, tags, and structural information to raw data for machine learning comprehensionSpecific task of assigning target labels or classes to data points for supervised learning
Work CoverageHolistic: includes labeling, segmentation, bounding, transcription, relationship mapping, and metadata enrichmentNarrower: primarily classification or regression target assignment
Common Tasks
  • Semantic segmentation
  • Polygonal/instance segmentation
  • Landmark/keypoint annotation
  • Entity-relationship mapping
  • 3D point cloud annotation
  • Video object tracking
  • Audio transcription + tagging
  • Image/object classification
  • Sentiment labeling
  • Binary/multi-class categorization
  • Simple presence/absence tag
  • Content moderation flags
Complexity LevelHigh complexity: requires domain expertise, spatial-temporal reasoning, and understanding of relationshipsLow to medium complexity: primarily follows straightforward guidelines with binary or categorical decisions
ML ImpactEnables advanced models: object detection, semantic segmentation, pose estimation, action recognition, relationship learningEnables fundamental models: classification, regression, basic recognition, content filtering

Data Annotation Types

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Image Annotation

Bounding boxes, polygons, and key points applied to images, enabling object detection, image classification, and facial recognition for ML models.
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Text Annotation

Entities, keywords, sentiment, and POS tags added to textual data, giving NLP models the foundation for sentiment analysis, NER, and language translation.
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Audio Annotation

Transcriptions, speaker tags, and emotion labels structured by trained annotators, helping ML models interpret speech and sound across any domain or language.
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Video Annotation

Frame-by-frame tags for objects, actions, and events, giving learning models the context for object tracking, activity recognition, and scene understanding.
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Geospatial Annotation

Roads, buildings, and vegetation labeled in satellite imagery using polygon annotations, helping ML models interpret the physical world for urban planning and environmental research.
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3D Annotation

Point clouds and 3D models annotated with cuboid annotation and 3D cuboids, giving ML models the spatial understanding for autonomous driving, robotics, and AR.
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3D Point Cloud Annotation

Surface points and boundaries labeled across 3D point data, structuring accurate datasets for ML models to navigate and map complex environments.
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OCR

Forms, handwritten notes, and scanned documents labeled so ML models can extract and interpret written content, making document digitization reliable.

Software We Use for Data Annotation Services

Labelbox

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Labelbox is a comprehensive data annotation platform that supports a wide range of data types, including images, text, and video. It's designed to streamline the annotation process with its intuitive interface and powerful collaboration features.

Best For:

Projects requiring a highly customizable and scalable annotation platform with robust quality control mechanisms.

Key Features

  • Customizable workflows and interfaces for different annotation tasks.
  • Integrated quality assurance and review tools.
  • Scalable for large projects with a high volume of data.
  • Supports collaborative work, enabling teams to work simultaneously.

SuperAnnotate

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SuperAnnotate is an advanced platform that combines annotation tools with project management features. It is especially strong in image and video annotation tasks, offering a high level of precision and automation.

Best For:

Teams looking for a powerful, AI-assisted annotation tool that can handle complex image and video data.

Key Features

  • Automated annotation tools powered by AI to speed up the annotation process.
  • Collaboration tools for large teams.
  • Supports a wide range of annotation types, including polygons, bounding boxes, and keypoints.
  • Integration with popular machine learning frameworks.

How Unidata Provide Data Labelling Process

A Clear, Controlled Workflow From Brief to Delivery

01 Kickoff Briefing and Task Setup
You
Share your raw data, annotation requirements, and quality standards
Unidata
We analyze your data, define the methodology, and assign a dedicated project lead. The right annotation type and domain-matched annotators are confirmed before anything starts.
02 Pilot & Scoping Pilot and Estimate
You
Review annotated samples, validate quality, and approve scope before full-scale work begins.
Unidata
We annotate a small representative sample and deliver a clear cost estimate broken down by complexity, hours, and validation rounds.
03 Legal & Confidential Agreement and NDA
You
Review and sign. Scope, quality thresholds, and deadlines are all defined in writing upfront.
Unidata
We prepare a full confidentiality agreement covering your data, guidelines, and any proprietary model details.
04 Technical Setup Tools and Workflow Configuration
You
Share existing guidelines and format requirements. No guidelines yet? We build them together.
Unidata
We configure the right annotation platform for your data type: Labelbox, SuperAnnotate, CVAT, or Label Studio. Workflows, label taxonomy, and quality benchmarks are set before a single label is applied.
05 Execution Annotation in Progress
You
Review sample batches at each milestone and share feedback with your project lead.
Unidata
Trained, domain-matched annotators work through your dataset. No batch moves forward without passing internal quality checks.
06 QA Human-in-the-Loop Review
You
Review edge cases and confirm acceptance criteria before final delivery.
Unidata
Every batch goes through automated validation and human review. Inter-annotator agreement (IAA) is tracked throughout. Inconsistencies are caught and resolved before the dataset moves forward.
07 Delivery Production-Ready Dataset
You
Receive your annotated dataset in the format you need: COCO, Pascal VOC, JSON, CoNLL, PCD, or custom. Full quality report included.
Unidata
Clean, validated, training-ready data delivered on schedule. Final invoice aligned to the scope agreed at Step 02.

Have questions about the process? Every project starts with a free consultation — no commitment required.

Request Custom Research

Data Annotation Challenges? Value You Get with Unidata

Real Challenges

  • No annotators, tools, or workflow to process collected data
  • No quality check on labeled data before it hits the pipeline
  • No way to ensure two annotators label the same object consistently
  • Can’t find annotators with LiDAR, medical, or financial expertise
  • Scope creep and rework cycles exhaust the budget before delivery

Value with Unidata

  • Project lead assigned and pilot launched within days
  • Every batch validated before delivery, 95%+ accuracy via multi-stage QA
  • Label consistency tracked per batch, issues caught before training fails
  • 1,000+ annotators matched by domain — the right expert, every time
  • Pilot-first pricing, fixed scope, zero hidden rework charges

Data Annotation Files Example

Working with annotation data from CVAT and JSON formats, you'll receive optimized code that seamlessly processes both file types, complete with practical examples and visual representations of your data structure.

Other Services

Ready-Made Datasets

Get our ready-made datasets to enhance the quality of your models and improve testing

Data Collection

Collect and enhance diverse image, video, text, and audio data for your business

Data Annotation

Get accurate data labeling and annotation for your machine learning projects

LLM Training Services

Comprehensive data services for training, evaluation, and testing of LLM models across 12 industries

What our clients are saying

UniData

4 3 Reviews

PA

Paul 2025-02-21

Very Positive Experience!

The team was very responsive when requesting a specific dataset, and was able to work with us on what data we specifically needed and custom pricing for our use case. Overall a great experience, and would recommend them to others!

TH

Thorsten 2025-01-09

Very good experience

We got in touch with UniData to buy several datasets from them. Communication was very cooperative, quick, and friendly. We were able to find contract conditions that suited both parties well. I also appreciate the team's dedication to understand and address the needs of the customer. And the datasets we bought from UniData matched with our expectations.

Max Crous 2024-10-08

Data purchase

Our team got in touch with UniData for purchasing video data. The team at UniData was transparent, timely, and pleasant to communicate and negotiate with. Their samples and descriptions aligned well with the data we received. We will certainly reach out to UniData again if we're in search of 3rd party video data.

Abhijeet Zilpelwar 2025-02-26

Data is well organized and easy to…

Data is well organized and easy to consume. We could download and use it for training within few hours of receiving the data links.

Trusted by the world's biggest brands

FAQ

What is the minimum amount of data required to start?
No minimum. Unidata supports both small pilot datasets and large production-scale projects — ML teams can start with limited raw data to validate annotation techniques before scaling up.
How fast can you deliver annotated data?
Delivery speed depends on data type, annotation complexity, and volume. Unidata combines 1,100+ trained annotators with AI-assisted tools to ensure fast turnaround without sacrificing quality.
Can you handle large-scale annotation projects?
Yes. Unidata scales human annotators, automation tools, and quality workflows to meet enterprise-level requirements across diverse datasets and 19+ industries.
Who will be labeling my data?
Your data is handled exclusively by our in-house team of 1,100+ experienced human annotators with deep domain expertise across 19+ industries — not outsourced to crowds.
Can I start with a pilot project?
Yes. Unidata offers pilot projects so ML teams can evaluate annotation quality, workflows, and model compatibility before committing to full production scale.
How do you ensure annotation quality?
Through a dedicated Quality Control Department (QCD) with 6+ years of experience. Validators review annotated data daily, ensuring consistent labels, rapid error correction, and 95%+ accuracy across all batches.
What annotation accuracy can we expect?
95%+ accuracy, validated daily by the QCD. Accuracy targets are calibrated to your specific project requirements and data types before annotation begins.
How is my data kept secure?
All services are GDPR and CCPA compliant, running on AWS infrastructure certified under ISO 27001 and ISO 27701. Strict access controls are applied throughout the annotation process.
What annotation tools and techniques do you use?
Unidata uses advanced annotation platforms, AI-powered automation, and human review workflows — supporting bounding boxes, polygons, keypoints, semantic segmentation, 3D LiDAR point clouds, and multimodal datasets.
How do you manage annotation projects?
Each project follows a structured, milestone-based workflow: requirement analysis → guideline development → annotation → validation → delivery. Every project is supervised by a dedicated Project Manager and backed by the QCD.
Do you use Agile or Scrum methodologies?
Yes. Unidata operates on a Scrum-based model, enabling iterative delivery, fast feedback loops, and flexible scaling — critical for ML projects where requirements evolve during training data collection.
What are the risks of poor-quality annotation?
Inaccurate labels lead to biased predictions, reduced model performance, and costly retraining cycles. In critical applications — computer vision, NLP, generative AI — poor annotation can cause production failures. That's why every Unidata batch goes through multi-stage QA before delivery.
Why is data annotation important for AI and ML?
AI models learn entirely from training data. Accurate annotations directly determine model performance, generalization ability, and real-world deployment success — garbage in, garbage out.
What is the difference between data annotation and data labeling?
Data labeling covers basic tasks — assigning categories or tags. Data annotation is broader: it includes semantic segmentation, keypoint annotation, polygon markup, metadata enrichment, and relationship mapping required for advanced ML models.

Why Companies Trust Unidata’s Services for ML/AI

Share your project requirements, we handle the rest. Every service is tailored, executed, and compliance-ready, so you can focus on strategy and growth, not operations.

Rely on 1,100+ Experts

  • 1,100+ in-house labelers and specialists
  • Consistent quality and rapid scaling
  • Complex multi-type annotation projects
01

Discover 19+ Industry Expertise

  • Finance, IT, E-commerce, Retail, Healthcare, Medical, Fintech, and more
  • Deep domain knowledge for industry-specific requirements
  • Support for industry-specific annotation challenges
02

Get Turnkey Services for ML/AI

  • From data collection to labeling and validation
  • Project tailored to your requirements
  • Complex annotation, multiple annotation types at once
03

Ensure Legal & Secure Data

  • GDPR & CCPA compliant
  • AWS ISO 27001/27701 storage
  • Curated and legally sourced
04

Process Different Content Types

  • Multimodal Data: 333K+ texts, 550K+ audio, 11K+ videos, 26K+ images
  • Formats: DICOM, LiDAR, and specialized types
  • Annotation: multiple types at once with high accuracy
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Request Custom Research

Have questions about the process? Every project starts with a free consultation — no commitment required.

Explore our cases

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