Data Labeling Services | Outsource to India

Precise BPO AI annotation team in Pune India — image and video annotation for AI training datasets

Security & Compliance

ISO 27001 Aligned Practices

HIPAA Aligned Workflows

GDPR Aligned for EU/UK

NDA-Signed Staff Only

About Our Services

Why Choose Precise BPO as Your Data Annotation Partner

Building reliable AI models requires high-quality data annotation services — large, diverse, edge-case-rich datasets that reflect real-world complexity. Our human-in-the-loop quality model ensures every label is reviewed, validated, and refined by domain-trained specialists — producing ground truth data your machine learning pipelines can depend on.

Founded in 2008 and based in Pune, India, Precise BPO has spent 17+ years building production-ready AI training datasets and markup pipelines for ML teams worldwide. With 540+ permanent, specialist labelers, we serve AI startups, enterprise R&D groups scaling their computer vision pipelines, research labs, and universities — bringing both scale and domain depth that in-house teams rarely match. Our AI training data pipelines are trusted by 600+ global clients, who consistently report 40–60% cost savings after making the switch.

Our work spans autonomous vehicles, AgriTech, retail, GIS, robotics, medical imaging, and enterprise NLP — powering computer vision and multimodal pipelines at scale. Every project runs through our security-first workflow with encrypted transfers, role-based access, and full audit trails. Learn more about our company or contact us to get started.

🎯

Accuracy Consistently Above 99.5%

Multi-level QA, audits, rule refinement, and feedback loops ensure reliable datasets across every stage of your AI pipeline.

⚡

Start in 48 Hours — No Long Contracts

From requirement confirmation to first labeled batch in 48 hours. Try before you commit — pilot batch included.

🔒

Zero Freelancers — 100% In-House

Every annotator is a permanent, background-verified, NDA-signed employee. No crowdsourcing. No data risk.

Learning Centre

What Is Data Annotation & Labeling?

A practical guide to understanding ML annotation, tagging types, and how to choose the right approach for your AI project.

🏷️

What Is Data Annotation & Labeling?

Data annotation services add meaningful metadata to raw data — images, video frames, text, audio, or point clouds — so machine learning models can learn to recognise patterns. Without accurately marked-up training data, supervised ML models cannot be trained.

Common examples include drawing bounding boxes around vehicles in dashcam footage for autonomous driving, pixel-level masks on medical scans for radiology AI, or entity tagging in clinical notes for NLP pipelines. The consistency of these labels — the ground truth — directly determines model accuracy and reliability at inference time.

📐

Types of Data Labeling

Bounding Box: Rectangles around objects — foundation of object detection & image classification
Semantic Segmentation: Pixel-level class masks for AVs, medical imaging
Polygon / Instance: Precise shape outlines for irregular objects
Polylines: Lane detection, road markings, linear features
Keypoints: Pose estimation, facial recognition, joint mapping
3D Cuboids / LiDAR: Depth data for robotics and autonomous systems
NLP Annotation: NER, intent, sentiment, span classification

Read: What Is AI Data Labeling? →

⚙️

Data Labeling vs. Data Tagging

The two terms are often used interchangeably. Labeling typically assigns class-level tags (e.g., "cat" or "dog"), while annotation covers richer spatial markup — bounding boxes, polygons, keypoints — that captures structural context a model needs to understand the scene.

Both are essential for accurate supervised learning. We handle both in a single managed workflow, from simple classification tags to complex multi-class pixel segmentation.

Read our in-depth ML data preparation guide, our governance framework for enterprise teams, and Google's ML data preparation guide for further context on industry best practices.

💡

In-House vs Outsourced Labeling

Building an in-house team costs 40–60% more once you account for recruiting, onboarding, tooling, and QA infrastructure. Partnering with a specialist team gives you immediate scale and domain expertise with none of that overhead.

The key differentiators to evaluate: permanent in-house staff (not crowdsourcing), multi-level QA, domain-specific training (medical, automotive, agriculture), security compliance, and a free pilot before commitment.

Read our training data cost guide for a full in-house vs outsourced comparison.

See How It Works →

Sound Familiar?

Data Annotation Problems Slowing Your AI

Most AI teams hit the same walls. Here's what we fix.

Low-quality labels keep breaking your model?

Crowdsourced or freelance work looks cheap — until your model's mAP tanks and you're paying engineers to re-label everything. Our human-in-the-loop multi-level QA catches errors before delivery, not after. Every batch is independently reviewed before handover. Learn how we maintain 99.5% object detection labeling accuracy and 99.2% segmentation accuracy at scale.

✓ Fix: Independent multi-level QA on every batch

Scaling your dataset is taking months instead of weeks?

In-house teams hit capacity ceilings fast. Hiring, onboarding, and training specialists takes 4–6 weeks minimum — before a single label is created. We spin up a full project team within 48 hours. No hiring. No ramp-up lag. See how our end-to-end process works, or start your project in 48 hours →

✓ Fix: Over 540 specialists ready within 48 hours

Can't pass security review because your vendor uses freelancers?

Medical imaging, financial documents, and defence projects require data to never leave controlled environments. Every member of our team is a permanent, NDA-signed, background-verified employee — no freelancers, ever. Our security practices cover encrypted SFTP, role-based access controls, VPN tunnels, and full audit logs. See our HIPAA-aligned medical imaging service and data de-identification services.

✓ Fix: 100% in-house staff, zero data risk

Training data costs eating your AI budget?

US and European labeling teams charge $25–$60/hr for work that costs $5–$9/hr at equivalent quality from our Pune centre. Our clients save 40–60% vs in-house operations — without sacrificing accuracy, security, or turnaround time. Companies that outsource to an India-based partner like Precise BPO eliminate recruiting lag and tooling overhead entirely. See transparent pricing rates →

✓ Fix: $5–$9/hr dedicated teams — 40–60% savings

Inconsistent labels causing model regression between versions?

Labeling guidelines drift when teams scale without proper rule enforcement. We assign dedicated project managers who maintain guideline consistency across every specialist, batch, and sprint — so your model training stays stable. Learn more about our full annotation capabilities and end-to-end process.

✓ Fix: Dedicated PM enforcing guidelines every sprint

No edge-case coverage making your model fragile in production?

Models trained on clean data fail on real-world noise, occlusion, and rare classes. Our domain specialists — medical, automotive, agriculture — proactively identify and label edge cases that generalist vendors miss entirely.

✓ Fix: Domain specialists who know your edge cases

Tagging Types

Complete Data Annotation Services

Every data annotation service your ML pipeline needs — image annotation, video labeling, LiDAR, and NLP — delivered with domain expertise, multi-level QA, and enterprise-grade security.

⬜

Bounding Box Annotation

Rectangular tagging for object detection and image classification. Foundation of computer vision training data at scale.

Explore bounding box →

🎨

Semantic Segmentation

Pixel-level class masking for autonomous vehicles, medical image tagging, and satellite imagery. 99.2%+ accuracy.

View segmentation service →

🔷

Polygon Annotation

Precise shape outlines for irregular objects — crops, vehicles, body parts, architectural elements.

See polygon service →

〰️

Polyline Annotation

Lane detection, road markings, cable routing, linear infrastructure for autonomous systems.

View polyline service →

📍

Landmark / Keypoint

Pose estimation, facial recognition, joint mapping. Skeletal keypoints for human action recognition.

Explore keypoints →

📦

3D Cuboid & LiDAR

3D point cloud markup for robotics, AVs, industrial automation. Full volumetric spatial tagging.

LiDAR & 3D cuboid →

💬

Text & NLP Annotation

NER, intent classification, sentiment, span labeling, and RLHF feedback for language model training.

Text & NLP services →

🎬

Video Annotation & Tracking

Multi-object tracking across frames. Action recognition, behavior analysis, temporal event markup.

Enquire about video annotation services →

🔒

Data De-identification

HIPAA-aligned PII redaction for medical records, financial documents, legal filings.

De-identification service →

🏥

Medical Image Annotation

Lesion detection, organ segmentation, radiology AI. HIPAA-aligned clinical labeling workflows.

Medical imaging service →

🚗

Autonomous Vehicle

Full AV pipeline: object detection labeling, LiDAR, lane detection, 3D cuboids for ADAS and self-driving.

Automotive AI labeling →

🌾

Agriculture Annotation

Crop monitoring, disease detection, drone imagery analysis for precision farming AI models.

AgriTech labeling →

Also available: fashion & apparel labeling, sports AI labeling, and explicit content tagging.

Industries We Serve

Data Labeling Across Every Industry

Supporting AI training data and image annotation projects across mobility, robotics, agriculture, retail, healthcare, finance, GIS, security, media, manufacturing, and emerging domains.

🚗

Autonomous Vehicles

High-quality labeled training data for self-driving and ADAS systems. Rectangular annotation, LiDAR, and semantic segmentation for path planning.

Automotive annotation →

🏥

Healthcare & Medical AI

HIPAA-aligned medical image annotation — lesion detection, organ segmentation, radiology AI with precision tagging.

Medical imaging service →

🌾

Agriculture & AgriTech

AI models for crop monitoring, disease detection, and yield prediction from drone and satellite imagery.

AgriTech labeling →

🛒

Retail & E-Commerce

Product image tagging, catalog classification, and customer interaction data for recommendation engines and visual search.

Retail AI service →

🤖

Robotics & Automation

Precise ground truth datasets training robots for navigation, object recognition, and industrial automation with 3D cuboid labeling.

3D cuboid & LiDAR labeling →

💰

Finance & Insurance

Annotated financial documents for risk assessment, fraud detection, and automated claims processing.

Document de-identification →

🗺️

GIS & Mapping

High-accuracy geospatial tagging for mapping AI, urban planning, and satellite imagery analysis.

Polygon & geospatial labeling →

🔒

Security & Surveillance

Video and image tagging for threat recognition, behavior detection, and monitoring AI. Multi-object tracking.

Bounding box service →

⚽

Media, Sports & Fashion

Labeled datasets for video analysis, highlights generation, performance tracking, and visual search. Sports annotation and fashion & apparel labeling available.

Fashion AI labeling →

🏭

Industrial Automation

AI training data for quality inspection, predictive maintenance, and smart factory automation.

Medical imaging service →

🛸

UAV, Drone & Marine

AI models for aerial, marine, and drone-based imaging. Terrain, vessel, and infrastructure detection from aerial tiles.

Polyline & aerial labeling →

🎓

Education & Research

AI initiatives in universities and research labs rely on our labeled datasets for experiments and model validation.

Request a free pilot →

Client Results

Client Results — Before & After

Real-world examples showing how structured labeling and markup work boosts AI accuracy, automation, and decision-making across industries globally.

🇺🇸

Autonomous Driving · USA

Object Detection — Vehicles, Pedestrians & Road Users

Created high-accuracy object detection labels and pixel-level masks across thousands of frames. Full pipeline: autonomous vehicle labeling →

Before

Model accuracy at 78%, unsafe for real-world deployment

After

Accuracy improved to 96%, enabling safer autonomous navigation

+18%

Accuracy Gain

96%

Final Accuracy

🇧🇪

Autonomous Vehicles · Belgium

Road Scene Segmentation — Lane & Infrastructure Labeling

Performed dense pixel-mask segmentation to label lane boundaries, curbs, and road infrastructure for AV path planning. See our full automotive labeling service →

Before

No reliable automated road scene understanding

After

Smoother AV navigation and more reliable decision-making

Pixel

Level Masks

Path Planning

🇬🇧

Retail AI · UK

Product Catalog Tagging — 2.1× CTR Improvement

Applied bbox annotations and attribute tagging to each product image across 500K+ products. Full service: retail annotation →

Before

Manual catalog management with low search relevance

After

2.1× increase in product discovery CTR and sales

2.1×

CTR Increase

500K+

Products Tagged

🇯🇵

E-Commerce QC · Japan

Defect Detection — 40% Reduction in Product Returns

Created binary masks highlighting damaged areas on product photos to automate quality control. Service: retail annotation →

Before

Manual QC with 8% average product return rate

After

40% reduction in return rates, major cost savings

-40%

Return Rate Drop

1.2M+

Images Processed

🇰🇷

Robotics · South Korea

Object Recognition — Robotic Grasp Optimization

Labeled 3D cuboids and keypoints for precise grasp points across varied object types and orientations.

Before

Inconsistent grasp success in automated picking lines

After

Successful robotic picks increased by 29%

+29%

Pick Success Rate

Cuboid + Keypoints

🇮🇱

Document AI · Israel

OCR Mapping — 95% Structured Extraction Accuracy

Annotated text regions and applied NER tagging for key entities across large document volumes. Service: text & NLP tagging →

Before

Manual extraction slow and error-prone at scale

After

95% structured extraction accuracy, faster workflows

95%

Extraction Accuracy

NER

Entity Tagging

🇸🇬

GIS & Mapping · Singapore

Satellite Asset Mapping — 90%+ Accuracy

Polygon segmentation applied to satellite tiles for precise feature extraction at city scale.

Before

Manual mapping slow and inconsistent across tiles

After

90%+ accuracy supporting urban planning and resource management

90%+

Mapping Accuracy

Polygon

Segmentation

🇨🇭

Sports Analytics · Switzerland

Player & Ball Tracking — Real-Time Analytics

Multi-keypoint skeleton markup for each player and the ball across match footage. Full service: sports annotation →

Before

No automated performance tracking system

After

Real-time analytics accuracy improved, aiding coaching

Real-time

Tracking

Multi-

Keypoint Skeleton

🇦🇺

Manufacturing QC · Australia

Defect Detection — 35% Fewer Manual Inspections

Dense defect segmentation on production line images to train automated inspection AI.

Before

High manual inspection overhead with missed defects

After

35% reduction in manual inspections, increased throughput

-35%

Manual Inspections

Dense

Segmentation

🇸🇪

Security & Surveillance · Sweden

Abnormal Behaviour Detection — +22% Accuracy

Bbox annotation combined with activity tagging for human and object detection across surveillance footage. Service: bounding box annotation service →

Before

Low detection accuracy in complex surveillance scenes

After

Detection accuracy improved by 22%, enhancing safety monitoring

+22%

Detection Accuracy

Activity

Tagging

🇳🇴

UAV & Drone Mapping · Norway

Terrain & Infrastructure Labeling from Aerial Imagery

Annotated polygons with class segmentation for land-use analysis across drone-captured tiles. Service: polygon annotation service →

Before

Manual aerial image interpretation, slow and inconsistent

After

Improved terrain and infrastructure classification for planning

UAV

Aerial Tiles

Polygon

Class Segmentation

🇳🇱

Marine AI · Netherlands

Vessel Tracking — 92% Tracking Consistency

Combined object tracking with mask tagging for precise vessel detection on water. Service: semantic segmentation →

Before

Unreliable automated vessel detection in complex scenes

After

92% tracking consistency, safer navigation and monitoring

92%

Tracking Consistency

Mask

Annotation

🇮🇹

Media & Moderation · Italy

Content Moderation — 50% Less Manual Review

Applied content tagging and classification for automated moderation pipelines. Service: explicit content tagging →

Before

Manual review of all flagged media, high overhead

After

50% reduction in manual moderation, improved compliance

-50%

Manual Review

Auto

Classification

🇦🇪

Multimodal AI · UAE

Vision-Language Alignment — Multimodal Dataset

Caption tagging with region markup across images and associated text for multimodal pipelines. Service: text & NLP tagging →

Before

Misaligned visual and text data limiting model performance

After

Enhanced representation learning across multimodal datasets

Multi-

Modal Alignment

Caption

+ Region Tags

🇮🇳

NLP & Text AI · India

Named Entity Recognition — Unstructured Text Mapping

Free-form text tagged for NLP pipelines including intent detection, entity recognition, and sentiment analysis. Full service: text & NLP labeling →

Before

Unstructured text data blocking NLP pipeline development

After

Clean NER datasets enabling faster model training cycles

NER

Entity Tagging

NLP

Pipeline Ready

🇩🇪

AgriTech · Germany

Crop/Weed Differentiation — Precision Farming AI

Annotated polygonal crop boundaries with multi-class segmentation across large-scale farm imagery. Full service: agriculture annotation →

Before

No reliable automated crop health monitoring

After

31% improvement in crop-health monitoring and yield predictions

+31%

Crop Accuracy

3M+

Images Labeled

🇨🇦

Healthcare AI · Canada

Medical Imaging Annotation — +18% Model Recall

Polygon and pixel-level tagging reviewed by QA specialists, HIPAA-aligned throughout. Full service: medical imaging annotation →

Before

Diagnostic model missing edge-case anomalies

After

+18% recall improvement in cancer screening model

+18%

Recall Improvement

99.5%

QA Accuracy

🇫🇷

Insurance AI · France

Claims Automation — 4.3× Faster Processing

Performed instance segmentation of exterior damages enabling end-to-end automated claims assessment.

Before

Manual claims review taking 5–7 business days

After

4.3× faster processing — under 24 hours automated

4.3×

Processing Speed

−82%

Processing Time

Quality Standards

Data Annotation Quality Assurance & Security Standards

Every labeling type maintains dedicated quality benchmarks tracked project-by-project. We publish our accuracy rates — because our clients need to trust the data that trains their models.

Accuracy maintained through: multi-layer QA → independent reviewer validation → random sampling audits → active learning feedback loops → continuous guideline refinement.

Bounding box accuracy is validated using IoU (Intersection over Union) threshold checks run by a dedicated QA reviewer on every batch. Segmentation masks are verified against reference contours with pixel-level diff scoring. LiDAR cuboid accuracy is measured by point-cloud overlap ratio, with a specialist sign-off required on all 3D frames. NLP labels undergo inter-annotator agreement checks — only batches scoring above 0.92 Cohen's Kappa advance to delivery.

Our QA workflow uses a 3-tier system: primary reviewer → independent QA specialist → senior sign-off. Random 10% sampling audits are run on every batch to detect drift. Our security practices are aligned to ISO/IEC 27001 — the international standard for information security management. See how we compare in the top data annotation companies roundup.

These accuracy figures are not marketing estimates — they are measured per-batch metrics tracked in our internal QA dashboards and available on request. Since 2008, our QA methodology has been refined across 810M+ labeled images and 17+ years of production annotation work. Clients in regulated industries such as healthcare and autonomous vehicles routinely audit our process documentation, inter-annotator agreement scores, and batch-level accuracy logs before committing to long-term programs.

Request QA Documentation →

Bounding Box Labeling99.5%

Semantic Segmentation99.2%

Polygon Labeling99.4%

LiDAR Point Cloud99.1%

Text & NLP Labeling99.3%

Video Multi-Object Tracking98.9%

Why Precise BPO

Precise BPO vs Scale AI, Appen & Others

See how outsourcing to Precise BPO compares to in-house teams and other vendors across the key criteria AI teams care about.

Each column represents a realistic category of provider — enterprise platform, crowdsource marketplace, or dedicated in-house partner. Criteria were chosen based on what AI engineering and data teams consistently flag as project risks: accuracy consistency, security alignment, dedicated staffing, and time-to-first-label.

Criteria	Scale AI / Appen	Toloka / Freelancers	Precise BPO ✦
Accuracy	⚡ 95–98% (automated)	✗ Inconsistent	✓ 99.5%+ human-validated
Pricing Model	High platform fees + markups	Low but unpredictable quality	✓ From $5/hr · transparent
Project Start Time	1–2 weeks onboarding	3–5 days	✓ 48 Hours
Dedicated Team	✗ Pooled workforce	✗ Gig workers	✓ Named team, same annotators
ISO 27001 / HIPAA Aligned	⚡ Platform-level only	✗ No	✓ Fully aligned, NDA-signed
GDPR Aligned	⚡ Partial (US-centric)	✗ No	✓ EU/UK fully aligned
Zero Freelancers	✗ Crowdsourced	✗ All freelancers	✓ 100% in-house staff
Multi-Level QA	⚡ Algorithm-based	✗ Minimal	✓ Human QA every batch
Medical / Handwritten Docs	✗ Not supported	⚡ Limited	✓ Full support
Free Pilot Batch	✗ No	✗ No	✓ Always included

Precise BPO is the only vendor in this comparison offering 99.5%+ human-validated accuracy, a free pilot batch, and a 48-hour project start — with zero freelancers and full ISO 27001 alignment.

Precise BPO column highlighted · Comparison based on publicly available vendor information

Why Precise BPO

Our Proven AI Training Data & Annotation Track Record

Enterprise-grade markup trusted by AI startups, research labs, and global enterprises since 2008.

🏆

17+ Years · 810M+ Images

Since 2008, delivering image tagging, AI training data, and labeled video datasets across automotive, healthcare, agriculture, and 15+ more verticals. No India-based partner matches this track record. Teams who partner with us get 17 years of process maturity from day one.

🔒

ISO 27001 · HIPAA · GDPR Aligned

Encrypted SFTP, role-based access, VPN tunnels, full audit logs. Zero freelancers — every specialist is a permanent, background-verified, NDA-signed employee.

⚡

48-Hour Project Start

First labeled batch delivered within 48 hours of requirement sign-off. Free pilot batch included — validate quality before any financial commitment.

📈

Pilot to Enterprise-Scale

Scale your AI training data from 10K to millions of items with consistent quality. Explore our segmentation and NLP annotation services.

💰

40–60% Savings vs In-House

No recruitment, training, or tooling overhead. Pay for output, not headcount. Our Pune delivery centre gives US, UK, EU, and APAC teams a cost-effective partner with IST timezone overlap. See our pricing guide or check our listing in top labeling companies.

Transparent Pricing

Data Labeling Pricing & Rates

Transparent per-item and hourly rates. No hidden fees. Volume discounts from 50K+ items. Use the calculator to estimate your project cost.

Markup Type	Rate Range (USD)	Best For
Bounding Box	$0.01 – $0.06	Object detection, simple images
Polygon / Instance	$0.08 – $0.35	Precise shape outlines, irregular objects
3D Cuboid / LiDAR	$0.12 – $0.50	Robotics, AV depth data
Segmentation	$0.10 – $0.45	Autonomous driving, medical imaging
Polyline	$0.06 – $0.25	Lane detection, road markings
Landmark / Keypoint	$0.015 – $0.05	Pose estimation, facial recognition
De-identification	$0.015 – $0.05	Medical records, HIPAA compliance

Team Type	Rate Range	Best For
Annotation Specialist	$5 – $7 / hr	High-volume standard annotation
Senior / Domain Specialist	$7 – $9 / hr	Medical, LiDAR, complex segmentation
QA Reviewer	$6 – $8 / hr	Independent validation and auditing

📋

Project-Based Pricing

For large, multi-stage, or long-term programs — we provide a scoped fixed quote covering dataset volume, tagging types, QA levels, and delivery milestones.

Request a Project Quote →Validate Before Committing

Cost Estimator

Configure Your Project Cost

Annotation type: Bounding Box

Bounding Box

Polygon

Polyline

Landmark

Segmentation

3D Cuboid / LiDAR

Text & NLP

Video Labeling

De-identification

Volume: 10,000 items

1K100K250K500K

Avg. objects per image: 3

Scene complexity: Medium

Simple

Medium

Dense

QA level: Standard (2-tier)

Basic

Standard

Premium

Est. project cost

$900

indicative · USD

Base rate$0.030/item

Obj. multiplier×1.14

Complexity×1.00

QA level×1.00

Effective rate$0.034/item

vs. in-house (est. 50% saving)

$900 saved

Get Exact Quote →Request a Callback

Indicative only. Final pricing depends on labeling complexity, dataset specifics, and timeline. Read our pricing guide →

FAQ

Frequently Asked Questions

What data labeling services do you provide? ▾

We provide bbox annotation, polygon labeling, polylines, keypoints, 3D cuboids, semantic and instance segmentation, text tagging, LiDAR point cloud markup, and de-identification. These support image, video, and multimodal AI datasets across all major industries.

Are your annotation workflows secure? ▾

Yes, our facilities and workflows are aligned for ISO 27001, HIPAA, and GDPR, with encrypted SFTP transfers, role-based access controls, VPN tunnels, and full audit logs. All staff are NDA-signed permanent employees. Zero freelancers — ever.

How do you ensure the quality of labeled data? ▾

We implement multi-tier QA including expert review, automated checks, and feedback loops to deliver 99.5%+ accuracy consistently. Every batch goes through: primary reviewer → independent QA specialist → senior sign-off. Random 10% sampling audits are run on every batch. Read our governance guide.

Can you scale for large-scale AI projects? ▾

Yes. With 540+ in-house experts, we can rapidly scale to process millions of data points. We have labeled 810M+ images and 330M+ videos across global programs. We spin up a full team within 48 hours — no hiring or ramp-up lag.

Do you support custom labeling tool integration? ▾

Yes. Our team works with all major platforms — CVAT, Labelbox, Scale AI, Roboflow, V7 — and can integrate directly within your proprietary AI environment. We deliver in COCO JSON, YOLO TXT, Pascal VOC XML, custom JSON schemas, and any format required by your ML pipeline.

What is the difference between data labeling and data annotation? ▾

Data labeling and data annotation are often used interchangeably. Labeling typically refers to assigning class-level tags to data (e.g., "cat" or "dog"), while annotation covers richer markup such as rectangular annotations, polygons, keypoints, and semantic segmentation that captures spatial or structural context. Both are essential for training accurate supervised machine learning models. Read our full guide on AI data labeling.

Why outsource data labeling to India rather than building an in-house team? ▾

Outsourcing to an experienced India-based partner gives you immediate access to 540+ trained labelers and proven multi-level QA. You also get ISO 27001-aligned security and 40–60% cost savings versus building an in-house team. In-house hiring typically takes 4–8 weeks before a single label is created. Your ML engineers can focus on model architecture — we handle the ground truth. Read our pricing guide for a full cost comparison.

What output formats do you deliver for labeled data? ▾

We deliver ML annotation datasets in COCO JSON, YOLO TXT, Pascal VOC XML, CSV, custom JSON schemas, and any format required by your ML pipeline — including PyTorch, TensorFlow, and proprietary platform formats. Format specifications are agreed during the requirements phase.

What industries do you serve? ▾

We serve automotive, healthcare, agriculture, retail, robotics, GIS, media, sports, security, manufacturing, UAV, marine, finance, and education sectors. Serving enterprises across US · UK · Canada · Australia · Europe · Middle East · APAC · LATAM.

What types of machine learning projects do you support? ▾

We support supervised machine learning across computer vision — object detection, semantic segmentation, pose estimation, and OCR. We also cover natural language processing: NER, sentiment, and intent classification. Multimodal AI and reinforcement learning from human feedback (RLHF) are supported too. All ground truth datasets are delivered pipeline-ready for PyTorch, TensorFlow, and cloud ML environments.

Data Labeling
& Annotation
Services

Why Choose Precise BPO as Your Data Annotation Partner

Accuracy Consistently Above 99.5%

Start in 48 Hours — No Long Contracts

Zero Freelancers — 100% In-House

What Is Data Annotation & Labeling?

What Is Data Annotation & Labeling?

Types of Data Labeling

Data Labeling vs. Data Tagging

In-House vs Outsourced Labeling

Data Annotation Problems Slowing Your AI

Complete Data Annotation Services

Works With Your Existing Tools

Our Work, Across Every Labeling Type

Our Data Annotation & Labeling Process

Data Labeling Across Every Industry

Client Results — Before & After

Data Annotation Quality Assurance & Security Standards

Precise BPO vs Scale AI, Appen & Others

What Our Clients Say

Our Proven AI Training Data & Annotation Track Record

Data Labeling Pricing & Rates

Configure Your Project Cost

Outsource Data Labeling to India's Most Trusted AI Data Partner

Frequently Asked Questions

Data Labeling & Annotation Services

Why Choose Precise BPO as Your Data Annotation Partner

Accuracy Consistently Above 99.5%

Start in 48 Hours — No Long Contracts

Zero Freelancers — 100% In-House

What Is Data Annotation & Labeling?

What Is Data Annotation & Labeling?

Types of Data Labeling

Data Labeling vs. Data Tagging

In-House vs Outsourced Labeling

Data Annotation Problems Slowing Your AI

Complete Data Annotation Services

Works With Your Existing Tools

Our Work, Across Every Labeling Type

Our Data Annotation & Labeling Process

Data Labeling Across Every Industry

Client Results — Before & After

Data Annotation Quality Assurance & Security Standards

Precise BPO vs Scale AI, Appen & Others

What Our Clients Say

Our Proven AI Training Data & Annotation Track Record

Data Labeling Pricing & Rates

Configure Your Project Cost

Outsource Data Labeling to India's Most Trusted AI Data Partner

Frequently Asked Questions

Data Labeling
& Annotation
Services