Data Labeling & Annotation Services

Q: What is data annotation?

Data annotation is the work of labeling raw data (images, video, audio, text or 3D point clouds) so a model knows what it is looking at. The labels tell it which objects, patterns or meanings are present. See our complete guide to data labeling for a deeper overview.

Q: What types of data and annotation tasks do you support?

We handle image and video annotation (bounding boxes, polygons, semantic and instance segmentation, keypoints, object tracking). We also do 3D LiDAR point cloud labeling, text annotation (classification, named entity recognition, sentiment tagging) and audio transcription for computer vision, NLP and speech AI projects, all under one engagement. If your source material is scanned documents or paper records, our data entry and data conversion teams can digitize it before labeling starts.

Q: How do you maintain annotation accuracy at scale?

Every standard batch goes through three tiers. In Tier 1, annotators label from client-approved guidelines and self-review against a checklist. In Tier 2, an independent QA specialist does a blind review and measures IoU and inter-annotator agreement. In Tier 3, a senior specialist signs off and re-reviews a 10% sample of the batch. For bounding box work we target an error rate under 0.2%; the other annotation types score 98.9–99.4% on our benchmarks above.

Q: How do you manage large annotation projects over time?

Each project starts with an ontology and annotation schema built around your model's classes. From there you can add volume, send new data for active-learning cycles, or come back after retraining your model. Each addition runs as a new batch against the same locked class definitions, with agreement tracked per batch.

Q: Which industries do you annotate data for?

We label data for autonomous vehicles (LiDAR, camera fusion), healthcare AI (medical imaging, radiology), retail and e-commerce (product detection, shelf analytics), agriculture (crop and disease detection), satellite and geospatial imagery, financial document AI, manufacturing, robotics, surveillance and security, sports analytics and NLP products.

Q: How do you keep labels consistent across a team of annotators?

We keep labels consistent with written annotation guidelines, a documented class taxonomy, inter-annotator agreement scoring and gold-standard calibration sets. Before any production batch begins, annotators complete calibration tasks scored against gold data to verify their understanding of edge cases and class boundaries. QA reviewers flag annotation drift, class ambiguity and boundary inconsistencies during review. See our annotation governance framework for full details.

Q: What formats do you deliver annotations in?

We deliver in the format your ML pipeline requires: COCO JSON, Pascal VOC XML, YOLO TXT, LabelMe JSON, CSV and custom schemas. For video, we deliver MOT-format tracking files or frame-by-frame JSON. For NLP, outputs include CoNLL for NER, JSON for classification, and custom formats for intent/slot labeling. These formats work with standard PyTorch and TensorFlow data loaders, Roboflow and major cloud ML services.

Q: How is annotation pricing structured?

Pricing depends on dataset volume, the complexity of the annotation type, class density per image or document, QA depth and turnaround requirements. Common engagement models include per-image, per-object, per-frame, hourly and monthly retainer. All engagements include a free pilot batch before you commit. See our data labeling pricing guide or request a tailored quote.

Q: Are your workflows ISO 27001, HIPAA and GDPR aligned?

Our workflows are aligned with ISO 27001, HIPAA and GDPR requirements, and we can share security documentation and sign a BAA for HIPAA-scoped projects. Every annotator signs an NDA before project access is granted. Access is permission-scoped, so annotators only see the dataset segments assigned to them. Data moves only over encrypted channels such as SFTP and VPN, so your training data and model IP stay protected.

Q: Do I need an in-house team to manage the project, or can you run it end to end?

No in-house team required. You send raw images, video or text and receive labeled datasets back. We run the full engagement with our own NDA-bound annotators. Comparing providers first? Our guide to the top data annotation companies shows how the main vendors differ.

Overview

What We Do

At a Glance

Three QA tiers. 48-hour start.

QA tiers on every standard batch

Self-review, independent QA, senior sign-off

10%

Of each batch re-checked by a senior specialist

Sampling audit before delivery

0.92+

Inter-annotator agreement target (Cohen's kappa)

Tracked per batch

100

Images labeled free in the pilot batch

Pilot delivered in 24–48 hours

48h

Pilot starts within 48 hours of signed NDA

In-house team, no freelancers

Precise BPO has been in business since 2008 and has labeled training data for AI teams since 2017. Today we work with teams building computer vision, autonomous driving, medical imaging, NLP and LiDAR systems, all from our delivery center in Pune. Our round-the-clock shifts overlap working hours in the US, UK, EU and APAC. We build every dataset to your model specification, label schema and quality threshold.

Our annotators work across bounding boxes, polygons, semantic masks, keypoint landmarks, 3D cuboids and NLP tagging, including NER, intent and RLHF preference data for fine-tuning large language models. We can work in your annotation platform or ours.

For high-volume computer vision work, you can go from pilot to production with us without building a labeling operation of your own. Every batch arrives ready to load into your training pipeline.

🏷️

All Annotation Types, One Partner

Bounding box, polygon, semantic segmentation, keypoints, 3D cuboid, LiDAR, NLP, video tracking and de-identification, all under a single NDA and SLA.

✅

3-Tier QA on Every Standard Batch

Annotators self-review, an independent QA specialist reviews, and a senior specialist signs off before delivery.

🔐

Security & Compliance

Workflows aligned with ISO 27001, HIPAA and GDPR, with NDA-bound staff, role-based permissions, VPN access and audit trails for your proprietary datasets.

Start a free pilot

The Fundamentals

What is Data Annotation & Labeling?

Data annotation, also called data labeling, is the process of adding structured metadata to raw data (images, video frames, text, audio or point clouds) so that machine learning models can recognize and classify it accurately. Annotators apply bounding boxes, polygons, semantic masks, keypoints, text tags and attribute labels to raw datasets, giving the model correct examples to learn from. Reliable labels come from annotators trained on your taxonomy, which is why many AI teams hand this work to specialists instead of pulling engineers into manual tagging.

Bounding box labels drive object detection, pixel masks drive segmentation and keypoints drive pose estimation. NLP models depend on text annotation in the same way. Good labels also depend on domain knowledge: how edge cases are handled, how classes are defined and what schema your model expects.

Computer Vision

Bounding boxes, polygon masks, semantic segmentation and keypoint annotations power object detection, scene understanding and visual classification models across automotive, retail, medical and satellite domains.

LiDAR & 3D Annotation

3D cuboid annotation and point cloud labeling for autonomous vehicles, robotics and industrial automation, giving perception models the 3D ground truth they train on.

NLP & Text Annotation

Named entity recognition, intent classification, sentiment tagging, span labeling and RLHF feedback datasets for large language model training, chatbot development and enterprise NLP pipelines.

Output Formats

COCO JSON, YOLO TXT, Pascal VOC XML, CSV, or custom schemas, compatible with standard PyTorch, TensorFlow and Detectron2 data loaders, or your own pipeline.

Industry Coverage

Industries We Work With

We annotate data for autonomous vehicles, healthcare, agriculture, retail, geospatial, language AI, document AI and manufacturing, and we work from each industry's own label taxonomy.

Autonomous Vehicles & Mobility

We label camera, LiDAR and radar data for self-driving perception teams, from 2D boxes to 3D cuboids, at production volume.

Medical Imaging & HealthTech

We annotate radiology scans, pathology slides and surgical video under HIPAA-aligned workflows, tracing organs, tumors and anatomical landmarks for diagnostic and surgical-robotics models.

Agriculture & Precision Farming

We label satellite and drone imagery for crop detection, disease classification and yield estimation.

Retail, E-Commerce & Fashion AI

We tag product and catalog images for visual search, recommendation engines and inventory systems, including fashion and apparel attributes.

Satellite & Geospatial Imagery

We annotate satellite and aerial imagery: building footprints, road networks and land-use change for urban planning and environmental monitoring.

NLP, LLM & Conversational AI

We label text for LLM fine-tuning and conversational AI, including NER, intent classification, sentiment and RLHF preference data in 20+ languages.

Financial Services & Document AI

We label invoices, KYC documents and handwritten forms for OCR and fraud-detection pipelines, under the strict data controls financial clients require.

Manufacturing & Industrial AI

We label production-line images for defect detection and component classification, supporting predictive maintenance and visual inspection.

Common Problems We Fix

Problems That Slow Your AI Pipeline

Most teams run into the same problems when they scale labeled datasets. These are the ones we're usually brought in to fix.

Low-quality labels are hurting your model

Crowdsourced or freelance work looks cheap until your model's mAP drops and engineers end up re-labeling everything. In our standard process, someone other than the annotator reviews every batch before it reaches you. See how our QA works →

Scaling your dataset is taking too long

In-house teams hit capacity ceilings fast. Recruiting, onboarding and training annotators typically take 4–6 weeks before a single label is created. Your pilot can start within 48 hours of signing the NDA. See our annotation process →

Your vendor is failing security reviews

Medical, financial and other regulated projects often require data to stay in controlled environments, which rules out freelancers and crowd workers. Everyone on our team is a permanent, NDA-signed, background-verified employee. Data moves over encrypted SFTP, with role-based access, VPN tunnels and full audit logs. We can also handle HIPAA-aligned de-identification before labeling starts.

Training data is costing more than you planned

Our dedicated teams run $4–$8/hr, with QA, tooling and project management included, which is 40–60% below US/UK rates. See pricing →

Inconsistent labels are causing model regression

Labeling guidelines drift when a team grows and nobody owns the rules. We assign dedicated project managers who maintain guideline consistency across every specialist, batch and sprint.

Your model is failing on edge cases

Models trained on clean data fail on real-world noise, occlusion and rare classes. Our medical, automotive and agricultural specialists know where edge cases tend to appear and label them from the start, something generalist vendors often miss.

Not sure yet? We'll label your first 100 images at no cost.

✓ No commitment ✓ NDA signed first ✓ 100 images delivered in 24–48 hours

Start a free pilot →

Services

Our Annotation Services

We deliver labeled datasets in your required format, from bounding boxes to LiDAR and NLP.

⬜

Bounding Box Annotation

Rectangular boxes around objects for detection and classification.

Bounding box annotation →

🎨

Semantic Segmentation

Pixel-level class masks for autonomous vehicles, medical images and satellite imagery, at 99.2%+ accuracy.

Semantic segmentation →

🔷

Polygon Annotation

Precise outlines for irregular objects such as crops, vehicles, body parts and architectural elements.

Polygon annotation →

〰️

Polyline Annotation

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

Polyline annotation →

📍

Landmark & Keypoint

Keypoints for pose estimation, facial recognition and human action recognition.

Landmark annotation →

📦

3D Cuboid & LiDAR

3D point cloud labeling for robotics, autonomous vehicles and industrial automation.

3D cuboid & LiDAR annotation →

💬

Text & NLP Annotation

NER, intent classification, sentiment tagging, span labeling and RLHF preference data for LLM training and fine-tuning.

Text annotation →

🎬

Video Annotation & Tracking

Frame-by-frame multi-object tracking, action recognition, temporal event labeling and behavior analysis for surveillance, sports, autonomous driving and media AI. Supports per-frame exports and interpolated tracks.

Ask about video annotation →

🔒

Data De-identification

PHI and PII redaction for medical records, financial documents and legal filings.

De-identification →

🏥

Medical Image Annotation

Lesion detection, organ segmentation and radiology AI datasets, labeled in HIPAA-aligned workflows.

Medical annotation →

🚗

Autonomous Vehicle Annotation

Object detection, LiDAR, lane detection and 3D cuboids for ADAS and self-driving perception stacks.

Autonomous vehicle annotation →

🌾

Agriculture Annotation

Crop monitoring, disease detection and drone imagery labeling for precision farming models.

Agriculture annotation →

👗

Fashion & Apparel Annotation

Garment tagging, attribute labeling and visual-search datasets for fashion AI and e-commerce recommendation engines.

Fashion annotation →

⚽

Sports AI Annotation

Player tracking, ball detection, skeletal keypoints and event labeling for sports analytics, coaching tools and broadcast AI.

Sports annotation →

🔞

Explicit Content Annotation

Content classification and tagging for moderation pipelines, built for platforms and trust & safety teams.

Explicit content annotation →

Platform Compatibility

Works With Your Existing Tools

We work inside the platform your team already uses, including CVAT, Labelbox, Roboflow, SuperAnnotate and V7, and deliver in the format your ML pipeline requires.

🛠️ Annotation Platforms

CVATOpen-source · self-hosted

LabelboxEnterprise annotation platform

RoboflowCV dataset management

SuperAnnotateHigh-throughput labeling

V7 DarwinAuto-label + review

Your Own PlatformProprietary or in-house tools

📄 Output Formats

COCO JSONStandard CV benchmark format

YOLO TXTYOLOv5 / v8 / Ultralytics

Pascal VOC XMLLegacy & academic pipelines

Custom JSON / CSVAny schema you specify

LiDAR / PCDPoint cloud output formats

NLP / CoNLL / JSONLText & NER outputs

🔐 Secure Transfer

Encrypted SFTPPrimary delivery channel

AWS S3Direct bucket delivery

Google Cloud StorageGCS bucket transfer

Secure PortalRole-based access control

VPN TunnelFor restricted-network environments

Full Audit LogsEvery access recorded

Choose the Right Annotation Type

Bounding Box, Polygon or Segmentation: Which Do You Need?

The annotation type you pick affects both model performance and labeling cost. This table compares the main types on shape, speed, cost and precision. If bounding boxes are your main format, our bounding box annotation guide covers the common mistakes and how to avoid them.

Criteria	Bounding Box	Polygon / Instance	Semantic Segmentation	Keypoint / Landmark
Shape	Rectangle: axis-aligned or rotated	Vertex-traced outline per object instance	Pixel-level mask per class	Coordinate points on joints or landmarks
Best for	Bounded objects: vehicles, people, products	Complex shapes: irregular or overlapping objects	Scene understanding: drivable area, background separation	Pose estimation, facial analysis, AR/VR datasets
Annotation Speed	Fastest: single drag per object	Moderate: vertex-by-vertex	Slowest: pixel-by-pixel fill	Fast: point placement per joint
Cost Efficiency	Highest: minimal effort per object	Medium: scales with shape complexity	Lowest: most effort per image	High: fast but requires domain expertise
Boundary Precision	Object-level (includes background)	High: shape-accurate	Pixel-level	Point-exact per joint
Video / Temporal	Excellent: fast frame-by-frame tracking	Good: higher effort per frame	Poor: very high effort per frame	Excellent for pose tracking across frames
Common Use Cases	Retail, ADAS, medical, surveillance, drone imagery	Autonomous driving, agriculture, satellite, fashion	Autonomous vehicles, medical imaging, aerial mapping	Human activity recognition, sports analytics, AR/VR
Our service page	Bounding Box	Polygon	Segmentation	Landmark

Not sure which annotation type fits your project? Talk to one of our annotation specialists and we'll recommend an approach based on your data, model architecture and accuracy targets. For lane markings and other linear features, see polyline annotation, which isn't covered in this table.

From Brief to Delivery

Annotation Workflow

Here is how a project moves from requirements review to final delivery.

Requirements Review

We define annotation goals, object classes, labeling taxonomy, quality thresholds and output schemas with your ML team before any labeling begins, so every annotator works to a single, locked specification.

Class taxonomy Labeling rules Edge-case handling SLA setup

Secure Data Intake

We receive raw images, video, LiDAR or text files over encrypted SFTP, normalize them to standard formats and organize them into batches on secure infrastructure that only NDA-bound staff can reach.

Encrypted SFTP NDA protection Audit logs Role-based access

Expert Labeling

Our in-house annotators label your data in the tool you specify or on our own platform, using bounding boxes, polygons, segmentation masks, keypoints, LiDAR cuboids or text labels as the project requires. Dedicated domain specialists handle medical, automotive, agricultural and NLP datasets.

Bounding box Segmentation LiDAR cuboids NLP & RLHF

3-Tier Quality Check

Every standard batch passes annotator self-review, an independent blind review with IoU and agreement metrics, and senior sign-off. On bounding box work we target an error rate under 0.2%.

Self-review Independent QA Senior sign-off 10% sampling audit

Client Review

You review each annotated batch and send feedback, and we work through it in structured revision cycles. We update the guidelines and lock them before the next batch begins, which keeps labels consistent across the full dataset.

Batch submission Feedback loop Revision cycles Guideline lock

Final Delivery & Ongoing Support

We deliver outputs in your required format through secure transfer, within agreed SLAs. We also support active learning pipelines, model retraining cycles and extended annotation programs.

COCO / YOLO / VOC Custom schema Secure delivery Ongoing support

Case Studies

Client Results: Before and After

What changed for clients after they moved annotation to us. Names withheld under NDA.

🇺🇸

Autonomous Driving · USA

Computer Vision

Object Detection: Vehicles, Pedestrians & Road Users

We labeled vehicles, pedestrians and other road users with pixel-level masks, following the client's class definitions. Annotation for autonomous driving →

Before

Model accuracy at 78%, not ready for deployment

After

Accuracy improved to 96% after retraining on the new labels

+18 pts

Accuracy Gain

96%

Final Accuracy

🇬🇧

Retail AI · UK

Catalog Tagging

Product Catalog Tagging: 2.1× CTR Improvement

The client's search relevance had plateaued on a catalog of 500K+ products with inconsistent, partly manual tagging. We rebuilt the attribute set from scratch and tagged the full catalog under a locked taxonomy. Retail catalog annotation →

Before

Manual catalog management with low search relevance

After

2.1× increase in product-discovery click-through rate

2.1×

CTR Increase

500K+

Products Tagged

🇨🇦

Healthcare AI · Canada

HIPAA-aligned

Medical Imaging Annotation: +18% Model Recall

Polygon and pixel-level tagging, reviewed by QA specialists. Medical imaging annotation →

Before

Diagnostic model missing edge-case anomalies

After

+18% recall improvement in cancer screening model

+18%

Recall Improvement

🇦🇺

Manufacturing QC · Australia

Dense Segmentation

Defect Detection: 35% Fewer Manual Inspections

Dense defect segmentation on production line images to train automated inspection AI. Semantic segmentation for defect detection →

Before

High manual inspection overhead with missed defects

After

35% reduction in manual inspections, increased throughput

−35%

Manual Inspections

🇩🇪

AgriTech · Germany

Polygon Segmentation

Crop/Weed Differentiation: Precision Farming AI

A German AgriTech team needed multi-class boundaries fine enough to separate young crops from weeds in dense drone imagery, a task their previous vendor's polygons couldn't hold consistently. Crop and drone imagery annotation →

Before

No reliable automated crop-health monitoring

After

31% improvement in crop-health monitoring and yield prediction

+31%

Crop-Health Monitoring

3M+

Images Labeled

🇰🇷

Robotics · South Korea

3D Cuboid

Object Recognition: Robotic Grasp Optimization

3D cuboids and keypoints labeled for precise grasp points across varied object types and orientations. 3D cuboid annotation for robotics →

Before

Inconsistent grasp success in automated picking lines

After

Successful robotic picks increased by 29%

+29%

Pick Success Rate

🇫🇷

Insurance AI · France

Instance Segmentation

Claims Automation: Review Time Cut to Under 24 Hours

A 5–7 day manual claims review was the client's biggest bottleneck. Instance segmentation of exterior vehicle damage let their model flag and route claims automatically, with adjusters reviewing only the edge cases. Segmentation for claims automation →

Before

Manual claims review taking 5–7 business days

After

Claims reviewed in under 24 hours, with adjusters handling only edge cases

5–7 days

Review Time Before

<24h

Review Time After

Quality Standards

How We Keep Labels Accurate

Every labeling type has its own quality benchmark, tracked project by project. We publish these figures so you can check them against your own acceptance criteria.

On every project, our 3-tier QA targets inter-annotator agreement of 0.92 or higher (Cohen's kappa).

T1: Primary Annotator Review

First-pass labeling with embedded rule checks. Self-review against the guideline checklist before handoff.

T2: Independent QA Specialist

A separate specialist reviews blind, runs IoU and pixel-diff checks, and measures inter-annotator agreement per batch.

T3: Senior Sign-Off + 10% Sampling Audit

A random 10% of every batch is re-reviewed by a senior specialist, and any drift is flagged before delivery.

Request QA documentation →

Accuracy by Annotation Type

Bounding Box Labeling (target)99.8%

Polygon Labeling99.4%

Semantic Segmentation99.2%

Text & NLP Labeling99.3%

LiDAR Point Cloud99.1%

Video Multi-Object Tracking98.9%

How Each Type Is Validated

⬜ Bounding Box: IoU threshold checks per batch
🎨 Segmentation: Pixel-level diff against reference contours
📦 LiDAR / 3D Cuboid: Point-cloud overlap ratio + specialist sign-off
💬 NLP / Text: Inter-annotator agreement ≥ 0.92 Cohen's kappa

Evaluating Annotation Quality

If you're comparing vendors, our Data Annotation Quality Standards guide covers what to ask about consistency and validation before you commit.

How the Options Compare

In-House Team vs. Generic BPO vs. Precise BPO

Three ways to build AI training data, compared on the points that decide timelines and cost. This is a general comparison; check any vendor's SLA before you decide.

Criteria	In-House Team	Generic BPO	Precise BPO
Time to first label	4–6 weeks	1–2 weeks	Pilot starts within 48 hours of NDA; delivered in 24–48 hours
Cost vs. in-house	Baseline (highest)	Lower, but markups apply	40–60% below US/UK rates
AI domain expertise	Depends on hiring	Often generalist	In-house, domain-trained annotators
QA process	Depends on team maturity	Often one review pass	3-tier QA, target of up to 99.8%
Scale-up speed	Months (hiring cycles)	Days–weeks	Days
Security alignment	Depends on your own controls	Confirm in the SLA	ISO 27001-, HIPAA- & GDPR-aligned
Freelancer risk	None (in-house)	Ask about subcontractors	No freelancers
Free pilot	N/A	Sometimes offered	100 images free
Dedicated PM	Internal only	Shared resource	Dedicated from Day 1

Want to see how it works on your data? Start with a free 100-image pilot.

Start a free pilot

Client Testimonials

What Our Clients Say

Feedback from AI teams that moved their annotation work to us.

Their object detection labels came out above our internal benchmarks. They picked up our class hierarchy quickly, and in review they flagged edge cases our own team had missed. We started with a 10K-image batch and we're now past 2M frames, with quality holding steady.

🇺🇸 R.M. — Head of AI Data Operations

Autonomous Vehicle Company, Silicon Valley, USA

We didn't want a vendor that only claimed compliance. Precise BPO gave us documentation and signed a BAA, and their annotators understood the clinical context of what they were labeling. Within three months of retraining, our model's recall was up 18%.

🇨🇦 D.P. — Chief AI Officer

Medical Diagnostics Platform, Toronto, Canada

We'd already tried two crowdsourcing platforms, and the results were inconsistent from batch to batch. Precise BPO's polygon annotations for our crop segmentation model stayed consistent across 3M+ images, and our project manager was available across time zones.

🇩🇪 M.T. — CTO & Co-founder

AgriTech AI Startup, Munich, Germany

Why Precise BPO

Why AI Teams Work With Us

Every annotator works from a locked taxonomy before labeling begins, and every batch goes through the same 3-tier QA unless you ask for a lighter review. That consistency is what lets clients scale from a pilot to millions of images while holding label quality.

Start a free pilot

In Business Since 2008

We started with data entry and document processing and have labeled AI training data since 2017, now covering bounding boxes, polygons, LiDAR point clouds and text datasets.

540+ In-House Team

Dedicated, background-verified, NDA-signed staff across all our service domains, with trained annotators on every labeling project. No freelancers or crowdsourced workers.

Security & Compliance

NDA-bound staff, encrypted transfer and audit logs, with a BAA available for HIPAA-scoped medical imaging projects.

98.9–99.8% Accuracy by Type, Checked Per Batch

Each standard batch ends with a senior sampling audit.

Lower Cost Than In-House

Our costs are 40–60% below US/UK rates because you don't carry recruitment or tooling overhead.

Works in Your Tools

We label in the platform you already use and deliver in the format your pipeline loads.

Rates

Project Pricing

Per-item and hourly rates with no hidden fees. Volume discounts start at 50K items.

Annotation Type	Rate Range (USD)	Best For
Bounding Box	$0.01 – $0.06	Object detection and tracking
Polygon / Instance	$0.08 – $0.35	Precise shape outlines, irregular objects
3D Cuboid / LiDAR	$0.12 – $0.50	Robotics, autonomous vehicle 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, PHI redaction
Text & NLP	Quoted per project, or hourly at $4–$8/hr	NER, intent, RLHF preference data
Video Tracking	Quoted per project, or hourly at $4–$8/hr	Multi-object tracking, action labeling

Team Type	Rate Range	Best For
Annotation Specialist	$4 – $6 / hr	High-volume standard annotation
Senior / Domain Specialist	$6 – $8 / hr	Medical, LiDAR, complex segmentation
QA Reviewer	$5 – $7 / hr	Independent validation and auditing

The hourly model suits dedicated team engagements, ongoing programs, or mixed annotation types. Minimum 160 hrs/month. NDA, IP rights and SLA included.

Rates shown are indicative ranges. Final pricing may vary by complexity, volume and domain. Contact us for a project-specific quote →

Configure Your Project Cost

Annotation Type

Volume (items)

QA Level

$300

Est. Project Cost (USD)

$0.030

Effective Rate / Item

40–60%

Below US/UK Rates

Get a Quote →

Common Questions

Frequently Asked Questions

Answers on scope, accuracy, formats, security and pricing: the questions we get asked most.