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Object detection, image segmentation, bounding boxes, polygons, landmarks, and semantic segmentation for autonomous vehicles, facial recognition, and medical imaging.

Named entity recognition, sentiment analysis, intent classification, and context aware annotation for chatbots and text-based AI models.

Transcription, timestamping, speaker identification with support for multiple languages and accents to fuel voice assistants and speech analytics.

Data Labelling for Computer Vision

From object detection to image segmentation, we annotate visual datasets to power the next generation of AI-driven visual intelligence.
Our computer vision labeling services provide the foundation for autonomous vehicles, facial recognition systems, medical imaging diagnostics, and countless other applications that rely on accurate visual interpretation.

Bounding boxes and polygon annotation
Landmark identification and tracking
Semantic and instance segmentation
High accuracy across diverse image formats
Support for various lighting and environmental conditions

why data labelling

Transformative Benefits of Data Labelling

Precise Predictions

Improved Data Usability

Frictionless Scalability

Cost Optimization

FOCUSED INDUSTRIES

Industries We Empower

01

Autonomous Vehicles

Train perception models for object detection, lane tracking, and pedestrian recognition to advance self driving technology.

02

Healthcare

Annotate medical scans and patient data for diagnostic and predictive modeling that saves lives.

03

Retail & E-commerce

Enable personalized recommendations and smart visual search through expertly labeled product data.

04

Security & Surveillance

Improve accuracy of facial recognition and threat detection systems for enhanced safety.

05

Agriculture

Label satellite and drone imagery for crop health monitoring and yield prediction optimization.

06

Robotics

Train robots to recognize environments, gestures, and objects through visual and sensor data labeling.

07

Finance & Banking

Tag documents, transactions, and communication data for fraud detection and process automation that protects assets and streamlines operations

08

Insurance

Label claims documents, inspection images, and policy data to streamline underwriting processes and improve risk assessment accuracy.

09

Manufacturing & Logistics

Enhance automation and predictive maintenance using labeled operational data to minimize downtime and optimize supply chains.

10

Wildlife Conservation

Identify species and monitor animal behavior using image and video datasets to support conservation efforts and ecological research.

Power Your AI Models with Quality Labeled Data!

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WHY US

Why Choose SotaTek Data Labelling Services?

+99%

Annotation Accuracy

24/7

Support Availability

100%

Data Security

Our Competitive Advantages

01

High Quality & Reliability

99%+ annotation accuracy through multi-level quality assurance and expert human verification.

02

Faster Turnaround Time

Agile workflows that meet tight deadlines without compromising quality or accuracy.

03

Cost-Effective Pricing

Flexible engagement models to suit your budget and project scope requirements.

04

Assured Data Security

Compliance with ISO 27001, GDPR, and HIPAA for complete data confidentiality.

05

Scalable Operations

Capability to handle projects from pilot to enterprise scale with consistent quality.

06

Global Presence

Trusted by clients across USA, UK, Europe, and APAC regions.

Our Quality-First Approach

Deep understanding of your specific use case and annotation guidelines
Specialized annotator training for domain-specific accuracy
Multi-layer annotation with advanced tools and methodologies
Rigorous multi-level QA checks and validation protocols

Seamless handoff with ongoing support and iteration

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our quality

Partner with SotaTek

“

Get reliable, domain-specific, and cost-effective data labeling services tailored to your business needs. Our team of expert annotators and quality assurance specialists is ready to accelerate your AI innovation journey.

Whether you're launching a pilot project or scaling enterprise-wide AI initiatives, SotaTek provides the precision, scalability, and security you need to succeed. Join leading organizations across the globe who trust us with their most critical AI training data.

Our Portfolio

portfolio1

3D Cloud Point Labelling

SotaTek provides Data QC and Labelers to annotate and verify the annotated data, such as:

  • Checking 3D LiDAR data
  • Checking traffic light signal data
  • Checking road lane data

>>> 1% Error Rate

portfolio2

Tomatoes Annotation

SotaTek works with client to develop cutting-edge deep learning algorithms tuned to identify tomatoes in images.

  • Class to annotate: Tomatoes
  • Type: Polygon

>>> 5000 images
>>> 1% Error Rate

portfolio3

Image Annotation For ADAS

SotaTek performs annotation for vehicle, pedestrian, cyclist and rider for a Japanese company.

  • Class to annotate: Vehicle, pedestrian, ...
  • Type: Bounding box

>>> 23K images
>>> 1% Error Rate

Testimonials

What Our Customers Say

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We were looking for an offshore company partner and finally we found SotaTek and had started working with them since September 2022. ( ... ) They helped us pay down the technical debt and also accelerate our development. Without SotaTek, we wouldn't be able to actually handle so many technical initiatives. Thanks to SotaTek, we were able to deliver all of them in time. So if you are looking for an offshore engineering team. I'd recommend SotaTek.

wesang


Andrew Jeon

CEO - Wesang

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Lemon Healthcare aims to extend its presence beyond South Korea into Southeast Asia. For that reason, we decided to choose SotaTek. SotaTek is already making a name for itself in Vietnam as a professional outsourcing company. We needed the ability of SotaTek. Through this, we intend to localize and expand smart hospital ERP in Southeast Asia and help both companies grow.

Lemon-healthcare-1


Steve Hong

CEO - Lemon Healthcare

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SOTATEK JSC successfully helped numerous clients to accelerate their digital transformation. Several apps and platforms were launched and received positive feedback from the end users, ultimately increasing customer base and revenue by 140%. Overall, they had an effective and collaborative workflow.

leo vu


Leo Vu

CEO of Fizen.io

SotaTek is our most important partner, enabling us to improve our products through joint development. We have been collaborating for over two years, and I have complete confidence in the technical abilities of the engineers working with us.


Kosuke Saito

President and CEO of Brainlab

SotaTek is a reliable software delivery partner to work with. They are honest and easy to communicate. There are little to no administrative hurdles & very flexible. They stick to their commitments and deliver reliably.


Josef Puchinger

CTO of Privé Technologies

We gave our ideas, vision and SotaTek’s comprehensive technical knowledge made it possible for Travala to build and incorporate multiple areas of blockchain within the platform. What even better is that changes were done in a timely manner if requested, which undoubtedly met the international standards. It feels natural to have a partner with the same mindset as us, who we can always trust and ensure that nothing is impossible.


Matthew Luczynsky Ba (Hons)

CEO & Co-Founder of Travala.com

Explore Industry Insights and Updates

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Frequently Asked Questions

Data labelling is the process of assigning meaningful tags or categories to raw data (images, text, audio, or video) so machine learning models can learn from it. Labels can describe what the data contains (e.g., “car,” “positive sentiment,” “speaker A”) or where it appears (e.g., bounding boxes, segmentation masks).
Machine learning models rely on labelled examples to recognize patterns and make accurate predictions. High-quality labels improve model performance, reduce training time, and help teams iterate faster with fewer errors in production.
Common examples include drawing bounding boxes around objects in images, creating segmentation masks for medical scans, tagging text for sentiment or intent, and transcribing speech with timestamps. Other examples include named entity recognition (NER), keypoint/landmark annotation, and speaker diarization in audio.
In practice, the terms are often used interchangeably. “Data annotation” is usually broader and can include detailed information like bounding boxes, polygons, segmentation masks, or timestamps, while “data labelling” often refers to assigning categories or tags (e.g., classification). Both aim to create AI-ready datasets.
Label quality directly affects how well a model learns. Noisy, inconsistent labels can lead to poor accuracy and unreliable predictions. Consistent guidelines and strong QA reduce bias and errors, improving metrics like precision/recall and overall model robustness.