Let’s Corporate to Get High-quality AI Data Annotation Projects!

LTS GDS delivers high-precision data annotation across industries, empowering AI models to learn faster, predict smarter, and perform with greater accuracy.

Trusted by Industry Leaders Worldwide

Diverse Data Types for Annotation

No matter the data type, our team provides accurate annotations tailored to your ML goals.

Image & Video
Image annotation involves detailed labeling of images, while video annotation enhances computer vision capabilities by labeling sequences of images to ensure accurate model training. Common use cases include object detection, image classification, and facial recognition.
  • Bounding Box
  • Polygon
  • Polyline & Spline
  • 3D Cuboid
  • Semantic Segmentation
  • Instance Segmentation
  • Panoptic Segmentation
  • Landmark
  • LiDAR Point Cloud
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Audio
Audio refers to recordings of various sound types. Audio annotation helps your model understand different natural languages and voices from diverse demographics. This process supports tasks like speech transcription, emotion detection, and language identification.
  • Speech Recognition
  • Sentiment Analysis
  • Audio-To-Text Transcription
  • Event and Sound Classification
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Text
Text data includes various types of documents in multiple languages (including words and numbers). Text annotation improves generative AI model’s ability to understand context and perform NLP tasks accurately. Key tasks involve text categorization, sentiment annotation, and optical character recognition.
  • Text Classification
  • Sentiment Analysis
  • Question Answering
  • Named Entity Recognition
  • Audio-To-Text Transcription
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Code
Code refers to data assigned with a label, number, or symbol. Code annotation helps build fine-tuned datasets for coding LLMs to achieve better performance. It supports activities such as prompt generation, answer verification, and dialogue evaluation.
  • Prompt Generation
  • Prompt Verification
  • Answer Generation
  • Answer Verification
  • Dialogue Generation
  • Dialogue Evaluation
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Time Series
Time series data is a sequence of data points collected or indexed over time. Time series annotation involves adding time-related labels, metadata, or descriptive information. Typical applications are event annotation, anomaly detection, and temporal interval annotation.
  • Event Recognition
  • Time Series Classification
  • Time Series Segmentation
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Multimodal
Multimodal annotation involves labeling data across multiple formats (e.g., text, image, audio). It enables AI models to understand and process information from different sources. Techniques used in multimodal annotation are caption generation, multimodal search, and speech-text alignment.
  • Image Captioning
  • Transcription
  • Action Recognition
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Our Data Annotation Services Workflow

Our quality-first workflow that delivers high-accuracy annotations at scale.

Requirements Analysis
Pilot Testing & Agreement
Team Assembly & Training
Project Setup & Communication Framework
Execution & Monitoring
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Quality Assurance & Delivery

Dedicated project manager from LTS GDS conducts a comprehensive assessment to understand your business specific needs and project requirements. We analyze your data types, annotation specifications, quality standards, timeline, and deliverable expectations. Based on this analysis, we propose tailored AI data annotation solutions and provide expert consultation before project initiation.

Our vetted engineers kick off a pilot project using your sample dataset to demonstrate our capabilities and validate our approach. Our data annotators complete this small-scale test, allowing businesses to evaluate our work quality and methodology. Following delivery, our team leader collects your feedback to refine project specifications and finalize the Service Level Agreement (SLA) and contract terms.

Our project manager and HR department carefully select team members based on project's timeline, scope, and specific requirements. We then conduct comprehensive training sessions led by battle-hardened team leaders to ensure all annotators understand the guidelines, quality standards, and project objectives before beginning work.

Clear communication protocols between both parties are established, including regular check-in schedules, reporting procedures, and escalation processes. Both teams are involved in creating a detailed project timeline and implementing tracking systems that will be used consistently across all delivery teams throughout the project lifecycle.

Our annotation team executes the project according to the agreed plan while monitoring progress and key performance metrics. The dedicated project manager from LTS GDS maintains backup solutions for unexpected challenges and provides regular progress reports to keep businesses informed and enable timely adjustments when needed.

All annotated datasets undergo strict multi-stage quality assurance processes before delivery. Upon project completion, we conduct feedback sessions to gather insights and testimonials, helping us improve our services for future projects.

Why LTS GDS for Your AI Data Annotation Projects?

Trusted by global tech leaders for our skilled teams, flexible pricing, and enterprise-grade quality.

Quality-first Strategies​

We prioritize quality, as reflected in our 99% accuracy, prestigious awards, strict four-step review process, and DEKRA's Certificate of Conformity for Data Labeling.​

Security Standard​s​

We safeguard your data confidentiality by signing a Non-Disclosure Agreement (NDA) and enforcing stringent security measures in full compliance with ISO 27001 and GDPR standards.​

Dedicated Teams​

We assemble dedicated, multi-tier teams by blending experienced leaders with a well-trained team, ensuring efficient management, scalability, and adaptability to business needs.​

Cost-effectiveness

Take advantage of competitive labor costs, favorable tax policies, and flexible pricing models to optimize overseas IT outsourcing projects and meet diverse client requirements.​

Wall of Achievement

99%

Accuracy

5M+

Data Units

11

Countries

200+

Projects

Our Case Studies

Explore real-world success stories where our data annotation services powered innovative AI solutions across industries.

[Data Annotation] Apply segmentation techniques to annotate automotive datasets 
12 - 11 - 2024
What the client needs  To gain a competitive edge in the autonomous vehicle race, the demand for high-quality data annotation services for AI models is rapidly increasing. So, it is...
RPA-Powered Inventory Management for Manufacturing
06 - 11 - 2024
Business Challenges Over 100 of their warehouse staff are currently burdened with manually managing thousands of inventory items, shipping units, and warehouse providers. This process is time-consuming, resource-intensive, and prone...
[RPA] Accelerating Invoice Processing and Stock Reporting in the Pharmaceutical Industry 
31 - 07 - 2024
Business Challenges  They encountered two primary challenges:  Accounting Operations: The internal accounting team had to process manually over 10,000 invoices daily. Specifically, matching SAP system invoices with purchase orders and contract...
[RPA] Enhancing Purchase Invoices Data Entry in Retail
21 - 05 - 2024
Business Challenges Managing and processing purchase invoices can be a demanding task for any business. In the case of a supermarket with millions of invoices from retail buyers and suppliers,...
[Data Annotation] Smart Transportation Systems Project
21 - 05 - 2024
What the client needs The company will use artificial intelligence technology to make driving safer; therefore, they need a high-quality data set that provides AI with the necessary information to...
[RPA] Issuing Motor Vehicle Insurance Online with RPA       
21 - 05 - 2024
Business Challenges   Our client sought a specialized RPA vendor to optimize their motor vehicle insurance issuance processes. Their goal was to seamlessly integrate Robotic Process Automation into their current system,...
[Data Annotation] Pizza ingredients annotation 
21 - 05 - 2024
What the client needs  The customer was developing an AI model to identify pizza ingredients and calculate their nutritional value using image segmentation. This allows customers to compare calorie consumption...
[RPA] Revolutionizing Daily Reporting in Banking
29 - 01 - 2024
Business Challenges  With a presence in over 160 global offices, our client serves a vast customer base of 5 million in Japan, offering them a range of financial services. This...
US Vehicle Annotation
29 - 01 - 2024
What the client needs Our customer requests us to label a dataset of transportation and vehicles in the long-term data annotation project. They were looking for a data labeling vendor...
[RPA] Optimizing data entry processing in banking
29 - 01 - 2024
Business Challenges Our client has over 200 branches operating in Japan and abroad. As a result, they have to manually process a significant amount of input data every day, taking...
Annotating 100,000 Runways Images for AI-Powered Flight
29 - 01 - 2024
What the client needs Our client sought a vendor proficient in annotating runways using a semantic segmentation technique. This is a specialized autopilot project with the following key requirements:   An...

Our Tools and Technologies

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FAQs about Data Annotation Services for AI/ ML Models

What is data annotation?

Data annotation is the process of labeling various data types such as text, images, audio, or video to enable AI models to comprehend and learn from them. It's a crucial step in training AI and machine learning models to recognize patterns, make predictions, and automate decision-making.

Why is data annotation important for AI and machine learning?

High-quality annotated data is fundamental to the accuracy and reliability of AI systems. Without properly labeled data, machine learning algorithms cannot learn effectively, which can result in poor model performance and inaccurate outputs.

What types of data can be annotated?

We offer annotation services for diverse data types, including image, text, audio, video, time series, code, and multimodal annotation, etc.

What is the difference between manual and automated annotation?

Manual annotation is performed by trained experts and delivers higher accuracy often up to 99%, making it ideal for projects that demand precision and quality. Automated annotation is faster but less reliable for complex tasks, often requiring human review.

How do you ensure annotation quality and accuracy?

We ensure annotation quality and accuracy through multi-layered quality control mechanisms. These include a comprehensive QA process (self-check, cross-review, vertical review, and final random inspection), extensive annotation guidelines and training, and regular audits and performance tracking.

Awards & Certifications

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