Top 50 Data Labeling Service in Asia

Cappersoft provides high-quality annotated datasets for training AI and machine learning models, specializing in image, video, text, audio, and document processing. The company addresses the need for precise data labeling to enhance the accuracy and efficiency of AI applications across various industries, including automotive, healthcare, and e-commerce.

Tasq.ai provides a configurable AI flow platform that integrates decentralized human guidance with best-in-class machine learning models to enhance data labeling and model accuracy. The platform addresses the challenges of scaling AI processes and ensuring ethical oversight, enabling organizations to optimize their AI workflows efficiently.

SuperAnnotate is an AI data platform that integrates dataset creation, curation, and model evaluation into a single workflow, enabling users to build and fine-tune high-quality models efficiently. The platform addresses the challenges of data annotation and model performance assessment by providing customizable tools and access to a global marketplace of trained annotation teams.

The startup operates a cloud-based computing platform that provides AI-driven solutions for researchers and enterprises, focusing on large language model development, programmatic data labeling, and machine learning testing. It offers high-performance computing resources, including access to powerful GPUs and virtual machines, while promoting e-waste reduction through environmentally friendly practices.

DataLoops provides a data management and annotation platform that automates the preprocessing and curation of unstructured visual data, enabling the rapid generation of machine-readable datasets. This solution enhances the efficiency of AI application development by streamlining data pipelines and integrating human feedback for improved accuracy.

Hirundo offers a Machine Unlearning Platform that enables users to identify and remove unwanted data from AI models without the need for retraining. This technology addresses data labeling issues that compromise model accuracy and efficiency, allowing data science teams to optimize their datasets and maintain compliance with regulations.

The startup offers a visual recognition platform that autonomously processes diverse visual data, including infrared and X-ray images, while accurately tagging objects of interest. This technology enhances operational efficiency and ensures high-quality results for clients across various industries.

The startup offers a no-code platform for managing machine learning operations, enabling users to annotate, train, and deploy deep learning models using unstructured data like medical images and satellite imagery. This solution simplifies the process of fine-tuning and deploying deep neural networks, making it accessible for clients without extensive technical expertise.

Nansen is a blockchain analytics platform that utilizes wallet labeling and on-chain data querying to provide crypto investors with actionable insights and real-time alerts on market movements. By enabling users to identify significant wallet activities and trends across multiple blockchains, Nansen helps investors make informed decisions and mitigate risks in their portfolios.

Neurolov offers a cloud‑native platform that runs curated machine‑learning models on genomics, transcriptomics, proteomics and phenotypic data to produce predictive biomarkers, pathway activity scores, and compound efficacy forecasts. The service automates data ingestion, preprocessing, feature engineering and model inference, delivering results through interactive dashboards and API endpoints for integration with LIMS and downstream analysis. By providing a managed, scalable compute environment with versioned model registries and GxP/HIPAA compliance, it shortens the turnaround time for drug discovery teams.

DagsHub is a collaborative platform that enables data scientists to manage, annotate, and version unstructured datasets while tracking experiments and model performance. By streamlining data workflows and integrating with existing AI tools, DagsHub enhances data quality and accelerates the development of machine learning models.

Rubrik's Data Security Posture Management (DSPM) platform provides continuous visibility and control over sensitive data across on-premises, cloud, and SaaS environments, enabling organizations to identify and remediate data exposure risks. By automating the discovery and classification of sensitive data, the solution minimizes the potential impact of cyberattacks and ensures compliance with data privacy regulations.

Lemonilo manufactures snack, noodle, and ready‑to‑eat products reformulated with low‑glycemic, high‑fiber, plant‑based ingredients, using low‑temperature extrusion and high‑pressure processing to retain nutrients and extend shelf life. The company distributes these affordable, healthier FMCG items through modern trade and e‑commerce channels, providing QR‑code labeling for transparent nutrition information.

Syncell develops the Microscoop® platform, which utilizes automated photo-biotinylation for high-precision microscopy-guided proteomic discovery at cellular and subcellular levels. This technology enables the unbiased identification of protein constituents in tissue samples, addressing the limitations of traditional proximity labeling and mass spectrometry methods in understanding disease-associated protein interactions.

Visual Layer provides a visual data management platform that utilizes a CPU-only graph engine to index and analyze large datasets of images and videos, enabling efficient organization and insight extraction. The platform automates data curation, reducing the time spent on manual processes by up to 90% and improving model performance by over 50% through high-quality, curated visual datasets.

The startup offers a data catalog platform that centralizes metadata management, enabling users to efficiently discover, understand, and retrieve data through an intuitive interface. This service addresses the challenges of data governance by optimizing data collection processes and enhancing overall data performance for clients.

Superb AI offers an end-to-end training data platform that automates data preparation and curation, enabling rapid and systematic dataset creation for AI model development. This solution addresses the inefficiencies in data handling, allowing organizations to streamline their AI workflows and enhance model deployment speed.

This logistics company offers a platform that enables businesses to generate shipping quotes, create shipping labels, track shipments, and access reporting tools. By providing these features, the company helps businesses streamline their shipping processes, reducing costs and improving operational efficiency.

Minimalist is a skincare line that utilizes ingredient transparency to empower consumers in making informed choices about their beauty products. By providing clear labeling and detailed information on product formulations, Minimalist addresses the lack of clarity in the beauty industry regarding ingredient safety and efficacy.

Lazuli provides a Product Data Platform (PDP) that utilizes AI to organize and enhance product data, enabling businesses to optimize their digital sales and marketing strategies. By automating data normalization and integration, Lazuli significantly reduces manual processing time and improves the accuracy of product information, leading to increased sales and enhanced customer insights.

The startup develops AI agents that automate back-office workflows by processing unstructured data using Natural Language Processing, Machine Learning, and Large Language Models. This technology eliminates manual data entry, enabling clients to make informed business decisions more efficiently.

Provides a Python SDK that integrates with existing machine learning workflows to enable real-time debugging, diagnosis, and improvement of training data without requiring data migration. It helps identify inefficient samples, track dataset changes, and optimize model performance by linking per-sample metrics to specific dataset revisions and hyperparameter combinations.

Cornerstone AI develops a machine learning platform that automates the cleaning and preparation of real-world healthcare data by generating unique data cleaning rules tailored to each dataset. This technology addresses the inefficiencies of traditional data cleaning methods, enabling organizations to enhance data quality and accelerate analysis, ultimately improving insights from clinical datasets.

Betterdata provides a data platform that generates programmable synthetic data to replace sensitive production data, ensuring compliance with data protection laws. This approach enables faster access to realistic data for product development and testing while mitigating privacy risks associated with sharing actual data.

The startup develops on-device artificial intelligence systems that utilize large language models to convert diverse data types, including text, images, and videos, into searchable vectors. This technology enables businesses to efficiently process complex data, enhancing their analytical capabilities and competitive positioning in the market.

PrimeNumbers offers a data integration service called TROCCO® that automates the data acquisition process, enabling engineers to efficiently manage and utilize data from various sources. This solution addresses the challenge of fragmented data environments by providing a centralized platform for data orchestration, enhancing data accessibility and operational efficiency for businesses.

illumex provides a Generative Semantic Fabric that automatically maps and labels structured data, creating a unified knowledge graph that enhances data discovery and governance. This technology enables organizations to deploy generative AI analytics agents that deliver precise, context-aware responses without hallucinations, ensuring reliable insights from complex data sources.

Wyseye provides AI-driven image processing solutions tailored for industries such as insurance, automotive, and finance, enabling businesses to automate and enhance their visual data analysis. By simplifying the implementation of advanced image recognition technologies, Wyseye helps organizations improve operational efficiency and decision-making.

TRK Technology offers AI-driven solutions for financial markets, providing algorithmic trading robots for autonomous execution based on market sentiment and historical data. Their platform also includes a smart labeling tool that accelerates AI model development by reducing manual text data annotation.

Tictag offers an AI-driven data annotation platform that crowdsources the labeling of unstructured data to create high-quality training datasets for machine learning models. This approach enhances the efficiency of data collection and annotation processes, enabling businesses to leverage precise datasets for improved AI model performance and real-world applications.

This startup provides AI-enabled machine learning services, utilizing advanced annotation tools for image, text, and video data to enhance computer vision applications across various sectors, including healthcare and autonomous driving. By offering detailed analytics and digital BPO services, the company helps organizations improve operational efficiency and reduce costs in critical areas such as quality of care and revenue cycle management.

SUPA provides high-quality training data for machine learning and artificial intelligence through a proprietary platform that utilizes a crowdsourced workforce for diverse human feedback. The company addresses the challenge of obtaining accurate and culturally nuanced data for model training by delivering over one million data points weekly, tailored to specific use cases.

The startup offers an AI platform that processes both structured and unstructured data, enabling users to extract, edit, and automate data capture from various document types, layouts, and languages. This technology liberates employees from repetitive financial workflows by enhancing data extraction accuracy and efficiency.

The startup offers a machine learning-based platform for data collection and analysis that extracts insights from unstructured public and private data. By analyzing customer feedback, the system identifies trends, uncovers new opportunities, and highlights potential risks, enabling companies to make data-driven business decisions.

Navigate is a decentralized data platform that gamifies the collection and labeling of training data through its Data Quest application, allowing users to earn points for their contributions. This approach addresses the scarcity of high-quality training data for AI models by enabling individuals to monetize their data while maintaining control over their privacy.

Develops an AI-powered platform that uses generative AI and real-time video analytics to detect safety compliance issues, such as PPE usage and proximity to hazardous areas, without manual labeling. The system reduces workplace accidents and liability by providing instant alerts, customizable policy enforcement, and detailed safety performance reports.

Nucleus OS streamlines the machine learning lifecycle by providing expert data annotation and a platform for automated model validation and performance benchmarking. We help organizations enhance AI system accuracy and reliability through high-quality labeled datasets and rigorous evaluation.

AkaiSpace provides high-quality regional and diverse datasets along with annotation and labeling solutions, utilizing blockchain technology to ensure data integrity and traceability. This approach addresses the challenge of acquiring reliable training data for the development of generative AI models, enhancing their performance and applicability.

Annotation AI offers a semi-automated data labeling platform that enhances the efficiency of the AI data analysis cycle by automating the preprocessing of training data with up to 99% accuracy. This technology significantly reduces the time required for data preparation, enabling businesses to produce high-quality datasets for AI projects more rapidly.

The startup offers a data analytics service tailored for small and medium-sized businesses, utilizing tag system-based data ELT technology and modular data onboarding to streamline data integration. This enables business owners to access actionable insights that are distinct from traditional data solutions, enhancing decision-making capabilities.

Stardust AI provides a comprehensive suite of DataOps solutions, including automated data labeling and a human feedback engine, to enhance the efficiency of AI model training and deployment. The company addresses data quality and accessibility challenges, enabling organizations to optimize their AI applications across various industries.

AI developers often struggle to obtain large, high‑quality annotated datasets that are consistent across modalities and tailored to specific industry domains. Gaps in data quality, format standardization, and annotation scalability increase time‑to‑market and model performance risk. APTO delivers an end‑to‑end data pipeline that combines a SaaS annotation platform with a managed cloud‑worker workforce to collect, label, and validate data for text, images, video, audio, and 3D LiDAR.

The startup develops a cognitive data extraction and management platform that utilizes image processing and cognitive automation to enhance data accuracy and reliability. This technology streamlines processes such as invoicing and compliance, significantly increasing productivity while minimizing manual errors in enterprise operations.

The startup develops an autonomous inspection system that minimizes human error in quality inspection by utilizing online learning and elastic algorithms to adapt to process variations. This technology enables clients to leverage unlabeled data for predictive quality assurance, enhancing deployment speed and scalability while improving overall inspection accuracy.

Select Star is a data platform that specializes in building, storing, and analyzing high-quality datasets for AI applications, utilizing structured data design and fine-tuning methodologies. The platform addresses the challenge of efficiently creating large-scale, reliable datasets necessary for training AI models, significantly reducing the time and resources required for data preparation.

Beed provides an API for AI-driven data extraction from documents and images, returning structured JSON outputs tailored to user-defined fields. This technology eliminates manual data entry, streamlining processes across various industries by automating the extraction of critical information from invoices, receipts, and other documents.

The company utilizes machine learning algorithms to analyze and optimize large datasets, enabling businesses to extract actionable insights from complex customer information. This process helps clients understand their customer base and identify trends, leading to more effective marketing strategies and improved decision-making.

The startup offers a business transformation platform that integrates real-time data from multiple sources, cleans and standardizes various data formats, and generates production-ready datasets using AI and machine learning. This enables organizations to automate processes and predict outcomes, addressing the challenges of data management and complex decision-making.

The startup provides an online exporting service that facilitates cross-border trade logistics for small and medium enterprises. By offering a marketplace for local consumer products and ensuring compliance with country-specific labeling and documentation requirements, it enables hassle-free and cost-effective shipping solutions.

The startup develops a synthetic data platform that generates virtual datasets for training machine learning models in remote sensing imaging analytics. This technology enables clients to effectively monitor and analyze data by providing high-quality, labeled synthetic data that overcomes the limitations of real-world data scarcity.