Top 10 Global Data Labelling &#038; Annotation Companies
Amid the burgeoning generative AI wave, data labelling and annotation have become one of the most important steps in the development of these large language models. This process involves assigning appropriate labels to raw data, such as images, text, or audio files, to clearly define the content. The performance of a model is directly influenced by the accuracy and quality of these labels. A higher quality of labelled data leads to improved model performance, hence well-labelled data is essential to enable the AI system to interpret and learn from the data accurately, ensuring reliable and precise predictions. The significance of accurate data labelling and annotation becomes evident as AI models may face challenges in understanding the context and may make errors without it. Now, let’s take a closer look at the top 10 companies in this domain. ScaleAI Alexander Wang’s ScaleAI offers a comprehensive suite of solutions to enhance AI model development. It specialises in efficient AI data labelling and annotation services, addressing challenges in the development of AI models. The Scale Rapid platform facilitates quick project setup, generating high-quality labels promptly, supporting organisations to scale data labelling without compromising quality. The platform includes a diverse labelling workforce, ensuring accurate and efficient results. It provides infrastructure for various data labelling use cases, offering customised solutions, tailored workflows, and quality assurance processes. AI-powered tools in Scale Studio automate labelling tasks, reducing time and effort. Google reportedly signed a contract with Scale AI, the $7.3 billion startup focused on training and validating AI software, to test the tools. Dataloop AI Israel-headquartered Dataloop AI specialises in creating data infrastructure and operating systems for AI companies. Its main product is a data management and annotation platform, helping AI teams in data visualisation, collaboration, and exploration. This platform consists of data management, an intuitive annotation tool with automatic capabilities, and tools for data quality assurance and debugging. Karya AI Bengaluru-based Karya AI facilitates data labelling and annotation in various Indian languages for AI models by engaging population from rural India. These workers gather and label non-English data in various regional languages to improve the quality of Indian-language data for AI and ML applications. The company’s ethical practices include compensating low income communities at a rate 20 times higher than the minimum wage. The startup’s approach has gained recognition and support from major tech players such as Google and Microsoft. Microsoft uses Karya for local speech data sourcing while Google, in partnership with Karya and others, engages in speech data collection across 85 Indian districts. Through its app, Karya AI involves users in tasks like recording audio in native languages, and contributing to AI data enrichment. The company is expanding its reach to various Indian languages, diversifying AI databases with linguistic data. Appen Appen excels in data collection, preparation, and annotation, handling diverse data types like text, speech, images, and videos. Its services are vital across industries, helping in the development of technologies like speech recognition and autonomous vehicles. With a vast, diverse global workforce, the Australia-based company leverages crowdsourcing to bring varied linguistic and cultural insights to projects. It integrates advanced tools with human intelligence, improving the accuracy and efficiency of its data services. Google, Microsoft, NVIDIA, Amazon, Tesla, Ford, General Motors, Waymo, Pfizer, Roche, Walmart, Alibaba, US Department of Defense, European Commission, etc. are some of the premium customers of Appen. Labelbox Entering the space in 2018, Labelbox has swiftly become a known entity in the space of data labelling. The company focuses on enhancing AI training efficiency through its platform. The collaborative training data platform offers diverse annotation tools for image, video, text, and geospatial data, featuring AI-assisted labelling to expedite the process. Quality control measures, like consensus-based review and benchmark tasks ensure high-quality annotations. Labelbox serves a diverse clientele, including automotive, agriculture, healthcare, retail, e-commerce, government, and defence sector. The company’s proficiency in handling large and intricate datasets with precision has established it as a preferred choice for those leveraging AI and ML in their daily workflow. Walmart, Burberry, Sharper Shape, Ancestry, and Intuitive are some of its customers. NextWealth Founded by Dr Sridhar Mitta, Anand Talwai, and Mythily Ramesh, NextWealth is a Bangalore-based company offering AI and ML data services, digital customer experience, and IT solutions to a global market. Their services range from data enrichment and back-office tasks to advanced AI-driven operations. NextWealth partners with local entrepreneurs in smaller towns to establish delivery centres, partially owned by the company, to ensure high-quality, standardised services while also fostering job creation, especially for women, in these areas. Employing innovative AI and ML technologies, NextWealth has carved out a niche in sectors like e-commerce, fintech, and healthcare, with significant partnerships like that with Jumio for identity verification solutions. Sama AI Sama AI of San Francisco focuses on providing data labelling services for computer vision, leveraging ML. Catering to sectors like retail, agriculture, and manufacturing, it aims to enhance AI development through ethical, scalable data solutions. Founded by the late Leila Janah, Sama AI specialises in manually labelling and annotating data, involving tasks such as identifying objects in images, transcribing text data, and classifying different data types. The company also offers customised workflow solutions tailored to specific project needs iMerit Founded in 2012 by Radha Basu, iMerit is a tech service company specialising in handling various data forms like text, images, and audio, providing precise annotation and labelling, essential for sectors like healthcare, automotive, and agriculture. Their custom solutions are tailored to meet diverse client needs, often involving complex annotation tasks. iMerit’s business model is socially conscious, emphasising hiring from underprivileged communities, contributing to their economic advancement. This approach benefits their data quality making it diverse and contributes to social good. It has expanded globally, catering to a wide range of industries beyond AI and ML, like e-commerce and geospatial technology. It integrates ML algorithms for semi-automated annotation and develops custom software solutions. Its cloud-based platforms enable efficient data processing and storage, while advanced analytics help monitor and improve service quality. SuperAnnotate San Francisco-based SuperAnnotate assists computer vision teams in annotating and managing image data for ML. It offers tools for annotating images and videos, essential for training AI models in various sectors such as autonomous vehicles, retail, agriculture, and healthcare. Founded by Tigran Petrosyan and Vahan Petrosyan, SuperAnnotate’s technology features AI-assisted annotation, facilitating faster and more accurate labelling, and integrates with other ML platforms and data storage services. Kili Founded in 2018, Kili focuses on creating a data labelling platform for ML applications in computer vision and neuro-linguistic programming. With additional offices in New York and Singapore, the Paris-based company caters to businesses aiming to develop reliable AI. Its major clients are L’Oreal, Renault, and Airbus in enhancing technologies ranging from facial recognition to autonomous driving and predictive maintenance. The company has secured over $30 million in funding, showing promising growth with projected revenues. Kili’s product suite includes tools for image, video, text, OCR, and geospatial annotation, and data labelling.
The performance of a generative AI model is directly influenced by the accuracy and quality of the data labels.
The performance of a generative AI model is directly influenced by the accuracy and quality of the data labels.  
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["Top Trend", "Go", "AI", "ML", "Scala", "computer vision", "RAG", "Aim", "analytics", "generative AI", "R"]
["AI", "ML", "computer vision", "analytics", "generative AI", "Aim", "RAG", "R", "Go", "Scala"]
Shritama (she/her) is a technology journalist at AIM who is passionate to explore generative AI with a special focus on big techs, database, healthcare, DE&amp;I, hiring in tech and more.