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Richmond, United States
1-10 Employees
2006
Xelex provides training data to technology companies for use in improving the accuracy of their artificial intelligence applications. With Xelex, we've applied those same core competencies to automate and simplify data annotation workflow management at scale. Plus, the Xelex platform gives all stakeholders in the company the kind of access to projects we’ve not had before“. Xelex AI excels at classifying hallucination types for model correction, including nuanced tasks like identifying partially correct statements, and identifying the source material used in misinterpretations. Both equip Xelex AI to deliver highly accurate and dependable data classification services across a wide range of domains. Xelex AI has curated thousands of hours of exam room conversations and are experts at data curation projects that help improve healthcare large language model accuracy. Xelex AI excels at voicing and collecting high-fidelity audio source material for large language model creation and refinement, including synthetic office note dictation and exam room conversations. Making domain experts more efficient by simplifying complex workflow tasks so that non-technical team members can play a larger role in project management.
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A data labeling platform built for efficiency & ease of use
... Xelex data collection, text labeling and voice data annotation platform is secure, efficient, and easy to ...
Quezon City, Philippines
51-100 Employees
2010
Flipside is a pioneer and leader in labeling for computer vision and perception, particularly for autonomous vehicles (AV), advanced driver assistance systems (ADAS) and robotics. Flipside Digital Content was founded in 2010 and operates as Philippines-based BPO, now specialized in providing outsourced AI data labeling services as Flipside AI after 8 years as an ebook production house. A true partner to both our customers and labelers, we operate an outsourced business process outsourcing (BPO) service to ensure high-accuracy, secure and ethical data labeling. As labeling domain experts, we specialize in complex 3D, LiDAR, RADAR sensor fusion and even SAR data for leading- edge ML models.
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data labeling for computer vision
... data labeling for computer ...
Dallas, United States
11-50 Employees
2021
We are already engaging in our 4th project with them. Our in-house data labeling service by experts provide cost efficient annotation services to generate high quality training datasets.
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Video Data Labeling & Video Transcription Services
... Trusted Video Data Labeling and Annotation Services | ...
Hingham, United States
1-10 Employees
2017
Some slowed their work pace, others had to stop their production, and a few of them introduced new alternatives to continue their duties respecting the new safety rules.
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Data Labeling, Training Data for AI & ML - Edgecase
... Data Labeling, Training Data for AI & ML - ...
San Juan, United States
51-100 Employees
2003
How AI models solve predicting churn among advantage members. Creating a new digital channel to improve millions of patients health. Solving the world's complex billing problems in a single, seamless solution. Building an architecture that improves 15 Million subscribers experience. Enabling a seamless AI model that facilitates ulcer treatment decision makers.
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Data Labeling Services
... Since data privacy is a real concern, Wovenware has a private crowd of in-house data specialists who are experts at labeling objects or data. ...
Mannarkkad, India
501-1000 Employees
2016
INFOLKS is a fast-growing and No.1 data labeling company in India. We are capable of labeling data irrespective of its volume to be handled. Among them, data labeling possesses paramount importance and we provide these services at the finest quality. With experience over 7 years, our workforce possess immense expertise in various fields of image annotation. Time promptness is a guaranteed trait of our services. We do free pilot works prior to our services for better understanding at minimal risks. This is a key reason behind our success. Procedures from project onboarding to final submissions are readily customizable to the client requirements.
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Ultimate Data Labeling Platform
... Data Labeling Service Provider For AI/ML | ...
Đà Nẵng, Vietnam
51-100 Employees
2015
A complete set of solutions for multiple-input annotation and service with the on-demand offer. Voice annotation: Provide a recorded speech data, a platform to annotate audio files. This is a comprehensible labeling tool with a flexible pipeline that helps you save a lot of time spent on your data while still retaining high accuracy using the power of Al. We also provide highly professional human workers as part of the flexible pipeline described above to help your data reach over 99% accuracy. With machine learning data, livestock and poultry are now more comprehensively cared for and provided with early intervention when there is a health problem more promptly.
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VBPO Data Annotation - The comprehensive solutions for superb AI training
... Data Labeling ...
Mannarkkad, India
51-100 Employees
2020
Labelops is now considered as the No.1 Data Labelling company in the world. We provide the data annotation service at the lowest rates in the global market ! We provide a 24x7 outstanding customer care support , which enable our clients to have an easy and fast communication with us despite the time zone differences. We are capable of preparing training data sets despite of it's volume , we have immense experience in handling very large datasets. We provide free live demos before starting a project to understand the service at minimal risk. A dedicated LabelOps Manager prepares the timeline and set up the project team by selecting expert annotators who have worked on similar projects before. Over these years of experience we have come across wide range of datasets of various usecases. Premium quality serivce for the lowest hourly rates is the trait which made us outstanding.
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Audio Annotation and Data Labeling Services - LabelOps
... Audio Annotation and Data Labeling Services - ...
Paris, France
11-50 Employees
2016
Because it gives access to information, training and work, it is a real lever for everyone to gain independence. We have accompanied more than 2,300 women in 39 countries, we have provided over 26,000 hours of training, we are the first European AI company certified BCorp. Do you want to contribute to the project of an impactful, innovative and fast-growing startup? They are also supported by Isahit in the realisation of their short-term life projects. Outsource smartly your repetitive and manual tasks to Isahit. Improve the user experience of your customers thanks to Isahit's solutions. We help Ecommerce Teams with their back-office activities such as product categorisation, product sheet enrichment, key attributes definition or product reviews moderation. Maria's project is to develop and launch a mobile app to offer free yoga classes to users all over the world.
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The ultimate guide of data labeling services - 2023
... Ultimate guide of data labeling annotations ...
Technologies which have been searched by others and may be interesting for you:
Some interesting numbers and facts about the results you have just received for Data Labeling
| Country with most fitting companies | United States |
| Amount of fitting manufacturers | 130 |
| Amount of suitable service providers | 125 |
| Average amount of employees | 51-100 |
| Oldest suiting company | 2003 |
| Youngest suiting company | 2021 |
Data labeling is the process of identifying raw data (like images, texts, videos) and adding one or more meaningful and informative labels to provide context so that a machine learning model can learn from it. These labels can range from simple categorizations to more complex annotations, such as identifying objects within images, annotating parts of speech in texts, or providing descriptions for audio clips. The purpose of data labeling is to train machine learning algorithms to understand patterns and make decisions based on the labeled data, essentially teaching the algorithm to recognize what each piece of data represents. This process is critical in developing accurate and efficient AI systems across various applications, from autonomous vehicles recognizing stop signs to voice assistants understanding natural language queries. The quality and accuracy of data labeling directly impact the performance of AI models, making it a vital step in the machine learning pipeline. As AI technologies continue to evolve, the demand for high-quality labeled data has surged, highlighting the significance of data labeling in advancing machine learning capabilities. Through this meticulous process, data scientists and AI developers can create models that not only mimic human intelligence but also enhance decision-making processes across industries, leading to innovations that could transform the way we interact with the digital world.
1. Enhanced Accuracy
Data labeling, a critical process in machine learning, significantly boosts the accuracy of model predictions. By meticulously tagging or categorizing raw data, models can better understand and interpret new, unseen information, leading to more precise outcomes compared to unsupervised learning methods.
2. Improved Model Performance
Through the provision of high-quality, annotated datasets, data labeling ensures that AI systems are trained on relevant and correctly identified information. This targeted approach enhances model performance, as algorithms can easily recognize patterns and make informed decisions, setting a higher benchmark than methods relying on unstructured data.
3. Customization and Flexibility
Data labeling offers unparalleled customization, allowing for the tailoring of datasets to meet specific project requirements. This flexibility ensures that AI models are not only accurate but also highly adaptable to varying tasks, a significant advantage over one-size-fits-all solutions.
4. Accelerated Development Time
By streamlining the training phase of AI development, data labeling significantly reduces the time needed to bring models to operational status. This efficiency enables quicker deployment and iteration of AI solutions, providing a competitive edge in fast-paced technological landscapes.
While evaluating the different suppliers make sure to check the following criteria:
1. Accuracy and Quality Control
Ensure the supplier has robust mechanisms for maintaining high accuracy levels in data labeling, including multiple layers of quality checks.
2. Scalability and Flexibility
The supplier should be capable of scaling operations up or down based on your project needs and be flexible in adapting to your specific requirements.
3. Technology and Tools
Assess the technology stack and tools the supplier uses for data labeling. Advanced tools can significantly enhance efficiency and accuracy.
4. Domain Expertise
The supplier should have experience and expertise in your specific industry or domain, ensuring they understand the nuances and complexities of your data.
5. Security and Confidentiality
Confirm the supplier has stringent data security and confidentiality protocols to protect sensitive information.
6. Turnaround Time
Evaluate the supplier's ability to deliver high-quality labeled data within your project timelines.
7. Cost-Effectiveness
While not compromising on quality, consider the cost-effectiveness of the supplier’s services to ensure they align with your budget constraints.
In the healthcare industry, data labeling plays a crucial role in training machine learning models to diagnose diseases from medical images, such as X-rays and MRIs. By accurately labeling these images with the correct diagnosis, AI systems can learn to identify patterns and anomalies, assisting doctors in making faster and more accurate decisions. The automotive sector leverages data labeling for the development of autonomous vehicles. Through the precise labeling of road images and sensor data, including traffic lights, pedestrians, and other vehicles, machine learning models are trained to navigate roads safely. This process is essential for enhancing the intelligence and reliability of self-driving cars. In the realm of finance, data labeling is used to detect fraudulent activities. By labeling transactions as fraudulent or legitimate, AI models can learn to identify suspicious patterns, helping banks and financial institutions to prevent fraud and secure their customers' assets. Retail companies utilize data labeling for improving customer experiences through personalized recommendations. By labeling product images and customer interaction data, machine learning algorithms can offer tailored product suggestions, optimizing marketing strategies and enhancing customer satisfaction.
Data labeling, a critical process in the development of machine learning models, is situated at a high Technology Readiness Level (TRL), specifically between levels 8 and 9. This advanced placement is attributed to the extensive deployment and validation of data labeling techniques across real-world applications, ranging from autonomous vehicles to personalized medicine. The process has evolved from manual annotation by humans to semi-automated and fully automated methods, leveraging sophisticated algorithms and artificial intelligence to enhance efficiency and accuracy. The integration of machine learning for auto-labeling, where the system itself generates labels on new data based on previously learned information, showcases the maturity of this technology. Furthermore, the adoption of active learning strategies, where the model identifies data points that would be most beneficial for it to learn from, illustrates the advanced capabilities and optimization in data labeling processes. The continuous refinement and application in diverse fields affirm its high TRL, underscoring the reliability, efficiency, and scalability of current data labeling methodologies. These technical advancements have significantly reduced the time and cost associated with preparing data for machine learning, thereby accelerating the pace of innovation and application of AI technologies across various sectors.
In the Short-Term, advancements in data labeling are poised to leverage increased automation and AI-driven tools. This phase sees the development of more sophisticated algorithms that can automatically label vast datasets with high accuracy, reducing manual efforts and time consumption. The integration of machine learning models for semi-supervised learning will enable systems to learn from a smaller set of labeled data, making the process more efficient and cost-effective. Moving into the Mid-Term phase, we anticipate the emergence of collaborative data labeling platforms that harness the power of the crowd. These platforms will facilitate the sharing of labeled datasets among researchers and organizations, fostering a collaborative environment that accelerates the labeling process. Enhanced privacy-preserving techniques will be developed to ensure that data sharing does not compromise sensitive information, thereby encouraging wider participation across industries. In the Long-Term, the focus will shift towards fully automated, intelligent data labeling systems. These systems will be capable of understanding context and nuances within data, allowing for accurate labeling without human intervention. The integration of natural language processing and computer vision technologies will enable these systems to label complex and diverse datasets, from text to images and videos, pushing the boundaries of what automated data labeling can achieve. This era will mark a significant milestone in making data labeling a seamless, highly efficient process.
