Train your software with precise and powerful  training data for AI.

Bid farewell to the hassle of juggling multiple annotation platforms. At TagX, we offer a comprehensive, one-stop solution for all your data annotation needs. Our end-to-end services encompass creating tailored annotation guidelines, curating the most suitable foundational models, and seamlessly integrating labeling workflows – all under one roof.

Consolidate resources, eliminate platform redundancies, and leverage our efficient processes, TagX empowers you to realize substantial cost savings. Moreover, our optimized approach significantly reduces project timelines, enabling you to swiftly operationalize your data-driven initiatives and gain a competitive edge.

TagX offers comprehensive data annotation services for computer vision projects, including image and video annotation for a variety of use cases. Our team of skilled annotators can accurately label and tag visual data to help train and develop computer vision models.

Our annotation services ensure the highest level of precision and consistency for the data labeling process, making sure that the labeled data is of the highest quality. With our services, you can be confident that the data used for training your computer vision models are of the highest quality, which in turn will lead to more accurate and effective models.

Natural Language Processing (NLP) deals with the interaction between human language and computers. TagX provides data annotation for text and audio which is crucial for NLP as it provides the training data for the models. Text annotation involves labeling and tagging text data, such as identifying entities, sentiment, and intent. Audio annotation, on the other hand, involves labeling and tagging audio data, such as transcribing speech and identifying speakers.

Labeled data by TagX is used to train models for different NLP use cases such as machine translation, sentiment analysis, speech recognition, and language understanding. For example, in sentiment analysis, text data is labeled with the positive, negative, or neutral sentiment, and this labeled data is used to train a model to classify text as positive, negative, or neutral. Similarly, in speech recognition, audio data is labeled with transcriptions of the speech, and this labeled data is used to train a model to transcribe audio data.