Founded on the belief that great AI starts with great data, DAIT was established to close the gap between raw data and real-world intelligence. From text, audio, image, and video annotation to data sourcing, quality assurance, and AI testing, we provide end-to-end support for organizations that rely on clean, structured, and ethically annotated data.
To empower innovation through accurate, inclusive, and secure data annotation, enabling AI solutions that truly reflect and serve the diversity of the world.
We deliver only the highest quality data, refined by experts and verified by multi-step QA.
We protect your data like it's our own with full compliance, privacy, and transparency.
Our strength lies in our people. We’ve built a team of highly skilled annotators, project managers, and quality control experts who work together with one goal: to deliver accurate, timely, and high-quality data for AI.
We offers integration with pretrained models that help automatically detect and annotate objects, significantly reducing manual effort.
Datumaro is a powerful toolkit that works seamlessly with CVAT to manage annotated datasets. It allows you to validate annotations, filter out errors and convert between formats like COCO, YOLO, VOC,
SuperAnnotate can integrate with CVAT workflows to provide a cleaner UI, strong QC features, task assignment, and review workflows. It's ideal for enterprises handling high-volume annotation tasks with distributed teams.
We started as a small team passionate about AI and data science. Our focus was simple: help machines learn better through accurate and ethical annotation.
We added audio and video annotation to meet the rising demand from voice assistant, surveillance, and autonomous vehicle clients.
We adopted CVAT and other advanced tools, streamlined our internal quality-control processes, and introduced human-in-the-loop systems.
We scaled up to serve clients across Europe, North America, Asia, and Africa, adapting to varied data policies and project demands.
We refined our data sourcing approach to ensure fairness and diversity in training datasets, and aligned with global data protection standards (GDPR, NDPR, HIPAA, etc.).
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