Computer vision annotation
Accurate image and video labeling tailored to your project needs.
- Bounding boxes
- Object detection & tracking
- Image segmentation
- Keypoint & landmark
Kelxor's expert annotation teams label images, video, audio and text with multi-level quality checks, so you can scale your AI projects with confidence.
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Accurate labeling across images, video, audio and text, tailored to your project and the model you're training.
Accurate image and video labeling tailored to your project needs.
Detailed transcription and labeling for stronger speech recognition.
Customized text labeling that preserves clarity and context for NLP applications.
Multi-level checks, scalable teams and strict data protection, so datasets stay precise as your project grows.
Securely received under NDA.
Trained annotators label to your guidelines.
Layered quality checks catch errors early.
Verified dataset delivered securely.
Multi-level quality checks for precise datasets.
Hundreds of trained annotators ready for any project size, with flexible solutions that expand with your needs.
NDA-compliant and secure handling of sensitive data.
A clear, streamlined approach to delivering precise data annotation.
Share your AI project requirements so we can tailor our annotation services to your specific goals and data types.
Our expert teams perform accurate computer vision, text and audio annotation to build high-quality datasets efficiently.
Receive your annotated datasets securely, with ongoing support to optimize model training and scalability.
Self-driving systems learn from labeled road data: vehicles, pedestrians, signs and lanes, frame after frame.
Our computer vision teams handle bounding boxes, object detection and tracking, and segmentation for this work. The workspace above shows the idea in miniature.
Your datasets are your competitive edge. We protect them with strict protocols so you can build with confidence.
We are growing our annotation teams. If you are detail-oriented and want to work on real AI projects, we would like to hear from you.
Send a short introduction and the kind of work you are interested in.
Share your data type, volume and what your model needs to learn. We will come back with how we would approach it.