Text annotation forms the backbone of large language models and natural language processing applications. Common annotation tasks include named entity recognition (NER), where entities such as people, organizations, or financial transactions are tagged within documents; sentiment labeling, which categorizes customer or employee feedback as positive, negative, or neutral; and prompt/response annotation, which provides structured data for reinforcement learning with human feedback (RLHF) in generative AI models. Enterprises use these annotations to power AI applications ranging from chatbots to regulatory compliance systems, ensuring models are trained on text that is both contextually accurate and linguistically diverse.

Computer vision models depend on large volumes of annotated images. Annotation can take the form of bounding boxes, polygons, or pixel-level segmentation. In enterprise contexts, this enables retail organizations to train models for shelf monitoring, ensuring inventory is tracked in real time; manufacturers use image labeling to detect product defects during quality assurance; and AV developers rely on millions of annotated pedestrian and vehicle images to train perception models. Without accurate image labeling, these AI models risk misclassification that can damage brand trust or even create safety risks.

Video annotation requires labeling sequences of frames, often at millisecond intervals. This is critical for AI systems that depend on temporal context. Warehouse robotics, for example, depend on annotated video to navigate efficiently and safely. Security monitoring systems rely on video annotation to detect threats or anomalies in real time. Sports organizations use video labeling for analytics, tagging player movements frame by frame. The complexity and volume of video data make accurate annotation particularly challenging, requiring workflow orchestration platforms to ensure both speed and precision.

LiDAR data annotation is at the heart of autonomous driving and robotics. LiDAR sensors generate massive 3D point clouds that must be segmented and labeled with precision. This involves classifying pedestrians, vehicles, and obstacles in three-dimensional space. Beyond AV, LiDAR annotation is critical for robotics navigation, drone-based mapping, and AR/VR spatial modeling. Unlike 2D images, LiDAR data introduces depth, making annotation significantly more complex. Only a combination of automation + human-in-the-loop (HITL) can deliver the accuracy enterprises require for safety-critical applications.