Multimodal AI Training Data
The eyes and ears for robotics, autonomous systems, and vision-language models. We produce the spatially precise, temporally rich datasets that enable AI to perceive and act in the physical world.
Data Capabilities
Two specialised services for teams training multimodal language models and vision-language models.
Audio-Visual Language Sync
Temporally aligned transcription, lip-sync annotation, and semantic coherence labeling across paired audio and video streams. The data layer for vision-language models that must understand not just what is said, but what is seen simultaneously.
Video Action & Intent Recognition
Frame-level and clip-level annotation of human actions, object interactions, and inferred intent across surveillance, sports, robotics, and consumer video. Supports temporal models that must interpret what is happening and what is about to happen.
LiDAR & Point Cloud Annotation
Precise 3D bounding boxes, instance segmentation, and multi-frame object tracking across LiDAR, radar, and depth sensor data. Built for autonomous vehicle pipelines and robotics systems that must understand distance, structure, and spatial relationships in the physical world.
Visual Scene Understanding
Pixel-level segmentation, bounding box annotation, attribute tagging, and spatial relationship labeling across diverse real-world image datasets. The foundational layer for vision models that need to recognize, locate, and reason about objects in complex, unstructured scenes.
Speech & Acoustic Event Labeling
Transcription, speaker diarization, acoustic event tagging, and tone/emotion annotation across multilingual and domain-specific audio. Powers speech recognition, voice assistants, and audio-language models that require richly structured sound data at scale.
Human Pose & Action Sequencing
Skeletal tracking, body pose estimation, and fine-grained action sequence labeling across motion capture and video data. Builds the physical priors that robotic manipulation and human-computer interaction models depend on.
Case Studies
How leading AI organisations trust Appen for multimodal & physical ai data.
How Nearmap Scaled AI Data Labeling for Aerial Imagery
Computer vision annotation pipeline for high-volume aerial and 3D imagery, enabling precision geospatial AI models.
Training an LLM Image Generator for Graphic Design in 20+ Languages
Multimodal dataset creation enabling image generation that is contextually accurate across language and cultural dimensions.
How Onfido Optimized AI Fraud Detection
Identity document and facial recognition data to power robust, bias-mitigated fraud detection at global scale.
Ready to build with confidence?
Talk to our team about multimodal AI training data, from vision-language model alignment to audio-visual synchronisation at scale.