Human data for frontier AI

Building agents that must execute, not just respond

Expert-designed data infrastructure that makes agents reliable at scale - built on golden trajectory creation, failure mode analysis, enterprise RAG evaluation, and full RL environments covering agentic task, verifier and reward design across coding, DevOps, ITSM, finance, and HR.

Made to improve real-world model performance

Domain-specific human intelligence that moves models from text generators to true reasoning agents - spanning multi-step CoT reasoning, bias detection, cultural mitigation, SME-led RLHF, LLM-as-a-Judge, LLM retrieval and search functionality, and more.

High-complexity multimodal data annotation and evaluation

Production-ready data annotation and evaluation built for multimodal language models and vision-language models - from text, image, audio, and video data labeling to SFT demonstrations, adversarial red teaming, rubric-based LLM output evaluation at scale.

Custom, responsible data collection for frontier models

Custom datasets tailored to the exact conditions your model will operate in - through egocentric video, robotic sensor capture, in-cabin automotive, wearable devices, speech, image, and conversational audio across 10+ global markets, with moderated and unmoderated programs built in.

Bespoke speech and audio data for every voice AI use case

Focusing on the natural interaction of frontier-training models to listen and speak like humans. From expressive TTS synthesis to real-time dialogue covering audio-to-text transcription, conversational AI training data, acoustic scene understanding, and dialectal speech across 500+ languages and locales.

Egocentric video, robotics, and embodied AI training data

Spatially precise Physical AI data layer for embodied, autonomous, and physically grounded systems - from LiDAR annotation to large-scale egocentric video datasets for world model collection.