DataFactor provides a data layer for frontier AI by operating a continuous pipeline that captures real‑world interactions and converts them into training‑ready signals. Their infrastructure gathers multimodal sensor streams—video, depth, force, tactile, and proprioception—from owned properties and teleoperated robots, while a global community of experts supplies human preference, safety, and bias judgments to enrich the dataset for post‑training and evaluation loops.
Funding
Funding not disclosed
Founders
Product
Problem
Frontier AI models require large volumes of high-quality, real-world human interaction data to improve performance, safety, and alignment, but existing datasets are often synthetic, scraped, or lack structured feedback signals.
Solution
DataFactor provides a dedicated data layer that captures live interactions on properties it owns and operates, converting each encounter into structured, training-ready signals such as preferences, safety judgments, and multimodal sensor streams. The platform continuously aggregates these signals into a living dataset that can be directly integrated into post‑training and evaluation pipelines for both digital and embodied AI systems. By leveraging a global network of domain experts and everyday users, DataFactor ensures the data reflects authentic human behavior and expert reasoning across regulated domains. The infrastructure supports synchronized video, depth, force, tactile, and proprioception data for robot policy training, as well as human feedback on model outputs for alignment and bias mitigation.
Target Audience
Primary customers are frontier AI labs and research teams developing large language models, multimodal systems, and embodied agents that require high-quality human feedback and sensor data for alignment and performance improvement.
Features
- Real-world data capture from owned properties, guaranteeing authentic, non‑synthetic interactions
- Automatic structuring of signals into preferences, comparisons, safety and bias judgments, and multimodal sensor streams
- Continuous pipeline that expands a living dataset with over 10 million interactions per month
- Support for both digital AI (text, image, audio) and embodied AI through synchronized video, depth, force, tactile, and proprioception data
- Expert‑validated feedback loops enabling human evaluation of model outputs and robot trajectories for safety and intent
- Scalable integration points for post‑training fine‑tuning and evaluation workflows