DataFactor builds the preference data layer for modern AI. Most large language models are trained on static or synthetic data with limited real-world feedback and a weak understanding of what users actually prefer between one models output and anothers. DataFactor solves this problem by capturing how real users choose between frontier model outputs (ChatGPT, Claude, Gemini, Grok, etc.) and turning those interactions into high-signal preference datasets for training and evaluating LLMs. Across our product ecosystem, users compare multiple AI models side-by-side on real prompts. We capture both implicit signals (dwell time, selections, switching behaviors, response selection) and explicit votes on which model answered best. Every record is schema-validated and structured for RLHF, RLAIF, DPO, and end-to-end evaluation pipelines, with SOC 2 and GDPR compliance across 12 regions. We’ve generated 10M+ interaction events across 47 models to date. Teams use our data to lift RLHF and DPO win rates against frontier competitors, catch eval-to-production gaps before shipping, and fine-tune for specific domains or user populations instead of generic helpfulness. Its preference signal at a scale most teams cant generate internally. And it’s sourced from real user choice rather than annotator approximations of it. We work with frontier AI labs, enterprise AI teams, research orgs, and alignment teams building the next generation of models.
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Frequently asked questions
When was DataFactor founded?
DataFactor was founded in 2025.
What industry is DataFactor in?
DataFactor operates in Technology, Information and Internet. Find similar Technology, Information and Internet investors on gritt.io.
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