DATA HEDGE lets users voluntarily archive their LLM usage history, protect sensitive information, and earn points for verified AI data contributions.
Total Points
18,420
Verified
27
Quality
92
Weekly contributions
+18% vs last
Privacy cleaning
Sensitive data detected
Upload. Clean. Earn.
Three simple steps to turn your AI usage history into a privacy-preserved data asset.
Drop your ChatGPT, Claude, Grok, or Gemini export. We accept text, JSON, CSV, HTML, and ZIP.
Names, emails, phone numbers, financial and health details are detected and removed before structuring.
Verified, high-quality data earns reward points. Track your tier as you contribute over time.
Why It Matters
Real human–AI interaction data is becoming foundational for the next wave of AI models, agents, and workflow intelligence.
Authentic prompt and response patterns are increasingly valuable for training next-gen models, agents, and workflow intelligence.
How people chain tools and refine prompts reveals real productivity behavior — data no synthetic dataset can replicate.
Coding, marketing, research, and productivity tasks each shape model behavior. Categorized data yields better fine-tuning.
DATA HEDGE is a user-consent-based AI data contribution network that turns LLM usage history into privacy-preserved AI data assets.
Privacy First
Privacy is not a feature here — it is the foundation of the contribution model.
Your raw inputs never leave a privacy-cleaning pipeline that strips identifying details before any data leaves your account.
Detection covers names, emails, phone numbers, financial figures, health information, and company-confidential phrases.
View, export, or delete any uploaded dataset at any time. Revoke specific consent scopes with a single toggle.
Reward points do not guarantee financial value. Future token-related benefits may be introduced only after policy and regulatory review.
Data Types
Structural patterns of how users phrase requests across tasks, domains, and tools.
Multi-turn flows showing how people refine output, switch context, and orchestrate tools.
Which responses users keep, edit, or discard — a signal for preference modeling.
Coding, marketing, research, productivity, creative, and education task tags.
Long-form, real-world conversational behavior across major LLM platforms.
Join the AI Usage Data Layer
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