About Hive
Hive is building a contributor-powered data supply layer for AI teams that need rights-cleared, real-world multimodal training data.
Why this exists
AI models increasingly need data that is specific, human, contextual, and difficult to obtain from public internet sources alone. Many teams need data that is not already available online, such as natural conversations, first-person video, product interactions, screen recordings, local environments, and language-specific recordings. Hive exists to help those teams source that data with consent, review, and documentation they can stand behind.
What Hive does
Hive helps AI teams request custom datasets and coordinates the contributor workflow needed to collect them. Contributors provide original data, submissions are reviewed by people, and approved data is organized with consent and quality records suitable for downstream evaluation and licensing discussions.
Our principles
Consent first
Data should be collected from people who understand what they are contributing and under clear rules.
No scraped dataset claims
We do not present scraped or repurposed web content as contributor-collected, rights-cleared training data.
Quality before scale
A smaller reviewed dataset is more useful than a large unreliable one.
Honest operations
If something is manual, we treat it as manual. We do not claim automation that is not in production.
Clear contributor expectations
Contributors should understand task requirements, review rules, and when payment applies.
For buyers
Buyers can request datasets and share what they need. Hive will review the request and reply with next steps.
For contributors
Contributors can create a profile so they can be considered for future paid data collection tasks.