Danbi
Danbi provides on-premise video indexing software that turns every frame of a content owner's archive into searchable structured data, including faces, speech, actions, objects, and timecodes. Users can search thousands of hours of footage in plain English, and the archive can be exposed to AI agents via the MCP standard with monetization through the x402 payment protocol. The system is built for organizations whose archives are too large, too mixed, or too private to upload to cloud services.
- Artificial Intelligence
- Software Only
- Creator / Creative Economy
Funding
Founders
Founder details are not available yet.
Product
Problem
Content owners with large archives of video—newsrooms, podcast networks, film producers, and independent creators—cannot efficiently search their own material, forcing editors to manually scrub hours of footage for a single clip. Valuable libraries sit unused for licensing and production, and the archives are not connected to the growing ecosystem of AI agents that consume content on behalf of users.
Solution
Danbi runs on the customer's own infrastructure and indexes every frame of a video into searchable structured data—including faces, spoken words, actions, objects, and exact timecodes. The product supports natural-language search across thousands of hours of footage, allowing users to instantly locate any moment. Danbi exposes the indexed archive through MCP, the open standard for AI tool connectivity, so any compliant AI agent can query the archive. Monetization is enabled through x402, a protocol that lets AI agents pay for content access. Only fragments that benefit from a third-party model are sent externally; the full indexed result stays entirely on the customer's hardware.
Target Audience
Primary customers are content owners whose archives are too large, too mixed, or too private to upload to cloud services, including newsrooms and television channels, podcast and radio networks, documentary and film producers, and YouTubers and independent creators.
Features
- Full on-premise deployment with local processing and indexing, ensuring archives never leave the owner's infrastructure
- Frame-level indexing of faces, speech, actions, objects, and timecodes into structured data
- Natural-language search across massive video libraries to instantly locate any moment
- MCP-compliant API that allows any AI agent to query the archive directly
- Integration with x402 payment protocol to let automated agents pay for content access
- Optional use of external AI models only for fragments that benefit from them, with default local processing