Your editors scrub through hours of material to pull a single clip. The library — worth millions in licensing — sits unused, because nobody can find anything in it.
More than half of web traffic is automated, most searches end without a click, and ad revenue is collapsing. The same AI agents reading, watching, and listening on someone else's behalf are starting to pay for the content they consume. But your archive isn't connected to it.
Danbi runs on your own hardware and turns every frame into searchable structured data — faces, words spoken, actions, objects, timecodes.
Your team types what they need. Danbi returns the exact moments — playable, citable, exportable. No SQL, no taxonomy work, no MAM tagging.
Expose the archive through MCP — the open standard for AI tools. Any compliant AI agent can query it and pay for access through x402.
Processed on your own infrastructure by default. Only fragments that benefit from a third-party model are sent out. The indexed result always stays where you control it.
One-time cost to process and index your archive — across the three options most teams compare: a multimodal API (GPT-4o, Gemini), a traditional MAM ingestion pipeline, or Danbi on your own hardware.
One-time cost to process and index. MAM rate is an industry estimate; refine with your vendor's contract.
Danbi started as an internal project at TV Rain — the largest independent Russian-language news channel, based in Amsterdam. 24,000 hours of original production. Licensing conversations with AI companies arrived and stalled. Danbi is what the newsroom built to solve its own problem.