Cloudglue

Describe returns a moment-by-moment breakdown of a single video, covering transcript, diarization, visual descriptions, audio descriptions, sound, and on-screen text. It leans on multimodal AI to combine speech, visuals, and audio rather than text alone. You get the full picture of a video in one call, which saves you from running several separate models. It's the right starting point when you need every detail before deciding what to extract.

Extract pulls structured data out of videos using either a plain prompt or a custom schema you define. You can target one file or run it across a collection so every video returns the same fields. For teams that need consistent output, the schema approach is the difference between clean rows and a pile of paragraphs to parse by hand.

Search adds semantic search over videos and segments using natural-language queries. Your users type what they're looking for and get back the clips that match, not just keyword hits. It runs on the same indexed corpus as chat, so you build the search layer once and reuse it everywhere.

Chat Completion lets you build conversational tools that query, compare, and reason across hundreds of videos, with citations attached to each answer. That citation support matters when users need to verify a claim. A Responses API adds streaming, multi-turn support, and background processing for longer jobs.

Entity collections process many videos with a consistent schema, while media description collections organize footage for searchable multimodal transcription. Collection chat then works across an entire library. This is how you move from handling one file to treating thousands of hours as a single corpus.

Cloudglue ships Python and JavaScript SDKs, an MCP server for Claude, Cursor, and Windsurf, webhooks for real-time processing notifications, and a browser playground. A built-in Schema Builder helps you design extraction schemas visually. The tooling keeps the integration light, so you spend time on your product rather than on plumbing.