SUMMARIZATION
Conversational Agent Over Zoom Recording Topic Index
A chat agent that answers natural-language questions about your recorded meetings by searching the chaptered topic index and returning answers with timestamped recording links.
How it runs
The automated pipeline, trigger to output.
- TriggerChat question received
- ActionSemantic search over chapter indexPostgres
- LogicBail if no chapters clear relevance threshold
- ActionCompose grounded answer from matched chaptersOpenAI
- OutputReply in chat with answer and recording jump linksZoom
What it does
Lets anyone ask 'what did we decide about the Q3 budget?' in chat and get an answer grounded in past Zoom calls, complete with the exact recording moments to verify.
When to use it
When meeting knowledge is buried across dozens of recordings and people need to retrieve specific decisions or discussions on demand rather than scrubbing video.
How it works
- 1A chat message starts the agent with the user's question.
- 2The agent embeds the query and searches the stored chapter index in Postgres, where prior runs have indexed every meeting's timestamped chapters and summaries.
- 3A logic step checks whether any chapters clear the relevance threshold; if not, the agent says it found nothing rather than guessing.
- 4The agent reads the matching chapter transcripts and composes a grounded answer, citing each source meeting.
- 5It replies in the chat with the answer plus timestamped `#t=` jump links into the relevant recordings.
- 6The agent can follow up across turns, refining the search as the conversation continues.
Set it up
What you configure once, before turning it on.
- 1Connect PostgresAny Postgres URL — query, write, migrate.
- 2Connect OpenAIModels, embeddings, files.
- 3Connect ZoomMeetings, recordings, transcripts.
- 4Set each agent's modelWe leave models unset so you pick the tier — fast + cheap, or top-quality.
- 5Tune it to your dataEdit the prompts, filters, and field mappings so it matches how your team works.
- 6Test, then turn it onRun once against a sample, confirm the output, then enable the trigger.
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Run this workflow in your colony.
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