AI & RAG

Index Loom SOP recordings into a searchable knowledge base

When a new Loom walkthrough is published, transcribe it, split the transcript into timestamped chunks, embed them, and store them in a vector index so 'how do I' questions can be…

CategoryAI & RAG
Enginesim
Difficultyintermediate
Triggerevent
Steps5
Setup~15 min

How it runs

The automated pipeline, trigger to output.

  • TriggerLoom recording finished processingLoomLoom
  • ActionFetch transcript + metadata from LoomLoomLoom
  • LogicChunk transcript with timestamp offsets
  • ActionEmbed chunks with OpenAIOpenAI
  • OutputUpsert chunks + vectors into pgvectorPostgreSQLPostgres

What it does

Turns your growing pile of Loom screen recordings into a queryable RAG knowledge base. Every new walkthrough is automatically transcribed, broken into time-aligned passages, embedded, and written to a Postgres pgvector table alongside the Loom share URL and a per-chunk timestamp offset. This is the ingestion half of a Loom SOP assistant — run it once and every later question can cite the precise second in the right video.

When to use it

Use it when your team records standard operating procedures in Loom but nobody can find the right clip later. Ideal for ops, support, and enablement teams who want answers that link straight to the relevant 20-second segment instead of forcing people to scrub a 15-minute video.

How it works

  1. 1A Loom webhook fires when a recording finishes processing.
  2. 2The flow pulls the video's transcript and metadata (title, duration, share URL) from Loom.
  3. 3It splits the transcript into overlapping chunks, each tagged with its start-time offset.
  4. 4OpenAI embeds each chunk into a vector.
  5. 5The chunks, vectors, Loom URL, and timestamps are upserted into a Postgres pgvector table, ready for retrieval.

Set it up

What you configure once, before turning it on.

  1. 1
    Connect LoomVideo transcripts, libraries.
  2. 2
    Connect OpenAIModels, embeddings, files.
  3. 3
    Connect PostgresAny Postgres URL — query, write, migrate.
  4. 4
    Set each agent's modelWe leave models unset so you pick the tier — fast + cheap, or top-quality.
  5. 5
    Tune it to your dataEdit the prompts, filters, and field mappings so it matches how your team works.
  6. 6
    Test, then turn it onRun once against a sample, confirm the output, then enable the trigger.

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