CUSTOMER SUPPORT

Suggest the right Loom video by classifying Intercom message intent

Reads each new inbound Intercom conversation, classifies what the customer is trying to do, and surfaces the best-matching Loom walkthrough to the agent as an internal note.

CategoryCustomer Support
Enginesim
Difficultyintermediate
Triggerevent
Steps5
Setup~15 min

How it runs

The automated pipeline, trigger to output.

  • TriggerNew Intercom conversation openedIntercomIntercom
  • ActionClassify message intent with confidence scoreOpenAI
  • LogicBranch on confidence threshold
  • ActionResolve Loom walkthrough for matched intentLoomLoom
  • OutputPost suggested video as internal noteIntercomIntercom

What it does

Instead of relying on agents to pick a tag, this workflow reads the customer's actual Intercom message, classifies the underlying intent (billing change, integration setup, data export, and so on), and matches it to your library of Loom how-to videos. It then leaves an internal note suggesting the most relevant walkthrough with a confidence score.

When to use it

Use it for high-volume inbound chat where customers describe problems in their own words and you want agents to reach for video help without first triaging the topic by hand. It works best once you have 5 or more Loom walkthroughs covering your top questions.

How it works

  1. 1A new Intercom conversation is opened by a customer.
  2. 2OpenAI classifies the message into one of your defined how-to intents and returns a confidence score.
  3. 3A branch checks the score: low-confidence messages skip suggestion to avoid noise.
  4. 4The workflow resolves the Loom video mapped to the matched intent.
  5. 5It posts an internal note in the Intercom conversation with the suggested video and reasoning, leaving the send decision to the agent.

Set it up

What you configure once, before turning it on.

  1. 1
    Connect IntercomConversations, contacts, articles.
  2. 2
    Connect OpenAIModels, embeddings, files.
  3. 3
    Connect LoomVideo transcripts, libraries.
  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.

Run this workflow in your colony.

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