CUSTOMER SUPPORT

Draft Front replies but Slack-escalate the low-confidence ones

Drafts a tone-matched Front reply from similar resolved threads, then branches on the model's confidence: high-confidence drafts post silently.

CategoryCustomer Support
Enginesim
Difficultyintermediate
Triggerevent
Steps5
Setup~15 min

How it runs

The automated pipeline, trigger to output.

  • TriggerNew inbound message in FrontFront
  • ActionRetrieve similar resolved threadsPostgreSQLPostgres
  • ActionGenerate reply with confidence scoreOpenAI
  • LogicBranch on confidence threshold
  • OutputCreate Front draft (high) or Slack escalate (low)Front

What it does

It writes a draft reply for every Front conversation, but treats uncertainty as a first-class signal. When the model is confident the answer matches a known resolution pattern, it quietly attaches the draft. When it isn't — novel issue, thin context, conflicting examples — it escalates to Slack instead of guessing.

When to use it

Use it when bad auto-drafts are worse than none, and you want a safety valve that routes hard cases to a human early instead of letting a weak draft sit in the queue.

How it works

  1. 1A new inbound Front message triggers the flow.
  2. 2The engine retrieves similar resolved threads from Postgres and asks OpenAI for both a reply and a confidence score.
  3. 3A logic branch checks the score against your threshold.
  4. 4High-confidence path: the draft is created on the Front conversation for quick agent review.
  5. 5Low-confidence path: a Slack message posts the question, the closest matches, and a link to the thread so a human can take it directly.

Set it up

What you configure once, before turning it on.

  1. 1
    Connect FrontShared inbox, conversations.
  2. 2
    Connect PostgresAny Postgres URL — query, write, migrate.
  3. 3
    Connect OpenAIModels, embeddings, files.
  4. 4
    Connect SlackChannels, DMs, threads, mentions.
  5. 5
    Set each agent's modelWe leave models unset so you pick the tier — fast + cheap, or top-quality.
  6. 6
    Tune it to your dataEdit the prompts, filters, and field mappings so it matches how your team works.
  7. 7
    Test, then turn it onRun once against a sample, confirm the output, then enable the trigger.

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