CUSTOMER SUPPORT

Verify a Front reply has no existing doc, then draft one in ReadMe

When a Front conversation is resolved, searches the existing knowledge base to confirm no article already covers it before drafting a new doc in ReadMe and pinging the author.

CategoryCustomer Support
Enginesim
Difficultyadvanced
Triggerevent
Steps6
Setup~25 min

How it runs

The automated pipeline, trigger to output.

  • TriggerFront conversation resolvedFront
  • ActionExtract question and answer from threadFront
  • ActionSearch existing knowledge base for a matchReadMeReadMe
  • LogicBranch on whether a matching doc exists
  • ActionDraft new article from verified answerOpenAI
  • OutputCreate hidden draft page in ReadMeReadMeReadMe

What it does

Stops false positives. Instead of assuming any freehand reply is a gap, it resolves the Front conversation, then actively searches your existing knowledge base for an article that already answers the question. Only when the search comes back empty does it draft a new doc. This avoids creating duplicates of articles agents simply forgot to link.

When to use it

Use it when your knowledge base is large enough that "no link" doesn't reliably mean "no article." Ideal for teams publishing developer or product docs in ReadMe who need verification before adding pages.

How it works

  1. 1A Front conversation is resolved, firing the trigger.
  2. 2The flow extracts the customer question and the agent's answer from the thread.
  3. 3It searches the existing ReadMe knowledge base for articles matching the question.
  4. 4A logic step branches: if a strong match exists, it nudges the agent in Front to link it next time and stops; if not, the gap is confirmed.
  5. 5OpenAI drafts a new article from the verified answer.
  6. 6The draft is created in ReadMe as a hidden page for editorial review.

Set it up

What you configure once, before turning it on.

  1. 1
    Connect FrontShared inbox, conversations.
  2. 2
    Connect ReadMeAPI docs, changelog, auth.
  3. 3
    Connect OpenAIModels, embeddings, files.
  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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