AI & RAG

Draft Grounded Support Replies from the Coda Knowledge Hub

On a new Zendesk ticket, retrieves matching rows from the Coda knowledge hub and drafts a reply grounded in them as an internal note.

CategoryAI & RAG
Enginesim
Difficultyintermediate
Triggerevent
Steps5
Setup~15 min

How it runs

The automated pipeline, trigger to output.

  • TriggerNew or updated Zendesk ticketZendeskZendesk
  • ActionRetrieve matching rows from Coda knowledge hubCodaCoda
  • LogicBranch: no relevant row → mark low confidence, skip draft
  • ActionDraft reply grounded only in retrieved rowsOpenAI
  • OutputPost draft, citations, and confidence as internal ticket noteZendeskZendesk

What it does

Gives support agents a head start without auto-sending anything risky. When a ticket arrives, the workflow finds relevant Coda knowledge rows, drafts a reply that only uses facts from those rows, and posts it as an internal note on the ticket along with citations and a confidence level. The agent edits and sends.

When to use it

Use it when your support answers live in Coda and you want faster first responses while keeping a human in the loop. Best for teams that value accuracy and traceability over full automation.

How it works

  1. 1A new or updated Zendesk ticket triggers the workflow.
  2. 2The workflow retrieves the top matching rows from the Coda knowledge hub using the ticket subject and body.
  3. 3A logic step assigns low confidence and skips drafting when no row is sufficiently relevant.
  4. 4OpenAI drafts a reply grounded strictly in the retrieved rows, listing each cited row.
  5. 5The draft, citations, and confidence flag are posted as an internal note on the Zendesk ticket for agent review.

Set it up

What you configure once, before turning it on.

  1. 1
    Connect ZendeskTickets, queues, knowledge base.
  2. 2
    Connect CodaDocs, packs, automations.
  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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