n8n with AI and privacy: a customer service workflow
Use RN's n8n example to receive a message and return an answer. Check authentication, limits and responsibilities before connecting your channel.
Rota Nacional · Published · Reviewed
The example performs a bounded task
RN provides an importable n8n customer service workflow. It receives a message field from another workflow, validates input and calls RN chat. The result contains reply and model. The example uses fictional data so the first run needs no customer information.
The workflow does not send WhatsApp messages. The calling application owns the channel. It must authenticate input, control duplicates and decide when to hand the conversation to a person.
Separate authentication from content
Import the integration JSON and configure Bearer Auth in the HTTP Request node. Keep the key in n8n credentials, separate from the JSON body. The example does not need an external provider key: it uses the RN key.
Check the rota/* model and the key's organization. The call follows RN's configured policy. If the team requires the barrier, continue using the protected contract when adapting the workflow.
Connect to a real process with controls
The published version validates messages from 1 to 8,000 characters. It does not save execution data or automatically retry a failed call. Preserve a retention policy appropriate to your process when adapting the workflow.
Test errors, insufficient balance and human handoff before connecting a real channel. Do not treat a generated answer as authority to issue charges, change records or promise commercial terms. Those actions require application rules and permissions.
Frequently asked questions
Does the workflow send the answer to WhatsApp?
No. It returns reply and model. The calling application integrates the channel and controls authentication, duplicates and human handoff.
Does the key belong in the request JSON?
No. Configure Bearer Auth in n8n and keep credentials separate from content.
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