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Run 24/7 WhatsApp support and track application status

A consulting firm used n8n multi-agent workflows to answer WhatsApp questions, collect information, parse uploaded documents and notify clients of status.

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Use case overview

A consulting firm used n8n multi-agent workflows to answer WhatsApp questions, collect information, parse uploaded documents and notify clients of status.

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Reported experience

  1. What they did

    The author built five AI agents (customer interaction, document interpretation, user and admin application tracking, and a supervisor) with n8n workflows handling WhatsApp, Google Drive and email connections. Because n8n is stateless, he added a database for conversation state. He added custom Python for retrieval over company documents, and a task queue so the supervisor could hand uploaded documents to the right agent.

  2. How it went

    The article describes the architecture and code patterns but gives no usage figures, accuracy numbers or client results. It says the approach works for small teams but offers no measured evidence.

  3. Worth knowing

    n8n isn't built for high concurrency or vector storage, so expect to add your own queue, state store and retrieval code outside it.

Example prompts

Try with n8n
Build an n8n workflow for my [consulting or services] business that answers client questions on WhatsApp around the clock, collects [required details, e.g. name, case type, documents], parses uploaded PDFs or photos into a Google Sheet, and messages the client when their status changes in [status column]. Show me the first message it would send for each status before enabling sending, and finish when one test client goes from intake to a status update.

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