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AI Customer SupportSocial Messaging Automation

Facebook AI Customer Support Agent

An AI-powered Facebook Messenger support agent connected to n8n and a business knowledge base. The system receives customer inquiries, retrieves relevant business information, generates context-aware responses, and sends the reply through the Meta Messenger API.

Workflow canvas for Facebook AI Customer Support Agent
OwnershipStrategy, workflow design, implementation, and documentation by Dominic Ortega.
Project statusIn Progress
Evidence and privacyWorkflow diagrams and implementation details are shown; sensitive client and lead data is excluded.

Business problem

Businesses answering Facebook inquiries manually can miss messages, repeat the same explanations, and struggle to maintain consistent response quality.

What I built

A Messenger webhook sends incoming messages into n8n, extracts sender and message data, retrieves approved business knowledge, drafts a response, and sends a formatted reply through the Meta API.

How I built the workflow

01

Receive message

Facebook sends a Messenger event into the webhook.

02

Extract context

The workflow identifies sender details and message content.

03

Retrieve knowledge

Approved business information is loaded from configured documents.

04

Generate response

AI drafts a context-aware response using the available knowledge.

05

Send reply

The Meta Messenger API delivers the formatted message.

06

Escalate

Unclear or sensitive cases can be routed to a human.

Tools and integrations

n8nFacebook MessengerMeta Graph APIWebhooksGoogle DocsAI agentJSONKnowledge baseAI model

Key features

  • Facebook webhook intake
  • Sender identification
  • Message extraction
  • Business knowledge retrieval
  • AI-generated support responses
  • Messenger reply formatting
  • Error and fallback handling
  • Human escalation path

Intended impact

  • Expected to improve response consistency.
  • Expected to reduce repetitive support drafting.
  • Keeps human escalation available for sensitive or unresolved cases.

Project results

ExpectedSupport response

Designed for faster first replies after production testing.

DesignedEscalation path

Human handoff is planned as part of the flow.

Future improvements

Vector database or RAG layer after the basic support flow is provenConversation taggingAgent handoff dashboard
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