AI驱动고객反馈분석与路由，통합Gmail、Zendesk、Slack및Pipedrive

• •Google 계정 및 Gmail API 인증 정보
• •Slack Bot Token 또는 Webhook URL
• •Notion API Key
• •OpenAI API Key

{
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    {
      "id": "60256706-eabb-4ff1-abf0-78b5a9ca0869",
      "name": "수동 트리거: Start VOC Analysis",
      "type": "n8n-nodes-base.manualTrigger",
      "position": [
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      "parameters": {},
      "typeVersion": 1
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    {
      "id": "be859333-a941-4a71-951f-eeb6adcd0e4f",
      "name": "설정: Initial Parameters",
      "type": "n8n-nodes-base.set",
      "position": [
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      "parameters": {
        "options": {},
        "assignments": {
          "assignments": [
            {
              "id": "d69bdbe2-f51a-4956-9d5d-bfe3a82ec82d",
              "name": "CSM email",
              "type": "string",
              "value": "your-email@example.com"
            },
            {
              "id": "3efe4a59-2983-4f07-8e5c-130a5aad6fdb",
              "name": "slack_billing_channel",
              "type": "string",
              "value": "#billing-feedback"
            }
          ]
        }
      },
      "typeVersion": 3.4
    },
    {
      "id": "c263ec19-9bdb-46fb-afde-4a17da961d3c",
      "name": "Config: 설정 LLM for Agents",
      "type": "@n8n/n8n-nodes-langchain.lmChatOpenAi",
      "position": [
        1552,
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      ],
      "parameters": {
        "model": {
          "__rl": true,
          "mode": "list",
          "value": "gpt-4.1-mini"
        },
        "options": {
          "temperature": 0
        }
      },
      "typeVersion": 1.2
    },
    {
      "id": "bf4488bd-0e20-4827-b370-77396415f7c8",
      "name": "Config: Set Agent 메모리",
      "type": "@n8n/n8n-nodes-langchain.memoryBufferWindow",
      "position": [
        432,
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      ],
      "parameters": {
        "sessionKey": "1",
        "sessionIdType": "customKey"
      },
      "typeVersion": 1.3
    },
    {
      "id": "8f128774-c198-4a13-8d19-8cf5ac19c8b8",
      "name": "AI 에이전트: Gather Customer Feedback",
      "type": "@n8n/n8n-nodes-langchain.agent",
      "position": [
        736,
        368
      ],
      "parameters": {
        "text": "=- Get **ALL** the mails sent after {{Date.now() - 7 * 24 * 60 * 60 * 1000}} from the user {{ $('Set: Initial Parameters').item.json['CSM email'] }}. Return only the Subject and the snippet.\n- Get **ALL** the messages from Slack return the user ID as customerId.\n- Get **ALL** the notes from Pipedrive. Use person_id as the customerId\n- Get **ALL** the tickets from Zendesk. Use requester_id as customerId",
        "options": {},
        "promptType": "define",
        "hasOutputParser": true
      },
      "typeVersion": 2.2
    },
    {
      "id": "6b70f528-bd39-4758-9d04-6b3ff93af6ff",
      "name": "AI: Structure Feedback Data",
      "type": "@n8n/n8n-nodes-langchain.outputParserStructured",
      "position": [
        1184,
        592
      ],
      "parameters": {
        "autoFix": true,
        "jsonSchemaExample": "\n  [{\n  \"source\": \"Zendesk | Gmail | Slack | Pipedrive\",\n  \"customerId\": \"...\",\n  \"messageId\": \"\",\n  \"subject\": \"...\",\n  \"text\": \"...\"\n}]\n\n"
      },
      "typeVersion": 1.3
    },
    {
      "id": "7c5791aa-2842-47af-818b-004af9685455",
      "name": "Tool: Get Gmail Messages",
      "type": "n8n-nodes-base.gmailTool",
      "position": [
        576,
        592
      ],
      "webhookId": "d4a1dd6b-781c-4c44-b6a6-2d84a5542281",
      "parameters": {
        "filters": {
          "sender": "=",
          "receivedAfter": "={{ /*n8n-auto-generated-fromAI-override*/ $fromAI('Received_After', ``, 'string') }}"
        },
        "operation": "getAll",
        "returnAll": "={{ /*n8n-auto-generated-fromAI-override*/ $fromAI('Return_All', ``, 'boolean') }}"
      },
      "typeVersion": 2.1
    },
    {
      "id": "fbca39a8-6593-4cdb-a582-3bb81d18cb3a",
      "name": "Tool: Get Pipedrive Notes",
      "type": "n8n-nodes-base.pipedriveTool",
      "position": [
        736,
        592
      ],
      "parameters": {
        "resource": "note",
        "operation": "getAll",
        "additionalFields": {}
      },
      "typeVersion": 1
    },
    {
      "id": "a833e7b4-6510-44a5-934a-9d3b46289717",
      "name": "Tool: Get Zendesk Tickets",
      "type": "n8n-nodes-base.zendeskTool",
      "position": [
        880,
        592
      ],
      "parameters": {
        "options": {},
        "operation": "getAll"
      },
      "typeVersion": 1
    },
    {
      "id": "28a49bba-62fa-4896-8604-098903b84450",
      "name": "Tool: Search Slack Messages  Export to Sheets",
      "type": "n8n-nodes-base.slackTool",
      "position": [
        1024,
        592
      ],
      "webhookId": "cfa05622-054d-4981-8b27-79a0c88bfaea",
      "parameters": {
        "query": "={{ /*n8n-auto-generated-fromAI-override*/ $fromAI('Search_Query', ``, 'string') }}",
        "options": {
          "searchChannel": ""
        },
        "operation": "search",
        "authentication": "oAuth2"
      },
      "typeVersion": 2.3
    },
    {
      "id": "b9212f35-c028-481e-908b-12aa3324ac25",
      "name": "AI 체인: Extract Key Signals",
      "type": "@n8n/n8n-nodes-langchain.chainLlm",
      "position": [
        1552,
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      ],
      "parameters": {
        "text": "=Prompt:\nYou are analyzing raw customer feedback from multiple sources (Gmail, Slack, Pipedrive, Zendesk).\nYour task: compress the \"text\" of each feedback into a concise signal (1–2 sentences max) that captures the core issue, request, or sentiment without losing meaning.\n\nRules:\n\t•\tStrip away greetings, signatures, and filler.\n\t•\tKeep specific product terms, error codes, or feature names if present.\n\t•\tNeutral, factual tone (don’t add assumptions).\n\t•\tIf the text is vague, summarize it at the same level of vagueness.\n\t•\tOutput only the summary text, no extra commentary.\n\nExample:\n\t•\tInput: “Hi team, I’ve tried three times to update my billing info but the system keeps failing with error 502. Can someone help?”\n\t•\tOutput: Customer unable to update billing info due to repeated error 502. \n\nHere is the content:\n{{ JSON.stringify($json.output) }}",
        "batching": {
          "batchSize": 5
        },
        "promptType": "define",
        "hasOutputParser": true
      },
      "typeVersion": 1.7
    },
    {
      "id": "7ec417ac-bc65-4c9e-a1f7-dc3d295403e0",
      "name": "AI: Structure Key Signals",
      "type": "@n8n/n8n-nodes-langchain.outputParserStructured",
      "position": [
        1696,
        592
      ],
      "parameters": {
        "jsonSchemaExample": "[\n\t{\"original_text\": \"\",\n\t\"signals\": [\"\", \"\"]\n}]"
      },
      "typeVersion": 1.3
    },
    {
      "id": "09797f66-2ce6-4392-afb4-a646ba733799",
      "name": "AI 체인: Cluster Signals into Topics",
      "type": "@n8n/n8n-nodes-langchain.chainLlm",
      "position": [
        1904,
        368
      ],
      "parameters": {
        "text": "=Prompt:\nYou will receive a set of short customer feedback signals.\nYour task: group them by shared topic or problem and assign each group a clear, human-readable label.\n\nRules:\n\t•\tLabels should be broad enough to cover all items in the group, but still actionable (e.g. Billing, Onboarding, Performance, Feature Requests).\n\t•\tAvoid vague labels like General Feedback unless no pattern exists.\n\t•\tEach cluster must include:\n\t•\tLabel\n\t•\tCount of items\n\t•\tRepresentative examples (1–3 feedback snippets).\n\n {{ JSON.stringify($json.output) }}",
        "batching": {},
        "promptType": "define",
        "hasOutputParser": true
      },
      "typeVersion": 1.7
    },
    {
      "id": "d31b8993-f7c4-496f-9515-ae2e39e84f17",
      "name": "AI: Structure Clustered Topics  Export to Sheets",
      "type": "@n8n/n8n-nodes-langchain.outputParserStructured",
      "position": [
        2048,
        592
      ],
      "parameters": {
        "jsonSchemaExample": "[\n  {\n    \"label\": \"Billing\",\n    \"count\": 8,\n    \"examples\": [\n      \"Unable to update billing info due to error 502\",\n      \"Invoice shows wrong amount\"\n    ]\n  }\n]"
      },
      "typeVersion": 1.3
    },
    {
      "id": "b26017dd-99f0-4033-9fb4-cfcf3be03c14",
      "name": "AI 에이전트: Route Topics to Actions",
      "type": "@n8n/n8n-nodes-langchain.agent",
      "position": [
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        368
      ],
      "parameters": {
        "text": "=Prompt:\nYou will receive a list of feedback clusters, each with a label, count, and examples.\nFirst you will send the processed input to \"{{ $('Set: Initial Parameters').item.json['CSM email'] }}\" with the subject \"Weekly digest\"\nYour task: decide the correct destination action for each cluster based on the label and the count if it is superior to 1. Each message will have the examples in it.\n\n## Routing Rules:\n### Performance / Feature gaps → Product\n    - Create a zendesk ticket with the label as the title and the examples as the description\n### Billing / Contract issues → Finance or Sales Ops\n  - Post message to Slack channel {{ $('Set: Initial Parameters').item.json.slack_billing_channel }} with the examples as the text\n### Onboarding / Training → CS Enablement\n  - Create Notion task with the label as the title and the examples as the content\n### High-risk sentiment / VIP account → CS Manager\n  - Send direct email to cs Manager \"{{ $('Set: Initial Parameters').item.json['CSM email'] }}\" with tzhe subject \"Problem with the software\" and the examples as the text\n### Sales and customer engagement\n  -  Send direct email to cs Manager \"{{ $('Set: Initial Parameters').item.json['CSM email'] }}\" with the subject \"Customer engagement\" and the examples as the text\n\n### Client Management and Proposals\n    -  Send direct email to cs Manager \"{{ $('Set: Initial Parameters').item.json['CSM email'] }}\" with the subject \"Client Management and Proposals\" with the examples as the text\n  \nIf the cluster doesn’t fit above, mark as \"unassigned\" but keep it in the output.\nThe input:\n {{ JSON.stringify($json.output) }}",
        "options": {},
        "promptType": "=define"
      },
      "typeVersion": 2.2
    },
    {
      "id": "ebefbd63-8ea6-49e3-be48-4aaf1b4f7011",
      "name": "Tool: Create Zendesk Ticket",
      "type": "n8n-nodes-base.zendeskTool",
      "position": [
        2320,
        592
      ],
      "parameters": {
        "description": "Ticket generated by n8n",
        "jsonParameters": "={{ /*n8n-auto-generated-fromAI-override*/ $fromAI('JSON_Parameters', ``, 'boolean') }}",
        "additionalFields": {}
      },
      "typeVersion": 1
    },
    {
      "id": "2b0c3dc2-cf60-4d2b-8127-4141d34e7bee",
      "name": "Tool: Send 이메일 Alert",
      "type": "n8n-nodes-base.gmailTool",
      "position": [
        2464,
        592
      ],
      "webhookId": "127092c9-48cd-40be-9d99-afd264bd95fb",
      "parameters": {
        "sendTo": "={{ /*n8n-auto-generated-fromAI-override*/ $fromAI('To', ``, 'string') }}",
        "message": "={{ /*n8n-auto-generated-fromAI-override*/ $fromAI('Message', ``, 'string') }}",
        "options": {},
        "subject": "={{ /*n8n-auto-generated-fromAI-override*/ $fromAI('Subject', ``, 'string') }}",
        "emailType": "text"
      },
      "typeVersion": 2.1
    },
    {
      "id": "b47fc28d-72f5-42f9-a661-254a392ae443",
      "name": "Tool: Create Notion Page",
      "type": "n8n-nodes-base.notionTool",
      "position": [
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      ],
      "parameters": {
        "title": "={{ /*n8n-auto-generated-fromAI-override*/ $fromAI('Title', ``, 'string') }}",
        "simple": false,
        "options": {},
        "resource": "databasePage",
        "databaseId": {
          "__rl": true,
          "mode": "id",
          "value": ""
        },
        "propertiesUi": {
          "propertyValues": [
            {
              "key": "Content|rich_text",
              "textContent": "={{ /*n8n-auto-generated-fromAI-override*/ $fromAI('propertyValues0_Text', ``, 'string') }}"
            }
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      },
      "notesInFlow": false,
      "typeVersion": 2.2
    },
    {
      "id": "fdac92b0-5b36-4fdc-a384-93c2d6c20cc6",
      "name": "노트: Data Gathering",
      "type": "n8n-nodes-base.stickyNote",
      "position": [
        704,
        176
      ],
      "parameters": {
        "color": 7,
        "width": 380,
        "height": 128,
        "content": "### Data Gathering Agent\nThis AI Agent's job is to collect all recent customer interactions.\nIt uses its tools (Gmail, Pipedrive, Zendesk, Slack) to fetch the raw data based on the initial prompt."
      },
      "typeVersion": 1
    },
    {
      "id": "ab1edac7-c359-433a-bad5-70c876d9cfb1",
      "name": "노트: Analysis Chain",
      "type": "n8n-nodes-base.stickyNote",
      "position": [
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        160
      ],
      "parameters": {
        "color": 7,
        "width": 476,
        "height": 152,
        "content": "### AI Analysis Chain\nThis chain processes the raw data in two steps:\n1.  **Signal Extraction:** The first LLM Chain reads all the raw text and compresses it into concise 'signals'.\n2.  **Clustering:** The second LLM Chain takes these signals and groups them into actionable topics (e.g., 'Billing', 'Performance')."
      },
      "typeVersion": 1
    },
    {
      "id": "0e500b93-135e-4a03-91e8-2159cc58718b",
      "name": "노트: Action Agent",
      "type": "n8n-nodes-base.stickyNote",
      "position": [
        2224,
        160
      ],
      "parameters": {
        "color": 7,
        "width": 412,
        "height": 152,
        "content": "### Action & Routing Agent\nThis final AI Agent acts as a dispatcher. It analyzes the clustered topics and follows a set of 'Routing Rules' in its prompt to decide which action to take.\nIt then uses its tools to send the information to the correct destination (Zendesk, Slack, Notion, or Email)."
      },
      "typeVersion": 1
    },
    {
      "id": "0642f4ae-f111-4446-9e9a-c9bb0c1e609c",
      "name": "Workflow Documentation",
      "type": "n8n-nodes-base.stickyNote",
      "position": [
        16,
        -320
      ],
      "parameters": {
        "color": 4,
        "width": 980,
        "height": 392,
        "content": "### **Voice of Customer AI Analysis & Routing**\nThis workflow automates the process of gathering customer feedback from multiple sources, using a chain of AI agents to analyze, summarize, and categorize it, and finally routing the insights to the appropriate teams for action.\n\n**How it Works:**\n1.  **Gathers Data:** An AI Agent uses tools to collect recent messages from Gmail, Pipedrive, Zendesk, and Slack.\n2.  **Analyzes & Summarizes:** An AI Chain processes the raw text, first extracting key 'signals' and then clustering those signals into topics (e.g., 'Billing', 'Feature Request').\n3.  **Routes for Action:** A final AI Agent analyzes the topics and uses tools to create Zendesk tickets, send Slack messages, create Notion pages, or send email alerts based on a set of rules.\n\n### 🚀 **How to Set Up**\n1.  **Configure Credentials:** Add your credentials for all the 'Tool' nodes and the `Config: Set LLM for Agents` node.\n2.  **Set Initial Parameters:** In the `Set: Initial Parameters` node, update the placeholder email address and the Slack channel name for billing alerts.\n3.  **Update Slack Search Channel:** In the `Tool: Search Slack Messages` node, set the channel you want the agent to search for feedback in.\n4.  **Activate Workflow:** Once configured, activate the workflow."
      },
      "typeVersion": 1
    },
    {
      "id": "eff608be-8f8d-4379-a9f0-5ab1beb26b3d",
      "name": "메모1",
      "type": "n8n-nodes-base.stickyNote",
      "position": [
        32,
        960
      ],
      "parameters": {
        "color": 6,
        "width": 432,
        "height": 176,
        "content": "## Contact me\n- If you need any modification to this workflow\n- if you need some help with this workflow\n- Or if you need any workflow in n8n, Make, or Langchain / Langgraph\n\nWrite to me: [thomas@pollup.net](<mailto:thomas@pollup.net>)"
      },
      "typeVersion": 1
    }
  ],
  "connections": {
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            "node": "AI Agent: Route Topics to Actions",
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    "Set: Initial Parameters": {
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    "Config: Set Agent Memory": {
      "ai_memory": [
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            "type": "ai_memory",
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    "7ec417ac-bc65-4c9e-a1f7-dc3d295403e0": {
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    "Config: Set LLM for Agents": {
      "ai_languageModel": [
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          {
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            "type": "ai_languageModel",
            "index": 0
          },
          {
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            "type": "ai_languageModel",
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          },
          {
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            "type": "ai_languageModel",
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          {
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}