최고 재무 책임자 예측 에이전트
고급
이것은AI, IT Ops분야의자동화 워크플로우로, 16개의 노드를 포함합니다.주로 Set, Code, Stripe, Supabase, GoogleSheets 등의 노드를 사용하며인공지능 기술을 결합하여 스마트 자동화를 구현합니다. Stripe 데이터 기반 GPT-4 및 Google Sheets 자동화 수익 예측
사전 요구사항
- •Stripe API Key
- •Supabase URL과 API Key
- •Google Sheets API 인증 정보
- •OpenAI API Key
- •Pinecone API Key
사용된 노드 (16)
워크플로우 미리보기
노드 연결 관계를 시각적으로 표시하며, 확대/축소 및 이동을 지원합니다
워크플로우 내보내기
다음 JSON 구성을 복사하여 n8n에 가져오면 이 워크플로우를 사용할 수 있습니다
{
"id": "mHSoMMyypmRzfVZn",
"meta": {
"instanceId": "84ad02d6104594179f43f1ce9cfe3a81637b2faedb57dafcb9e649b7542988db"
},
"name": "CFO forecasting Agent",
"tags": [],
"nodes": [
{
"id": "b946638e-ba68-4d73-816e-00f3a63d138f",
"name": "구조화된 출력 파서",
"type": "@n8n/n8n-nodes-langchain.outputParserStructured",
"position": [
1420,
220
],
"parameters": {
"jsonSchemaExample": "{\n \"forecast\": {\n \"June 2025\": \"$X,XXX.XX\",\n \"July 2025\": \"$X,XXX.XX\",\n \"August 2025\": \"$X,XXX.XX\"\n },\n \"trend\": \"Increasing / Decreasing / Stable\",\n \"confidence\": \"High / Medium / Low\",\n \"insights\": \"Short explanation of why this trend is predicted.\"\n}"
},
"typeVersion": 1.2
},
{
"id": "d80c3670-e8c8-43ee-ba12-2e0e18b99862",
"name": "OpenAI 채팅 모델",
"type": "@n8n/n8n-nodes-langchain.lmChatOpenAi",
"position": [
1180,
220
],
"parameters": {
"model": {
"__rl": true,
"mode": "list",
"value": "gpt-4o-mini"
},
"options": {}
},
"credentials": {
"openAiApi": {
"id": "wYwTjEv45IzlAOAu",
"name": "OpenAi account 2"
}
},
"typeVersion": 1.2
},
{
"id": "bb75cafb-9dad-4952-89af-0658c4d88aa4",
"name": "Pinecone 벡터 저장소",
"type": "@n8n/n8n-nodes-langchain.vectorStorePinecone",
"position": [
1200,
400
],
"parameters": {
"mode": "retrieve-as-tool",
"options": {},
"toolName": "Sales_data",
"pineconeIndex": {
"__rl": true,
"mode": "list",
"value": "new",
"cachedResultName": "new"
},
"toolDescription": "The data about stripe sales"
},
"credentials": {
"pineconeApi": {
"id": "PSI5CiZnLRSkEgJg",
"name": "PineconeApi account"
}
},
"typeVersion": 1.1
},
{
"id": "7d370700-89d9-4163-8070-4a0c643531ca",
"name": "OpenAI 임베딩",
"type": "@n8n/n8n-nodes-langchain.embeddingsOpenAi",
"position": [
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],
"parameters": {
"options": {}
},
"credentials": {
"openAiApi": {
"id": "wYwTjEv45IzlAOAu",
"name": "OpenAi account 2"
}
},
"typeVersion": 1.2
},
{
"id": "81de1ecf-75de-44fd-9e62-c381e907c1e1",
"name": "일일 예측 실행",
"type": "n8n-nodes-base.scheduleTrigger",
"position": [
0,
0
],
"parameters": {
"rule": {
"interval": [
{
"triggerAtHour": 9
}
]
}
},
"typeVersion": 1.2
},
{
"id": "9ce2e0e4-8784-4ed9-9499-b5f54241d04e",
"name": "Stripe 청구 내역 가져오기",
"type": "n8n-nodes-base.stripe",
"position": [
220,
0
],
"parameters": {
"resource": "charge",
"operation": "getAll",
"returnAll": true
},
"typeVersion": 1
},
{
"id": "c115eb0c-6877-46db-bf03-55e48527dbc5",
"name": "판매 요약",
"type": "n8n-nodes-base.code",
"position": [
440,
0
],
"parameters": {
"jsCode": "const charges = items.map(item => item.json);\nconst summary = charges.reduce((acc, charge) => {\n const date = new Date(charge.created * 1000).toISOString().split(\"T\")[0];\n acc[date] = (acc[date] || 0) + charge.amount / 100;\n return acc;\n}, {});\nreturn [{ json: { summary } }];\n"
},
"typeVersion": 2
},
{
"id": "fb33581a-9f41-49d7-a722-a68dfe5bc265",
"name": "데이터 준비",
"type": "n8n-nodes-base.set",
"position": [
660,
0
],
"parameters": {
"options": {},
"assignments": {
"assignments": [
{
"id": "6aa7f5d2-3aa4-4d4c-85df-0d56bb7b6c9e",
"name": "summary",
"type": "string",
"value": "={{ $json.summary }}"
}
]
}
},
"typeVersion": 3.4
},
{
"id": "d3458681-2654-4e22-8b2d-1711b60ed592",
"name": "예측 에이전트",
"type": "@n8n/n8n-nodes-langchain.agent",
"position": [
1240,
0
],
"parameters": {
"text": "=You are a CFO AI Agent. Based on the following Stripe sales data:\n\n{{ $json.summary }}\n\nAnalyze the trends, identify any patterns (growth, decline, seasonality), and forecast expected daily or weekly revenue for the next 3 months.",
"options": {},
"promptType": "define",
"hasOutputParser": true
},
"typeVersion": 1.9
},
{
"id": "325e4952-868c-460a-aa78-2e718477cc78",
"name": "Supabase에 예측 저장",
"type": "n8n-nodes-base.supabase",
"position": [
2100,
-160
],
"parameters": {
"dataToSend": "autoMapInputData"
},
"typeVersion": 1
},
{
"id": "9fd6efab-edc6-49be-9516-f2d36e1995b2",
"name": "Google 시트에 예측 기록",
"type": "n8n-nodes-base.googleSheets",
"position": [
2100,
120
],
"parameters": {
"columns": {
"value": {
"trend": "={{ $json.output.trend }}",
"forecast": "={{ $json.output.forecast }}",
"insights": "={{ $json.output.insights }}",
"confidence": "={{ $json.output.confidence }}"
},
"schema": [
{
"id": "forecast",
"type": "string",
"display": true,
"required": false,
"displayName": "forecast",
"defaultMatch": false,
"canBeUsedToMatch": true
},
{
"id": "trend",
"type": "string",
"display": true,
"required": false,
"displayName": "trend",
"defaultMatch": false,
"canBeUsedToMatch": true
},
{
"id": "confidence",
"type": "string",
"display": true,
"required": false,
"displayName": "confidence",
"defaultMatch": false,
"canBeUsedToMatch": true
},
{
"id": "insights",
"type": "string",
"display": true,
"required": false,
"displayName": "insights",
"defaultMatch": false,
"canBeUsedToMatch": true
}
],
"mappingMode": "defineBelow",
"matchingColumns": [],
"attemptToConvertTypes": false,
"convertFieldsToString": false
},
"options": {},
"operation": "append",
"sheetName": {
"__rl": true,
"mode": "list",
"value": "gid=0",
"cachedResultUrl": "https://docs.google.com/spreadsheets/d/108tyyL--yUCbDk4drB__eztLSwjlxcmoRkqYsFnMLrY/edit#gid=0",
"cachedResultName": "Sheet1"
},
"documentId": {
"__rl": true,
"mode": "list",
"value": "108tyyL--yUCbDk4drB__eztLSwjlxcmoRkqYsFnMLrY",
"cachedResultUrl": "https://docs.google.com/spreadsheets/d/108tyyL--yUCbDk4drB__eztLSwjlxcmoRkqYsFnMLrY/edit?usp=drivesdk",
"cachedResultName": "CFO Forecasting"
}
},
"credentials": {
"googleSheetsOAuth2Api": {
"id": "K5yYfUnKFTqaRn6A",
"name": "Google Sheets account"
}
},
"typeVersion": 4.5
},
{
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"content": "## 1️⃣ **🔁 Data Retrieval & Preprocessing**\n\n**Nodes:**\n\n* 🕒 `Run Daily Forecast`\n* 🟦 `Fetch Stripe Charges`\n* 🧩 `Summarize Daily Sales`\n* ✏️ `Prepare Forecast Prompt`\n\n---\n\n### 🕒 `Run Daily Forecast`\n\n**Type:** Cron Trigger\n**Purpose:**\nAutomatically runs the workflow every day to keep forecasts updated with the latest sales data.\n\n🔧 **Configuration:**\n\n* Schedule: Daily at 6 AM UTC (or as needed)\n\n---\n\n### 🟦 `Fetch Stripe Charges`\n\n**Type:** Stripe Node\n**Purpose:**\nRetrieves all successful transactions from Stripe in a defined timeframe.\n\n📥 **Details:**\n\n* Resource: `Charges`\n* Operation: `Get Many`\n* Filters:\n\n * `created[gte]` (e.g. last 30 days)\n * `status: succeeded`\n* Expansion (optional): `data.customer` for customer context\n\n✅ **Output:** Raw Stripe sales data with timestamps and amounts\n\n---\n\n### 🧩 `Summarize Daily Sales`\n\n**Type:** Code Node\n**Purpose:**\nProcesses Stripe charges and summarizes revenue per day.\n\n🧠 **Logic:**\n\n* Converts Unix timestamps to `YYYY-MM-DD`\n* Aggregates total revenue per day\n* Converts cents to dollars\n\n📦 **Output Sample:**\n\n```json\n{\n \"2025-05-01\": 1245.50,\n \"2025-05-02\": 980.00\n}\n```\n\n---\n\n### ✏️ `Prepare Forecast Prompt`\n\n**Type:** Edit Fields / Function\n**Purpose:**\nFormats the summary into a natural language prompt for OpenAI.\n\n🧠 **Example Prompt:**\n\n```txt\nGiven the following sales data:\n{ \"2025-05-01\": 1245.50, ... }\n\nPredict trends and forecast sales for the next 3 months.\n```\n\n🧾 **Output:** `prompt` (String) → sent to the AI Agent\n"
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"content": "## 2️⃣ **🤖 AI Agent Forecasting**\n\n**Nodes:**\n\n* 🤖 `Forecast with OpenAI Agent`\n* 🧠 `OpenAI GPT-4 Model`\n* 📄 `Extract Forecast Output`\n* 🌲 `Store Context in Pinecone` *(Optional)*\n* 🧬 `Generate Embeddings` *(Optional)*\n\n---\n\n### 🤖 `Forecast with OpenAI Agent`\n\n**Type:** Tools Agent\n**Purpose:**\nActs as an intelligent agent that reads the sales summary and responds with forecasts and reasoning.\n\n🧠 **Prompt Input:**\nPassed from `Prepare Forecast Prompt`\n\n💬 **Uses:**\n\n* Model: `GPT-4`\n* Output Parser: Structured JSON format\n\n📈 **Forecast Intent:**\nPredicts next 3 months, identifies trends, and gives a confidence level\n\n---\n\n### 🧠 `OpenAI GPT-4 Model`\n\n**Type:** OpenAI Node\n**Purpose:**\nHandles the natural language generation based on the supplied prompt.\n\n🧾 **Configuration:**\n\n* Model: `gpt-4` or `gpt-4-turbo`\n* Temperature: `0.2` (more deterministic)\n* Max Tokens: `1000`\n\n---\n\n### 📄 `Extract Forecast Output`\n\n**Type:** Structured Output Parser\n**Purpose:**\nParses the GPT response into usable JSON format.\n\n📦 **Expected Output:**\n\n```json\n{\n \"forecast\": {\n \"June\": \"$15,000.00\",\n \"July\": \"$16,500.00\",\n \"August\": \"$17,200.00\"\n },\n \"trend\": \"Increasing\",\n \"confidence\": \"High\",\n \"insights\": \"Sales show strong momentum...\"\n}\n```\n\n---\n\n### 🌲 `Store Context in Pinecone` *(optional)*\n\n**Type:** Vector Store\n**Purpose:**\nIndexes past data for retrieval-based prompting (RAG). Useful for long-term memory.\n\n---\n\n### 🧬 `Generate Embeddings` *(optional)*\n\n**Type:** Embeddings Node\n**Purpose:**\nConverts text into vector format before inserting into Pinecone."
},
"typeVersion": 1
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"content": "## 3️⃣ **📦 Storage & Reporting**\n\n**Nodes:**\n\n* 🟩 `Save Forecast to Supabase`\n* 📊 `Log Forecast in Google Sheets`\n\n---\n\n### 🟩 `Save Forecast to Supabase`\n\n**Type:** Supabase Node\n**Purpose:**\nStores all forecast results for analytics, versioning, or historical comparisons.\n\n🛢️ **Table:** `forecasts`\n🧾 **Columns Example:**\n\n| timestamp | raw\\_data | forecast\\_data |\n| ---------- | --------- | -------------- |\n| 2025-05-29 | {...} | {...} |\n\n---\n\n### 📊 `Log Forecast in Google Sheets`\n\n**Type:** Google Sheets Node\n**Purpose:**\nPushes structured data into a visual format for reporting dashboards or human review.\n\n📋 **Column Format:**\n\n| Date | Forecast (USD) | Trend | Confidence | Insights |\n| ---------- | -------------- | ---------- | ---------- | -------------------------- |\n| 2025-05-29 | \\$15,000.00 | Increasing | High | Sales rising at 10% weekly |\n\n---\n\n## ✅ Summary Flow\n\n```txt\n🔁 Sales Data (Stripe) \n → 🧠 Forecast Agent (OpenAI) \n → 📦 Stored in Supabase \n → 📊 Reported in Google Sheets"
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"content": "=======================================\n WORKFLOW ASSISTANCE\n=======================================\nFor any questions or support, please contact:\n Yaron@nofluff.online\n\nExplore more tips and tutorials here:\n - YouTube: https://www.youtube.com/@YaronBeen/videos\n - LinkedIn: https://www.linkedin.com/in/yaronbeen/\n=======================================\n"
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"content": "# 📊 CFO Forecasting Agent – Workflow Documentation\n\n---\n\n## 1️⃣ **🔁 Data Retrieval & Preprocessing**\n\n**Nodes:**\n\n* 🕒 `Run Daily Forecast`\n* 🟦 `Fetch Stripe Charges`\n* 🧩 `Summarize Daily Sales`\n* ✏️ `Prepare Forecast Prompt`\n\n---\n\n### 🕒 `Run Daily Forecast`\n\n**Type:** Cron Trigger\n**Purpose:**\nAutomatically runs the workflow every day to keep forecasts updated with the latest sales data.\n\n🔧 **Configuration:**\n\n* Schedule: Daily at 6 AM UTC (or as needed)\n\n---\n\n### 🟦 `Fetch Stripe Charges`\n\n**Type:** Stripe Node\n**Purpose:**\nRetrieves all successful transactions from Stripe in a defined timeframe.\n\n📥 **Details:**\n\n* Resource: `Charges`\n* Operation: `Get Many`\n* Filters:\n\n * `created[gte]` (e.g. last 30 days)\n * `status: succeeded`\n* Expansion (optional): `data.customer` for customer context\n\n✅ **Output:** Raw Stripe sales data with timestamps and amounts\n\n---\n\n### 🧩 `Summarize Daily Sales`\n\n**Type:** Code Node\n**Purpose:**\nProcesses Stripe charges and summarizes revenue per day.\n\n🧠 **Logic:**\n\n* Converts Unix timestamps to `YYYY-MM-DD`\n* Aggregates total revenue per day\n* Converts cents to dollars\n\n📦 **Output Sample:**\n\n```json\n{\n \"2025-05-01\": 1245.50,\n \"2025-05-02\": 980.00\n}\n```\n\n---\n\n### ✏️ `Prepare Forecast Prompt`\n\n**Type:** Edit Fields / Function\n**Purpose:**\nFormats the summary into a natural language prompt for OpenAI.\n\n🧠 **Example Prompt:**\n\n```txt\nGiven the following sales data:\n{ \"2025-05-01\": 1245.50, ... }\n\nPredict trends and forecast sales for the next 3 months.\n```\n\n🧾 **Output:** `prompt` (String) → sent to the AI Agent\n\n---\n\n## 2️⃣ **🤖 AI Agent Forecasting**\n\n**Nodes:**\n\n* 🤖 `Forecast with OpenAI Agent`\n* 🧠 `OpenAI GPT-4 Model`\n* 📄 `Extract Forecast Output`\n* 🌲 `Store Context in Pinecone` *(Optional)*\n* 🧬 `Generate Embeddings` *(Optional)*\n\n---\n\n### 🤖 `Forecast with OpenAI Agent`\n\n**Type:** Tools Agent\n**Purpose:**\nActs as an intelligent agent that reads the sales summary and responds with forecasts and reasoning.\n\n🧠 **Prompt Input:**\nPassed from `Prepare Forecast Prompt`\n\n💬 **Uses:**\n\n* Model: `GPT-4`\n* Output Parser: Structured JSON format\n\n📈 **Forecast Intent:**\nPredicts next 3 months, identifies trends, and gives a confidence level\n\n---\n\n### 🧠 `OpenAI GPT-4 Model`\n\n**Type:** OpenAI Node\n**Purpose:**\nHandles the natural language generation based on the supplied prompt.\n\n🧾 **Configuration:**\n\n* Model: `gpt-4` or `gpt-4-turbo`\n* Temperature: `0.2` (more deterministic)\n* Max Tokens: `1000`\n\n---\n\n### 📄 `Extract Forecast Output`\n\n**Type:** Structured Output Parser\n**Purpose:**\nParses the GPT response into usable JSON format.\n\n📦 **Expected Output:**\n\n```json\n{\n \"forecast\": {\n \"June\": \"$15,000.00\",\n \"July\": \"$16,500.00\",\n \"August\": \"$17,200.00\"\n },\n \"trend\": \"Increasing\",\n \"confidence\": \"High\",\n \"insights\": \"Sales show strong momentum...\"\n}\n```\n\n---\n\n### 🌲 `Store Context in Pinecone` *(optional)*\n\n**Type:** Vector Store\n**Purpose:**\nIndexes past data for retrieval-based prompting (RAG). Useful for long-term memory.\n\n---\n\n### 🧬 `Generate Embeddings` *(optional)*\n\n**Type:** Embeddings Node\n**Purpose:**\nConverts text into vector format before inserting into Pinecone.\n\n---\n\n## 3️⃣ **📦 Storage & Reporting**\n\n**Nodes:**\n\n* 🟩 `Save Forecast to Supabase`\n* 📊 `Log Forecast in Google Sheets`\n\n---\n\n### 🟩 `Save Forecast to Supabase`\n\n**Type:** Supabase Node\n**Purpose:**\nStores all forecast results for analytics, versioning, or historical comparisons.\n\n🛢️ **Table:** `forecasts`\n🧾 **Columns Example:**\n\n| timestamp | raw\\_data | forecast\\_data |\n| ---------- | --------- | -------------- |\n| 2025-05-29 | {...} | {...} |\n\n---\n\n### 📊 `Log Forecast in Google Sheets`\n\n**Type:** Google Sheets Node\n**Purpose:**\nPushes structured data into a visual format for reporting dashboards or human review.\n\n📋 **Column Format:**\n\n| Date | Forecast (USD) | Trend | Confidence | Insights |\n| ---------- | -------------- | ---------- | ---------- | -------------------------- |\n| 2025-05-29 | \\$15,000.00 | Increasing | High | Sales rising at 10% weekly |\n\n---\n\n## ✅ Summary Flow\n\n```txt\n🔁 Sales Data (Stripe) \n → 🧠 Forecast Agent (OpenAI) \n → 📦 Stored in Supabase \n → 📊 Reported in Google Sheets\n```\n"
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{
"json": {
"id": "ch_1NxXy2LzPp3ZhRAbCD123456",
"amount": 3500,
"object": "charge",
"status": "succeeded",
"created": 1716940800,
"currency": "usd",
"customer": {
"id": "cus_N8U1xX5TVgB1vW",
"name": "Jane Doe",
"email": "jane.doe@example.com"
},
"description": "Pro plan subscription",
"amount_captured": 3500,
"amount_refunded": 0,
"payment_method_details": {
"card": {
"brand": "visa",
"last4": "4242"
},
"type": "card"
}
}
},
{
"json": {
"id": "ch_1NxXy3LzPp3ZhRAbCD654321",
"amount": 1299,
"object": "charge",
"status": "succeeded",
"created": 1717027200,
"currency": "usd",
"customer": {
"id": "cus_N8U9YT5TWzA7LM",
"name": "John Smith",
"email": "john.smith@example.com"
},
"description": "Add-on purchase",
"amount_captured": 1299,
"amount_refunded": 0,
"payment_method_details": {
"card": {
"brand": "mastercard",
"last4": "5555"
},
"type": "card"
}
}
}
]
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[
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},
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[
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},
"bb75cafb-9dad-4952-89af-0658c4d88aa4": {
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[
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}
}자주 묻는 질문
이 워크플로우를 어떻게 사용하나요?
위의 JSON 구성 코드를 복사하여 n8n 인스턴스에서 새 워크플로우를 생성하고 "JSON에서 가져오기"를 선택한 후, 구성을 붙여넣고 필요에 따라 인증 설정을 수정하세요.
이 워크플로우는 어떤 시나리오에 적합한가요?
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저자
Yaron Been
@yaron-nofluffBuilding AI Agents and Automations | Growth Marketer | Entrepreneur | Book Author & Podcast Host If you need any help with Automations, feel free to reach out via linkedin: https://www.linkedin.com/in/yaronbeen/ And check out my Youtube channel: https://www.youtube.com/@YaronBeen/videos
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