Agente predictivo de CFO

Name: Agente predictivo de CFO
Rating: 4.5 (10 reviews)
Author: Yaron Been

Avanzado

Este es unAI, IT Opsflujo de automatización del dominio deautomatización que contiene 16 nodos.Utiliza principalmente nodos como Set, Code, Stripe, Supabase, GoogleSheets, combinando tecnología de inteligencia artificial para lograr automatización inteligente. Predicción automatizada de ingresos con GPT-4, Google Sheets y datos de Stripe

Requisitos previos

•Clave de API de Stripe
•URL y Clave de API de Supabase
•Credenciales de API de Google Sheets
•Clave de API de OpenAI
•Clave de API de Pinecone

Nodos utilizados (16)

OutputParserStructured

Categoría

Inteligencia Artificial

Operaciones de TI

Vista previa del flujo de trabajo

Visualización de las conexiones entre nodos, con soporte para zoom y panorámica

Analizador de Salida Estructurada

Modelo de Chat OpenAI

Almacén Vectorial Pinecone

Embeddings OpenAI

Ejecutar Predicción Diaria

Obtener Cargos Stripe

Resumir Ventas

Preparar Datos

Agente de Predicción

Guardar Predicción en Supabase

Registrar Predicción en Hojas Google

React Flow

Exportar flujo de trabajo

Copie la siguiente configuración JSON en n8n para importar y usar este flujo de trabajo

{
  "id": "mHSoMMyypmRzfVZn",
  "meta": {
    "instanceId": "84ad02d6104594179f43f1ce9cfe3a81637b2faedb57dafcb9e649b7542988db"
  },
  "name": "CFO forecasting Agent",
  "tags": [],
  "nodes": [
    {
      "id": "b946638e-ba68-4d73-816e-00f3a63d138f",
      "name": "Analizador de Salida Estructurada",
      "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": "Modelo de Chat OpenAI",
      "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": "Almacén Vectorial Pinecone",
      "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": "Embeddings OpenAI",
      "type": "@n8n/n8n-nodes-langchain.embeddingsOpenAi",
      "position": [
        1140,
        600
      ],
      "parameters": {
        "options": {}
      },
      "credentials": {
        "openAiApi": {
          "id": "wYwTjEv45IzlAOAu",
          "name": "OpenAi account 2"
        }
      },
      "typeVersion": 1.2
    },
    {
      "id": "81de1ecf-75de-44fd-9e62-c381e907c1e1",
      "name": "Ejecutar Predicción Diaria",
      "type": "n8n-nodes-base.scheduleTrigger",
      "position": [
        0,
        0
      ],
      "parameters": {
        "rule": {
          "interval": [
            {
              "triggerAtHour": 9
            }
          ]
        }
      },
      "typeVersion": 1.2
    },
    {
      "id": "9ce2e0e4-8784-4ed9-9499-b5f54241d04e",
      "name": "Obtener Cargos Stripe",
      "type": "n8n-nodes-base.stripe",
      "position": [
        220,
        0
      ],
      "parameters": {
        "resource": "charge",
        "operation": "getAll",
        "returnAll": true
      },
      "typeVersion": 1
    },
    {
      "id": "c115eb0c-6877-46db-bf03-55e48527dbc5",
      "name": "Resumir Ventas",
      "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": "Preparar Datos",
      "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": "Agente de Predicción",
      "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": "Guardar Predicción en Supabase",
      "type": "n8n-nodes-base.supabase",
      "position": [
        2100,
        -160
      ],
      "parameters": {
        "dataToSend": "autoMapInputData"
      },
      "typeVersion": 1
    },
    {
      "id": "9fd6efab-edc6-49be-9516-f2d36e1995b2",
      "name": "Registrar Predicción en Hojas Google",
      "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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      "name": "Nota Adhesiva",
      "type": "n8n-nodes-base.stickyNote",
      "position": [
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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."
      },
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    },
    {
      "id": "8e9b2ea7-58bb-4f62-a69c-eed70032019c",
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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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        "width": 1300,
        "height": 320,
        "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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    {
      "id": "2b9e9108-8084-4e3f-a620-5bee07040bc3",
      "name": "Nota Adhesiva4",
      "type": "n8n-nodes-base.stickyNote",
      "position": [
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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"
      },
      "typeVersion": 1
    }
  ],
  "active": false,
  "pinData": {
    "Fetch Stripe Charges": [
      {
        "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"
          }
        }
      }
    ]
  },
  "settings": {
    "executionOrder": "v1"
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}

Preguntas frecuentes

¿Cómo usar este flujo de trabajo?

Copie el código de configuración JSON de arriba, cree un nuevo flujo de trabajo en su instancia de n8n y seleccione "Importar desde JSON", pegue la configuración y luego modifique la configuración de credenciales según sea necesario.

¿En qué escenarios es adecuado este flujo de trabajo?

Avanzado - Inteligencia Artificial, Operaciones de TI

¿Es de pago?

Este flujo de trabajo es completamente gratuito, puede importarlo y usarlo directamente. Sin embargo, tenga en cuenta que los servicios de terceros utilizados en el flujo de trabajo (como la API de OpenAI) pueden requerir un pago por su cuenta.

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129 NodosKumar Shivam

Soporte

Información del flujo de trabajo

Nivel de dificultad

Avanzado

Número de nodos16

Categoría2

Tipos de nodos12

Descripción de la dificultad

Adecuado para usuarios avanzados, flujos de trabajo complejos con 16+ nodos

Autor

Yaron Been

@yaron-nofluff

Building 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

Enlaces externos

Ver en n8n.io →

Compartir este flujo de trabajo

Agente predictivo de CFO

Nodos utilizados (16)

Categoría

¿Cómo usar este flujo de trabajo?

¿En qué escenarios es adecuado este flujo de trabajo?

¿Es de pago?

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