{
  "workflow": {
    "id": 4576,
    "name": "Automated expense approval system with GPT-4, Airtable & Pinecone Vector DB",
    "views": 403,
    "recentViews": 0,
    "totalViews": 403,
    "createdAt": "2025-06-02T08:31:30.713Z",
    "description": "Automate expense reviews with AI-powered CFO-level analysis. This workflow monitors Airtable expense submissions, uses GPT-4 to analyze expenses like an experienced CFO, flags suspicious expenses with detailed reasoning, and maintains comprehensive audit trails in Pinecone vector database.\n\n## 🚀 What It Does\n- **Smart Monitoring**: Watches Airtable for new expense submissions\n- **AI CFO Analysis**: GPT-4 applies financial expertise to review amounts, categories, and descriptions\n- **Intelligent Flagging**: Automatically identifies policy violations and suspicious patterns\n- **Audit Trail**: Stores all decisions in Pinecone for compliance and searchability\n- **Auto Updates**: Updates Airtable records with AI decisions and detailed reasoning\n\n## 🎯 Perfect For\n- Finance teams needing intelligent expense oversight\n- CFOs wanting to automate expense policy enforcement\n- Growing companies scaling expense management\n- Businesses requiring compliance documentation\n\n## ⚙️ Key Benefits\n✅ 99% faster expense processing vs manual review  \n✅ CFO-level intelligence applied to every expense  \n✅ Complete audit trail for compliance  \n✅ Real-time fraud detection and policy enforcement  \n✅ Detailed explanations for every decision  \n\n## 🔧 What You Need\n- Airtable base with expense data (template included)\n- OpenAI API for GPT-4 analysis\n- Pinecone account for audit trail storage\n- Basic expense submission process\n\n## 📊 Sample Results\nInput: $4,500 business class flight to Tokyo  \nAI Decision: \"Flagged - Amount exceeds typical travel thresholds. Requires verification against travel policies and client justification for premium travel.\"\n\n## 🛠️ Setup & Support\n\n**Quick Setup**: Deploy in 60 minutes with included templates and documentation\n\n\nYouTube: [https://www.youtube.com/@YaronBeen/videos](https://www.youtube.com/@YaronBeen/videos)\n\n**💼 Expert Support**  \nLinkedIn: [https://www.linkedin.com/in/yaronbeen/](https://www.linkedin.com/in/yaronbeen/)\n\n**📧 Direct Help**  \nEmail: Yaron@nofluff.online\n\nTransform expense management from manual bottleneck to intelligent automation. Let AI handle policy compliance while your finance team focuses on strategy.",
    "workflow": {
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      "meta": {
        "instanceId": "84ad02d6104594179f43f1ce9cfe3a81637b2faedb57dafcb9e649b7542988db",
        "templateCredsSetupCompleted": true
      },
      "name": "CFO Expense Reviewer Agent",
      "tags": [],
      "nodes": [
        {
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          "name": "Watch New Expense Requests",
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          "position": [
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              "__rl": true,
              "mode": "url",
              "value": "https://airtable.com/appjaqV0O7FkXT2qj/shrst7GnlbzMDz4te"
            },
            "tableId": {
              "__rl": true,
              "mode": "url",
              "value": "https://airtable.com/appjaqV0O7FkXT2qj/tblTAvRqVFOo5AVDF/viwEp0ssaidZOo4nl?blocks=hide"
            },
            "pollTimes": {
              "item": [
                {
                  "mode": "everyHour"
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            "authentication": "airtableTokenApi",
            "additionalFields": {}
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              "id": "credential-id",
              "name": "airtableTokenApi Credential"
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          "name": "CFO Expense Review Agent",
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          "parameters": {
            "text": "=An employee submitted an expense:\nAmount: ${{ $json.fields.Amount }}\nSubmitted by: {{ $json.fields['Submitted By'] }}\nCategory: {{ $json.fields.Category }}\nDescription: {{ $json.fields.Description }}\nDate Submitted: {{ $json.fields['Date Submitted'] }}\nStatus: {{ $json.fields.Status }}",
            "options": {
              "systemMessage": "You are a CFO expense analysis agent. Flag suspicious expenses with the reason. When you answer try to give answer with all the given details"
            },
            "promptType": "define",
            "hasOutputParser": true
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            "model": {
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              "mode": "list",
              "value": "gpt-4o-mini"
            },
            "options": {}
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          "credentials": {
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              "name": "openAiApi Credential"
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            "jsonSchemaExample": "{\n  \"amount\": 4500,\n  \"submitted_by\": \"Alice\",\n  \"category\": \"Travel\",\n  \"description\": \"Business class flight to Tokyo\",\n  \"date_submitted\": \"2025-05-29\",\n  \"status\": \"Pending\",\n  \"decision\": \"Flagged\",\n  \"reason\": \"The amount of $4500 for a business class flight appears unusually high and requires verification against standard travel policies and previous similar expenses. Additionally, the submission date is in the future (2025), which raises concerns about the legitimacy of the expense. It is advisable to confirm the travel plans and the necessity of the business class flight for this trip.\"\n}\n"
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              "pineconeNamespace": "={{ $json.output.decision }}"
            },
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              "cachedResultName": "new"
            }
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              "mode": "list",
              "value": "appjaqV0O7FkXT2qj",
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              "value": {
                "id": "={{ $('Watch New Expense Requests').item.json.id }}",
                "Amount": "={{ $('CFO Expense Review Agent').item.json.output.amount }}",
                "Reason": "={{ $('CFO Expense Review Agent').item.json.output.reason }}",
                "Status": "=completed",
                "Category": "={{ $('CFO Expense Review Agent').item.json.output.category }}",
                "decision": "={{ $('CFO Expense Review Agent').item.json.output.decision }}",
                "Description": "={{ $('CFO Expense Review Agent').item.json.output.description }}",
                "Submitted By": "={{ $('CFO Expense Review Agent').item.json.output.submitted_by }}"
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                      "name": "Self-cleaning, keeps drinks cold for 24 hrs, BPA-free",
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            "options": {},
            "operation": "update"
          },
          "credentials": {
            "airtableTokenApi": {
              "id": "credential-id",
              "name": "airtableTokenApi Credential"
            }
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          "typeVersion": 2.1
        },
        {
          "id": "a45e7430-d205-4930-9b15-b52e51139d58",
          "name": "Sticky Note",
          "type": "n8n-nodes-base.stickyNote",
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          "parameters": {
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            "height": 680,
            "content": "## 🔁 **Section 1: Intake – Monitoring Expense Requests**\n\n### 1. **Watch New Expense Requests**\n\n**(Airtable Trigger)**\n\n* **Purpose:** Continuously monitors the Airtable `Expenses` table for new or updated entries with `Status = Pending`.\n* **What it does:** Triggers the entire workflow when a new expense is submitted or an existing one is updated for review.\n\n> 💡 Ensures real-time detection of incoming requests that need analysis."
          },
          "typeVersion": 1
        },
        {
          "id": "6d6229ee-a86b-442f-ab8e-25d44b9bbe24",
          "name": "Sticky Note1",
          "type": "n8n-nodes-base.stickyNote",
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          "parameters": {
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            "width": 500,
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            "content": "🧠 Section 2: AI Analysis – CFO Reasoning Engine\n2. CFO Expense Review Agent\n(AI Agent - Tools Agent)\n\nPurpose: Acts as a virtual CFO. It receives the expense data and reasons through the submission using a language model.\n\nConnected To: OpenAI GPT-4 Model and Structured Output Parser\n\n3. OpenAI GPT-4 Model\n(OpenAI Chat Model)\n\nPurpose: Powers the logic behind the CFO's decision-making.\n\nPrompt Format: Given structured context about the expense, it determines whether it should be flagged and explains why.\n\n4. Parse CFO Agent Response\n(Structured Output Parser)\n\nPurpose: Transforms the unstructured response from GPT into a clean JSON object.\n\nOutput: Fields like decision, reason, amount, submitted_by, category, etc., which can be used for downstream processing.\n\n🧩 This section gives intelligence to the system, allowing it to make smart and explainable decisions."
          },
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          "position": [
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            "content": "🧬 Section 3: Audit Trail – Embedding & Storage\n5. Prepare Data for Pinecone\n(Default Data Loader)\n\nPurpose: Loads the document (usually the reasoning or description) for embedding.\n\nInput: Reasoning text or metadata generated by the AI agent.\n\n6. Split Reasoning Text\n(Recursive Character Text Splitter)\n\nPurpose: Splits long reasoning content into smaller chunks (if needed) to fit OpenAI embedding limits.\n\n7. Generate Embeddings\n(Embeddings OpenAI)\n\nPurpose: Converts the textual reasoning into a vector representation using OpenAI's embedding model.\n\n8. Store Decision in Pinecone\n(Pinecone Vector Store)\n\nPurpose: Saves the embedded vector along with metadata like decision, amount, and reason.\n\nGoal: Creates a searchable, auditable archive of past decisions for future reference and pattern learning.\n\n🧾 This section builds a scalable memory system for compliance and insights over time."
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            "content": "✅ Section 4: Output – Updating Records\n9. Update Airtable Record\n(Airtable Update Node)\n\nPurpose: Writes the final decision (Approved or Flagged) and the reasoning back to the original Airtable record.\n\nFields Updated: Status, Reason, and optionally an ReviewedAt timestamp.\n\n📥 Ensures that Airtable remains the source of truth, visibly updated with the CFO agent’s input."
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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 Expense Approval Workflow Overview**\n\nThis workflow automates the process of monitoring, analyzing, and auditing expense requests using Airtable, OpenAI, and Pinecone. It is organized into the following four sections:\n\n---\n\n## 🔁 **Section 1: Intake – Monitoring Expense Requests**\n\n### 1. **Watch New Expense Requests**\n\n**(Airtable Trigger)**\n\n* **Purpose:** Continuously monitors the Airtable `Expenses` table for new or updated entries with `Status = Pending`.\n* **What it does:** Triggers the entire workflow when a new expense is submitted or an existing one is updated for review.\n\n> 💡 Ensures real-time detection of incoming requests that need analysis.\n\n---\n\n## 🧠 **Section 2: AI Analysis – CFO Reasoning Engine**\n\n### 2. **CFO Expense Review Agent**\n\n**(AI Agent - Tools Agent)**\n\n* **Purpose:** Acts as a virtual CFO. It receives the expense data and reasons through the submission using a language model.\n* **Connected To:** OpenAI GPT-4 Model and Structured Output Parser\n\n### 3. **OpenAI GPT-4 Model**\n\n**(OpenAI Chat Model)**\n\n* **Purpose:** Powers the logic behind the CFO's decision-making.\n* **Prompt Format:** Given structured context about the expense, it determines whether it should be flagged and explains why.\n\n### 4. **Parse CFO Agent Response**\n\n**(Structured Output Parser)**\n\n* **Purpose:** Transforms the unstructured response from GPT into a clean JSON object.\n* **Output:** Fields like `decision`, `reason`, `amount`, `submitted_by`, `category`, etc., which can be used for downstream processing.\n\n> 🧩 This section gives intelligence to the system, allowing it to make smart and explainable decisions.\n\n---\n\n## 🧬 **Section 3: Audit Trail – Embedding & Storage**\n\n### 5. **Prepare Data for Pinecone**\n\n**(Default Data Loader)**\n\n* **Purpose:** Loads the document (usually the reasoning or description) for embedding.\n* **Input:** Reasoning text or metadata generated by the AI agent.\n\n### 6. **Split Reasoning Text**\n\n**(Recursive Character Text Splitter)**\n\n* **Purpose:** Splits long reasoning content into smaller chunks (if needed) to fit OpenAI embedding limits.\n\n### 7. **Generate Embeddings**\n\n**(Embeddings OpenAI)**\n\n* **Purpose:** Converts the textual reasoning into a vector representation using OpenAI's embedding model.\n\n### 8. **Store Decision in Pinecone**\n\n**(Pinecone Vector Store)**\n\n* **Purpose:** Saves the embedded vector along with metadata like decision, amount, and reason.\n* **Goal:** Creates a searchable, auditable archive of past decisions for future reference and pattern learning.\n\n> 🧾 This section builds a scalable **memory system** for compliance and insights over time.\n\n---\n\n## ✅ **Section 4: Output – Updating Records**\n\n### 9. **Update Airtable Record**\n\n**(Airtable Update Node)**\n\n* **Purpose:** Writes the final decision (`Approved` or `Flagged`) and the reasoning back to the original Airtable record.\n* **Fields Updated:** `Status`, `Reason`, and optionally an `ReviewedAt` timestamp.\n\n> 📥 Ensures that Airtable remains the **source of truth**, visibly updated with the CFO agent’s input.\n"
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        "Generate Embeddings": {
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        "Store Decision in Pinecone": {
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                "type": "main",
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        "Watch New Expense Requests": {
          "main": [
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    "status": "published",
    "user": {
      "name": "Yaron Been",
      "username": "yaron-nofluff",
      "bio": "Building AI Agents and Automations | Growth Marketer | Entrepreneur | Book Author & Podcast Host\n\nIf you need any help with Automations, feel free to reach out via linkedin:\nhttps://www.linkedin.com/in/yaronbeen/\n\nAnd check out my Youtube channel:\nhttps://www.youtube.com/@YaronBeen/videos",
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