This article provides a complete, practical walkthrough of the Wait Code Send Webhook n8n agent. It connects HTTP Request, Webhook across approximately 1 node(s). Expect a Intermediate setup in 15-45 minutes. One‑time purchase: €29.

What This Agent Does

This agent orchestrates a reliable automation between HTTP Request, Webhook, handling triggers, data enrichment, and delivery with guardrails for errors and rate limits.

It streamlines multi‑step processes that would otherwise require manual exports, spreadsheet cleanup, and repeated API requests. By centralizing logic in n8n, it reduces context switching, lowers error rates, and ensures consistent results across teams.

Typical outcomes include faster lead handoffs, automated notifications, accurate data synchronization, and better visibility via execution logs and optional Slack/Email alerts.

How It Works

The workflow uses standard n8n building blocks like Webhook or Schedule triggers, HTTP Request for API calls, and control nodes (IF, Merge, Set) to validate inputs, branch on conditions, and format outputs. Retries and timeouts improve resilience, while credentials keep secrets safe.

Third‑Party Integrations

• HTTP Request
• Webhook

Import and Use in n8n

1. Open n8n and create a new workflow or collection.
2. Choose Import from File or Paste JSON.
3. Paste the JSON below, then click Import.
4. Show n8n JSON

   Certainly! Below is a short article detailing the functionality and benefits of the n8n workflow you provided, including a title, meta description, keywords, and a list of third-party APIs used.
   
   —
   
   📝 Title:
   Automated Invoice Extraction from Outlook Emails Using n8n and Google Gemini
   
   📋 Meta Description:
   Learn how to automate invoice processing from Microsoft Outlook emails using n8n. This workflow uses AI via Google Gemini to classify and extract PDF invoice data, then uploads the results to Microsoft Excel.
   
   🔑 Keywords:
   n8n invoice automation, Outlook email parsing, Google Gemini API, PDF invoice extraction, AI invoice classification, Gemini OCR, Gemini Vision, Microsoft Excel automation, accounts receivable automation, invoice data to Excel
   
   🔗 Third-Party APIs Used:
   
   1. Microsoft Outlook API – For fetching and downloading recent emails and attachments.
   2. Google Gemini (PaLM) API – To classify and extract invoice data using document understanding and vision-based OCR.
   3. Microsoft Excel API – To append extracted invoice data into an Excel workbook stored in Microsoft 365.
   
   —
   
   ⚙️ Article:
   
   Automating Invoice Capture from Email with n8n and Google Gemini
   
   Manually processing invoice emails can be a tedious and error-prone task for accounts receivable teams. Thankfully, with the help of n8n, Microsoft Outlook, and Google Gemini, it's now possible to automate the end-to-end pipeline—from email ingestion to data entry in Excel.
   
   This article explains how a low-code integration using n8n significantly reduces the time and effort required to extract and process invoices. The workflow described uses advanced AI to reason about the content of emails and invoice attachments, streamlining financial workflows efficiently and accurately.
   
   📥 Step 1: Fetching Invoice Emails from Microsoft Outlook
   
   The workflow begins with a Schedule Trigger node configured to run every hour. This node activates the Microsoft Outlook node, which fetches all incoming messages within the last hour from a predefined folder. Only messages that contain attachments are retrieved, which optimizes the workflow for efficiency and reduces API usage.
   
   To determine whether an email is actually an invoice from a supplier, the content of each email is parsed and classified using the n8n LangChain Text Classifier node, powered by Google Gemini. Unlike simple keyword-based filtering, this method uses large language models (LLMs) to determine the intent and context of the message, classifying it as an invoice or otherwise.
   
   📎 Step 2: Downloading and Filtering Relevant Attachments
   
   For emails identified as supplier invoices, their attachments are downloaded using the Outlook “downloadAttachments” option. All attachments are split using a custom code node to isolate individual binary files for classification.
   
   Following this, each document is passed to Google Gemini for classification. Here, Gemini checks if the document is:
   - An invoice,
   - Not sent by your company,
   - Issued to your company.
   
   This step verifies invoice authenticity via AI, leveraging Gemini’s support for analyzing PDFs directly—a unique benefit among LLM providers.
   
   📄 Step 3: Extracting Invoice Data with Gemini Vision
   
   Once invoices are validated, the documents are passed again to Gemini’s API, this time for content extraction via a structured OCR process. A custom HTTP Request node is used to interact with Gemini using its “generationConfig” parameters to request a JSON object with all necessary invoice fields.
   
   These extracted fields can include:
   - Invoice number and date
   - Supplier name and contact details
   - Booking and guest information
   - Line items and services
   - Invoice total and due amount
   
   In case something goes wrong during extraction, a fallback pathway ensures that the system skips unprocessable documents gracefully without halting the entire process.
   
   📊 Step 4: Output to Microsoft Excel
   
   Lastly, the structured data is appended to a pre-defined Excel workbook in Microsoft 365 using the Excel node in n8n. This could pave the way for further integrations like syncing with an ERP system, triggering reconciliation scripts, or feeding into automated payment workflows.
   
   By appending rows directly to Excel, this setup provides a human-readable audit trail and makes it easy for finance teams to verify the data before pushing it into the accounting system.
   
   🤖 Why This Matters
   
   This workflow offers significant cost and time savings for finance departments. Instead of spending hours each week manually opening emails, reading PDFs, and entering data into spreadsheets, this setup does it all automatically.
   
   According to user reports, such automation can save upwards of hundreds of hours per year in data entry time alone—freeing up staff for more strategic, high-value tasks.
   
   ✅ How to Get Started
   
   To use this workflow:
   - Connect your Microsoft Outlook and Excel accounts in n8n.
   - Set up your Google Gemini (PaLM API) credentials.
   - Point the folder to your designated inbox (e.g., finance@app.com).
   - Customize Excel schema mapping as required.
   
   👉 Pro tip: If you're receiving forwarded invoices or duplicates, use the "Remove Duplicates" node to prevent processing the same file multiple times.
   
   💬 Conclusion
   
   Whether you're a solo entrepreneur or a finance team manager, automating invoice intake and processing is one of the quickest wins in workflow automation. This n8n setup elegantly combines structured email parsing, document classification with LLMs, and AI-powered data extraction—all without writing lengthy scripts or deploying servers.
   
   Need help getting started? Join the official Discord or community forums to ask questions and learn from others automating finance team processes.
   
   📎 Resources:
   - n8n Documentation: https://docs.n8n.io/
   - Google Gemini API: https://ai.google.dev
   - Microsoft Excel API Docs: https://learn.microsoft.com/en-us/graph/api/resources/excel
   
   —
   
   Let me know if you’d like this article adapted into a blog post, tutorial format, or video script!

5. Set credentials for each API node (keys, OAuth) in Credentials.
6. Run a test via Execute Workflow. Inspect Run Data, then adjust parameters.
7. Enable the workflow to run on schedule, webhook, or triggers as configured.

Tips: keep secrets in credentials, add retries and timeouts on HTTP nodes, implement error notifications, and paginate large API fetches.

Validation: use IF/Code nodes to sanitize inputs and guard against empty payloads.

Why Automate This with AI Agents

AI‑assisted automations offload repetitive, error‑prone tasks to a predictable workflow. Instead of manual copy‑paste and ad‑hoc scripts, your team gets a governed pipeline with versioned state, auditability, and observable runs.

n8n’s node graph makes data flow transparent while AI‑powered enrichment (classification, extraction, summarization) boosts throughput and consistency. Teams reclaim time, reduce operational costs, and standardize best practices without sacrificing flexibility.

Compared to one‑off integrations, an AI agent is easier to extend: swap APIs, add filters, or bolt on notifications without rewriting everything. You get reliability, control, and a faster path from idea to production.

Best Practices

• Credentials: restrict scopes and rotate tokens regularly.
• Resilience: configure retries, timeouts, and backoff for API nodes.
• Data Quality: validate inputs; normalize fields early to reduce downstream branching.
• Performance: batch records and paginate for large datasets.
• Observability: add failure alerts (Email/Slack) and persistent logs for auditing.
• Security: avoid sensitive data in logs; use environment variables and n8n credentials.

FAQs

Can I swap integrations later? Yes. Replace or add nodes and re‑map fields without rebuilding the whole flow.

How do I monitor failures? Use Execution logs and add notifications on the Error Trigger path.

Does it scale? Use queues, batching, and sub‑workflows to split responsibilities and control load.

Is my data safe? Keep secrets in Credentials, restrict token scopes, and review access logs.