This article provides a complete, practical walkthrough of the Code Editimage Automation Webhook n8n agent. It connects Googledrive 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 Googledrive, 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

• Googledrive

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

   Title:
   Transforming Bank Statements into Structured Data with Vision AI in n8n
   
   Meta Description:
   Learn how to use n8n and vision language models (VLMs) to convert scanned bank statements into structured data like markdown and extract deposits using Google Gemini and Stirling PDF.
   
   Keywords:
   n8n workflow, vision language models, VLM, PDF to markdown, bank statement automation, Google Gemini, Stirling PDF, document AI, data extraction, OCR alternative, Google Drive, AI automation, PDF parsing
   
   Third-Party APIs Used:
   
   1. Google Drive API – to download the example bank statement PDF.
   2. Stirling PDF – for converting bank statement PDFs into individual image files per page.
   3. Google Gemini API (models/gemini-1.5-pro-latest) – for image-to-markdown transcription and intelligent data extraction using a multimodal LLM.
   
   Article:
   
   From Bank Statement to Structured Data in Minutes: Automating Financial Document Parsing Using Vision AI and n8n
   
   In the world of automation, turning unstructured documents into machine-readable data remains one of the most impactful—and challenging—tasks. Traditional OCR systems often fall short, especially when dealing with complex tables, low-quality scans, or inconsistent layouts. Fortunately, with tools like n8n and the rising power of Vision Language Models (VLMs), extracting meaningful content from bank statements has become easier, faster, and far more accurate.
   
   In this article, we walk through a robust n8n-powered automation that downloads a scanned bank statement PDF, transcribes it into structured markdown using Google Gemini’s multimodal AI capabilities, and finally extracts just the deposit table rows for deeper financial analysis. Whether you're dealing with personal finances or back office automation, this workflow is a game-changer.
   
   A Four-Step Transformation Journey
   
   Let’s break down the process that this intelligent n8n workflow follows:
   
   Step 1: Download the Bank Statement
   The workflow begins with a manual trigger and pulls in a sample bank statement PDF directly from Google Drive using the Google Drive node. While this use-case uses a static file for demo purposes, you can easily substitute this with a webhook or other automation to handle real-time uploads.
   
   🔗 Related Docs: [Google Drive Node in n8n](https://docs.n8n.io/integrations/builtin/app-nodes/n8n-nodes-base.googledrive)
   
   Step 2: Convert PDF to Images Using Stirling PDF
   Since most VLMs, like Google Gemini, cannot process PDFs natively, the document must be converted into images. The workflow integrates the free Stirling PDF web service to achieve this. The PDF is transformed into a series of high-resolution JPG images (300 DPI), one for each page. These are then unzipped and prepared for downstream tasks.
   
   Note: For privacy-sensitive data, it's strongly recommended to self-host the Stirling PDF service or replace it with your own PDF-to-image converter.
   
   🔗 Stirling PDF: [stirlingpdf.io](https://stirlingpdf.io) | [GitHub Repository](https://github.com/Stirling-Tools/Stirling-PDF)
   
   Step 3: Use a Vision Model to Transcribe Pages to Markdown
   Once the pages are converted into individual images, they are resized (scaled down by 75%) for optimal performance without losing meaningful resolution. Each page is then individually sent through a Google Gemini multimodal model using n8n’s LLM integrations.
   
   Through a custom system prompt, the VLM is instructed to convert all visible elements—including headings, subheadings, tables, and even disclaimers—into clean, markdown-formatted text. Tables are particularly well-handled thanks to markdown’s structural clarity. Adjacent tables are stacked vertically and any overflow rows are merged, ensuring readability and unity.
   
   This formatting makes it easy to index, search, or extract targeted information later.
   
   🔗 Learn more: [Using n8n LLM Nodes](https://docs.n8n.io/integrations/builtin/cluster-nodes/root-nodes/n8n-nodes-langchain.chainllm)
   
   Step 4: Extract Deposit Entries from Structured Text
   Once the markdown pages are generated, they are aggregated and passed through another LLM node (again using Google Gemini) specifically tasked with identifying and extracting deposit rows from the document.
   
   Using a schema-based system prompt, the AI is directed to locate tables, find rows that include deposit values, and return them as JSON objects according to a pre-defined format: date, description, and amount.
   
   This final structured data can now be stored, reported on, or sent via webhook for further processing.
   
   💡 Example Output:
   
   [
     {
       "date": "2024-05-01",
       "description": "Direct Deposit - Employer Payroll",
       "amount": 3250.00
     },
     ...
   ]
   
   Why This Matters
   
   Unlike OCR, which struggles with layout detection and data relationships, VLMs like Gemini understand document structure and semantics. This leads to:
   
   - Better accuracy with inconsistent scan quality
   - Easier table detection and parsing
   - Richer output formats like markdown or structured JSON
   - Faster time-to-value in automation workflows
   
   Best of all, this n8n template can be adapted for a variety of use-cases—from invoice processing, contract analysis, to digitizing handwritten forms—just by switching the input and tweaking a few prompts.
   
   A Word On Privacy
   
   This example uses a public third-party service (Stirling PDF), so be cautious with sensitive financial documents. If your use involves personally identifiable information (PII) or confidential data, always self-host or use enterprise-compliant versions of these services.
   
   Final Thoughts
   
   n8n’s modular, open-source nature combined with state-of-the-art AI models like Google’s Gemini makes it a powerful platform for document automation. By combining visual processing with intelligent extraction, you can finally unlock the full potential of your scanned PDFs—at a fraction of the cost and time.
   
   Feel free to experiment, swap out Google Gemini for other vision-compatible models like GPT-4o or Claude, and make the workflow your own.
   
   Ready to try it out? You can clone this entire workflow in your own n8n instance and never look at a financial document the same way again.
   
   Happy automating!
   
   —
   
   Want Help? Join the discussion:
   - n8n Discord: https://discord.com/invite/XPKeKXeB7d
   - Community Forum: https://community.n8n.io/
   
   📄 Want to test the workflow? Try this sample PDF:  
   https://drive.google.com/file/d/1wS9U7MQDthj57CvEcqG_Llkr-ek6RqGA/view?usp=sharing

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.