This article provides a complete, practical walkthrough of the Code Editimage Import 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

   Title:
   Transform Bank Statement PDFs into Structured Data Using n8n and Google Gemini
   
   Meta Description:
   Learn how to automate the extraction of deposit transactions from scanned or digital bank statements using Vision Language Models (VLMs) via a custom n8n workflow integrated with Google Gemini. Convert documents into structured markdown and extract tabular data effortlessly.
   
   Keywords:
   n8n workflow, Google Gemini, PDF to Markdown, Vision Language Model, bank statement data extraction, scanned PDFs, AI document parsing, Google Drive API, Stirling PDF API, GPT4o, Claude Opus, information extractor, markdown transcription, document automation, AI OCR replacement, LLM data processing, LangChain
   
   Third-Party APIs & Services Used:
   
   1. Google Drive API (OAuth2)
      Used to download the example bank statement PDF file from a specified Google Drive account.
   
   2. Stirling PDF (Web API)
      A public or self-hosted API service used to convert each page of the PDF document into individual images (JPEG format) at 300 DPI resolution.
   
   3. Google Gemini (Gemini-1.5 Pro via PaLM API)
      An advanced Vision Language Model (VLM) used to transcribe images of statement pages into faithful markdown text, preserving layout and content structure.
   
   4. LangChain (LLM and information extractor nodes via n8n)
      Used to interact with the Google Gemini model and to extract structured data—in this case, all deposit transactions—from the markdown transcript produced.
   
   —
   
   Article:
   
   Automated Bank Statement Parsing with n8n and Google Gemini: From Scanned PDFs to Structured Data
   
   In the ever-growing universe of automation, combining visual AI capabilities with powerful no-code or low-code tools like n8n opens up fascinating new possibilities. One such breakthrough is transforming a bank statement—scanned or digital—into structured data like markdown tables and transaction lists.
   
   If you’ve ever manually sifted through PDF bank statements to extract values like deposits, you’ll love this AI-powered n8n workflow. It elegantly combines cloud storage, visual AI processing, and natural language understanding to completely automate the task—while still preserving high accuracy and structure. Let’s walk through how it works.
   
   Step 1: Download a Bank Statement from Google Drive
   
   The workflow begins with a manual trigger for testing, but could easily be reconfigured for automation using email, webhook, or cloud storage triggers. In this example, our PDF bank statement is fetched directly using the Google Drive API. This statement, which could be a downloadable PDF or a scanned copy, resides in a publicly accessible Drive link, though authentication is still required for access.
   
   If you’re following along, make sure to use the provided example file or swap in your own.
   
   Step 2: Convert the PDF into Images Using Stirling PDF
   
   Rather than relying on traditional OCR (which often fails with complex PDF layouts), we convert each page of the PDF into separate high-resolution images using the Stirling PDF API. The public Stirling PDF service wraps this functionality in a simple REST interface, but privacy-conscious users can self-host this open-source tool as well.
   
   The conversion returns a zip file, which is uncompressed inside the workflow, separating out each image (one per page) for the next stage.
   
   Step 3: Preprocess and Transcribe Each Image with Google Gemini
   
   To prepare the page images for AI processing, they are first resized to 75% of their initial dimensions. This masterstroke ensures quicker processing and helps avoid token/timeout issues that large images can cause when run through multimodal models.
   
   Each resized page image is then passed into Google Gemini (model: gemini-1.5-pro-latest) via the LangChain-powered LLM Chat Model node. Unlike traditional OCR, Gemini is a Vision Language Model (VLM). It doesn’t just “see”—it understands the layout, structure, and intent of the document.
   
   Instead of spitting out raw text, it transcribes what it “sees” into structured markdown. This includes:
   - Headers and subheaders
   - Tables represented in markdown table format
   - Concatenated descriptions in table cells
   - Placeholders for images (e.g., “<scanned image of check>”)
   
   Each markdown page is returned and aggregated for the final stage of processing.
   
   Step 4: Extract Relevant Data Using an Information Extractor
   
   With all markdown-transcribed content collected and combined, we turn to the final task: extracting just the deposit transactions from the document.
   
   In another LangChain-powered node—this time an information extractor—we specify a system prompt to help the model locate and return only the deposit entries from embedded tables. These entries are returned as structured JSON objects, each containing:
   - Date
   - Description
   - Amount
   
   The schema ensures consistent validation for downstream use, whether for financial analysis, reporting, or archival purposes.
   
   Why This Workflow Matters
   
   This workflow showcases the real-world value of combining cloud storage, image manipulation, document conversion, and multimodal AI—all without writing hundreds of lines of code. With just a few nodes and Google Gemini, what used to take hours can now be done in seconds.
   
   Key Advantages:
   - Works with both downloaded and scanned PDFs
   - Automatically transcribes visual elements into markdown format
   - Extracts usable structured data (i.e., deposit table rows)
   - Avoids the common limitations of OCR
   - Easily extendable to retrieve withdrawals, balances, or apply filters
   
   Conclusion
   
   In a future that demands digital agility, intelligent automation is no longer a luxury—it’s a necessity. By marrying n8n’s flexibility with the prowess of Vision Language Models like Google Gemini, we gain powerful document understanding capabilities previously reserved for the most advanced AI systems.
   
   Whether you're a developer looking to streamline financial workflows, or a business automating back-office processes, this clever combination of tools could be your new best asset.
   
   Start parsing smarter, not harder.
   
   —
   
   Need Help Getting Started?
   
   Join the vibrant n8n community on Discord or the Forum to get support and share your automation creations.
   
   And remember: If you’re working with sensitive data, always self-host or use encrypted, privacy-safe services instead of public endpoints like Stirling PDF.
   
   Happy automating!

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.