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Dynamic AI Data Extraction from PDFs with Baserow and n8n

Dynamic AI Data Extraction from PDFs with Baserow and n8n

Tired of manually reading through PDFs just to fill in a few spreadsheet fields? Meet a smarter solution — where AI + automation fills your tables for you using dynamic prompts and uploaded files. This is not just automation — it’s AI PDF data extraction that adapts, understands, and grows with your workflows.

In this guide, you’ll learn how to combine n8n, Baserow, and AI language models (LLMs) to automate structured data extraction from PDFs using field-level prompts. This agentic pattern doesn’t just save time — it unlocks a whole new layer of intelligent, low-code workflows.

What Is AI-Powered Dynamic Data Extraction?

This setup monitors for specific occurrences, like a PDF upload to a Baserow row. The system then handles the document. Data is gathered by this tool and deposited into your database.

How AI PDF data extraction Works (Step-by-Step)

This outlines the operational structure for an automated process, integrating n8n and Baserow:

1. Event Trigger via Webhook

The workflow starts whenever a row is updated or a new field is created/modified in Baserow. These events trigger the automation via a webhook.

Events tracked:

  • row.updated
  • field.created
  • field.updated

This reactive design ensures real-time updates to your table — no polling, no delays.

2. Fetch Schema & Dynamic Prompts

Every field in Baserow can include a description. In this AI PDF data extraction workflow, that becomes the dynamic prompt — instructions for the LLM on what to extract from the uploaded PDF.

For example:

  • Field Name: Invoice Number
  • Description (Prompt): Extract the invoice number from the PDF

This means your table is not just a data holder — it becomes an instruction set for your AI agents.

3. Read and Extract PDF Data

When a row is updated with a new file (usually a PDF), the automation:

  • Downloads the file
  • Extracts raw text using a PDF parser
  • Prepares this context for the AI model

4. LLM Prompt Execution

Here’s where the magic happens: for every field with a prompt, the workflow sends the extracted text + the prompt to an LLM (like GPT-4) via a chainLlm node in n8n.

Each field acts as a dynamic mini-agent asking a specific question about the PDF.

Example:

<file>

…extracted PDF text…

</file>

Data to extract: Total contract value in USD

Output format: number

The LLM responds with a concise answer (or returns n/a if it can’t find it), which is then sent back into Baserow.

5. Update the Baserow Table Automatically

Once the values are generated, the AI PDF data extraction workflow:

  • Matches them to the correct fields
  • Updates the corresponding row
  • Moves on to the next batch

Thanks to batch processing with splitInBatches, updates happen smoothly, without overwhelming the system.

Why Dynamic Prompts Matter

Unlike traditional field-mapping, dynamic prompts allow non-technical users to instruct the system what to extract — simply by writing clear descriptions in the field settings.

This means:

  • No need to touch the code or logic
  • Easy updates and schema changes
  • True agentic behavior — where the LLM interprets human instructions directly

Integration at Scale: n8n + Baserow + LLMs

This AI PDF data extraction setup shows how you can build a smart, automated AI system using no-code tools—perfect for scaling tasks without writing complex code. At the core, Baserow acts as your data hub. It stores documents, user inputs, or prompt templates in a structured way, making it easy to manage and update content. Next, n8n handles the logic and automation. A Webhook ties it all together, making the flow real-time and reactive. 

Example Use Cases of AI PDF data extraction

Resume Parsing

Automatically extract Name, Email, Skills, Experience, and more from uploaded CVs.

Invoice Scanning

Extract Invoice #, Due Date, Vendor Name, and Amount from PDF invoices without manual entry.

Contract Summary Generator

AI can be directed to identify and sum relevant figures. It holds particular value for settlements, calculating damages, and formal transactional agreements.

How to Set It Up (Quick Start)

  1. Import the n8n workflow (available via n8n.io/workflows)
  2. Create a Baserow table with:
    • A File column (for uploading PDFs)
    • Other fields with descriptions as extraction prompts
  3. Connect your OpenAI API Key
  4. Set up webhooks in Baserow
    • Watch for row.updated, field.created, and field.updated
  5. Test it out! Upload a PDF and watch your fields fill automatically 

*Note: For the JSON template, please contact us and provide the blog URL.

Privacy & Error Handling

  • Any LLM “misses” return a safe n/a response
  • Data remains contained within your tools (Baserow + n8n)
  • Easily extend the workflow to include human review or fallback logic

Conclusion: The Future of AI Data Entry

Why waste time on copy-pasting when you can transform your PDFs into structured data instantly — just by writing clear instructions?

This isn’t just AI PDF data extraction. This is agentic AI workflow automation — dynamic, intelligent, and fully customizable.

Get started today with n8n + Baserow + OpenAI and say goodbye to manual data entry.

Related Reads:

FAQ

What is dynamic prompt extraction?

It’s the use of natural language descriptions in field metadata to guide AI on what data to extract from a document.

Can I use this with Airtable?

Yes — there’s a version for Airtable too! See the Airtable version →

Do I need coding experience?

Not at all. This workflow is designed for no-code builders using n8n + Baserow.

Is this production-ready?

Yes — but always test with your specific use cases. You can add retries, validation logic, and backups as needed.

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