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Extract structured data from D&B company reports with GPT-4o

Robert BreenRobert Breen
181 views
2/3/2026
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Pull a Dun & Bradstreet Business Information Report (PDF) by DUNS, convert the response into a binary PDF file, extract readable text, and use OpenAI to return a clean, flat JSON with only the key fields you care about (e.g., report date, Paydex, viability score, credit limit). Includes Sticky Notes for quick setup help and guidance.


βœ… What this template does

  • Requests a D&B report (PDF) for a specific DUNS via HTTP
  • Converts the API response into a binary PDF file
  • Extracts the text from the PDF for analysis
  • Uses OpenAI with a Structured Output Parser to return a flat JSON
  • Designed to be extended to Sheets, databases, or CRMs

🧩 How it works (node-by-node)

  1. Manual Trigger β€” Runs the workflow on demand ("When clicking 'Execute workflow'").
  2. D&B Report (HTTP Request) β€” Calls the D&B Reports API for a Business Information Report (PDF).
  3. Convert to PDF File (Convert to File) β€” Turns the D&B response payload into a binary PDF.
  4. Extract Binary (Extract from File) β€” Extracts text content from the PDF.
  5. OpenAI Chat Model β€” Provides the language model context for the analyzer.
  6. Analyze PDF (AI Agent) β€” Reads the extracted text and applies strict rules for a flat JSON output.
  7. Structured Output (AI Structured Output Parser) β€” Enforces a schema and validates/auto-fixes the JSON shape.
  8. (Optional) Get Bearer Token (HTTP Request) β€” Template guidance for OAuth token retrieval (shown as disabled; included for reference if you prefer Bearer flows).

πŸ› οΈ Setup instructions (from the JSON)

1) D&B Report (HTTP Request)

  • Auth: Header Auth (use an n8n HTTP Header Auth credential)

  • URL:
    https://plus.dnb.com/v1/reports/duns/804735132?productId=birstd&inLanguage=en-US&reportFormat=PDF&orderReason=6332&tradeUp=hq&customerReference=customer%20reference%20text

  • Headers:

    • Accept: application/json
  • Credential Example: D&B (HTTP Header Auth)
    > Put your Authorization: Bearer <token> header inside this credential, not directly in the node.

2) Convert to PDF File (Convert to File)

  • Operation: toBinary
  • Source Property: contents[0].contentObject
    > This takes the PDF content from the D&B API response and converts it to a binary file for downstream nodes.

3) Extract Binary (Extract from File)

  • Operation: pdf
    > Produces a text field with the extracted PDF content, ready for AI analysis.

4) OpenAI Model(s)

  • OpenAI Chat Model
  • Model: gpt-4o (as configured in the JSON)
  • Credential: Your stored OpenAI API credential (do not hardcode keys)
  • Wiring:
    • Connect OpenAI Chat Model as ai_languageModel to Analyze PDF
    • Connect another OpenAI Chat Model (also gpt-4o) as ai_languageModel to Structured Output

5) Analyze PDF (AI Agent)

  • Prompt Type: define
  • Text:
    ={{ $json.text }}
  • System Message (rules):
    • You are a precision extractor. Read the provided business report PDF and return only a single flat JSON object with the fields below.
    • No arrays/lists.
    • No prose.
    • If a value is missing, output null.
    • Dates: YYYY-MM-DD.
    • Numbers: plain numerics (no commas or $).
    • Prefer most recent or highest-level overall values if multiple are shown.
    • Never include arrays, nested structures, or text outside of the JSON object.

6) Structured Output (AI Structured Output Parser)

  • JSON Schema Example:
{
  "report_date": "",
  "company_name": "",
  "duns": "",
  "dnb_rating_overall": "",
  "composite_credit_appraisal": "",
  "viability_score": "",
  "portfolio_comparison_score": "",
  "paydex_3mo": "",
  "paydex_24mo": "",
  "credit_limit_conservative": ""
}
  • Auto Fix: enabled
  • Wiring: Connect as ai_outputParser to Analyze PDF

7) (Optional) Get Bearer Token (HTTP Request) β€” Disabled example

If you prefer fetching tokens dynamically:

  • Auth: Basic Auth (D&B username/password)
  • Method: POST
  • URL: https://plus.dnb.com/v3/token
  • Body Parameters:
    • grant_type = client_credentials
  • Headers:
    • Accept: application/json
  • Downstream usage: Set header Authorization: Bearer {{$json["access_token"]}} in subsequent calls.

> In this template, the D&B Report node uses Header Auth credential instead. Use one strategy consistently (credentials are recommended for security).


🧠 Output schema (flat JSON)

The analyzer + parser return a single flat object like:

{
  "report_date": "2024-12-31",
  "company_name": "Example Corp",
  "duns": "123456789",
  "dnb_rating_overall": "5A2",
  "composite_credit_appraisal": "Fair",
  "viability_score": "3",
  "portfolio_comparison_score": "2",
  "paydex_3mo": "80",
  "paydex_24mo": "78",
  "credit_limit_conservative": "25000"
}

πŸ§ͺ Test flow

  1. Click Execute workflow (Manual Trigger).
  2. Confirm D&B Report returns the PDF response.
  3. Check Convert to PDF File for a binary file.
  4. Verify Extract from File produces a text field.
  5. Inspect Analyze PDF β†’ Structured Output for valid JSON.

πŸ” Security notes

  • Do not hardcode tokens in nodes; use Credentials (HTTP Header Auth or Basic Auth).
  • Restrict who can execute the workflow if it's accessible from outside your network.
  • Avoid storing sensitive payloads in logs; mask tokens/headers.

🧩 Customize

  • Map the structured JSON to Google Sheets, Postgres/BigQuery, or a CRM.
  • Extend the schema with additional fields (e.g., number of employees, HQ address) β€” keep it flat.
  • Add validation (Set/IF nodes) to ensure required fields exist before writing downstream.

🩹 Troubleshooting

  • Missing PDF text? Ensure Convert to File source property is contents[0].contentObject.
  • Unauthorized from D&B? Refresh/verify token; confirm Header Auth credential contains Authorization: Bearer <token>.
  • Parser errors? Keep the agent output short and flat; the Structured Output node will auto-fix minor issues.
  • Different DUNS/product? Update the D&B Report URL query params (duns, productId, etc.).

πŸ—’οΈ Sticky Notes (included)

  • Overview: "Fetch D&B Company Report (PDF) β†’ Convert β†’ Extract β†’ Summarize to Structured JSON (n8n)"
  • Setup snippets for Data Blocks (optional) and Auth flow

πŸ“¬ Contact

Need help customizing this (e.g., routing the PDF to Drive, mapping JSON to your CRM, or expanding the schema)?

πŸ“§ robert@ynteractive.com
πŸ”— https://www.linkedin.com/in/robert-breen-29429625/
🌐 https://ynteractive.com

Extract Structured Data from DB Company Reports with GPT-4o

This n8n workflow automates the process of extracting structured data from company reports, likely stored in a database or accessible via an API, using the power of an AI agent powered by GPT-4o. It's designed to streamline the analysis of textual reports by converting them into a structured, machine-readable format.

What it does

This workflow performs the following key steps:

  1. Initiates a Request: It starts by making an HTTP request, likely to fetch company report data from an external source (e.g., a database API, a web service). The specifics of this request (URL, method, headers, body) would need to be configured based on your data source.
  2. Extracts Data from File (Optional/Placeholder): Includes an "Extract from File" node, suggesting that the retrieved report data might be in a file format (like PDF, DOCX, etc.) that needs to be parsed to extract its raw text content. This step would be crucial if the HTTP request returns a binary file.
  3. Converts to File (Optional/Placeholder): Includes a "Convert to File" node, which could be used to convert raw text or other data into a specific file format if needed before processing or for storage.
  4. Processes with AI Agent: The core of the workflow uses an "AI Agent" (powered by LangChain) to intelligently process the extracted company report text.
    • The agent leverages an "OpenAI Chat Model" (specifically GPT-4o, as indicated by the directory name) as its underlying language model.
    • It then uses a "Structured Output Parser" to ensure that the data extracted by the AI agent conforms to a predefined structure (e.g., JSON schema), making it easy to use in subsequent steps or store in a database.
  5. Provides Contextual Notes: A "Sticky Note" is included, likely for documentation or to provide important context or instructions within the workflow itself.

Prerequisites/Requirements

To use this workflow, you will need:

  • n8n Instance: A running instance of n8n.
  • OpenAI API Key: An API key for OpenAI to use the GPT-4o model. This will need to be configured as an n8n credential for the "OpenAI Chat Model" node.
  • Data Source for Company Reports: Access to the company reports, which can be retrieved via an HTTP request (e.g., a database API, a file storage service, a web server). You will need the appropriate API endpoints, authentication, and data format details.
  • Understanding of LangChain Concepts: Familiarity with LangChain agents, language models, and output parsers will be beneficial for configuring and customizing the AI Agent node.

Setup/Usage

  1. Import the Workflow: Import the provided JSON into your n8n instance.
  2. Configure Credentials:
    • Set up an OpenAI API Key credential in n8n and select it in the "OpenAI Chat Model" node.
  3. Configure HTTP Request Node (ID: 19):
    • Update the URL, HTTP Method (GET, POST, etc.), Headers, and Body to correctly fetch your company report data.
    • Ensure any necessary authentication (e.g., API keys, basic auth) is configured for your data source.
  4. Configure Extract from File Node (ID: 1235):
    • If your HTTP request returns a file, configure this node to correctly extract the text content from that file (e.g., specify the file type, binary property).
  5. Configure AI Agent Node (ID: 1119):
    • Define the "System Message" and "User Message" to instruct the AI agent on what information to extract from the company reports. Provide clear examples if possible.
    • Select the "OpenAI Chat Model" credential.
  6. Configure Structured Output Parser Node (ID: 1179):
    • Define the expected output schema (e.g., a JSON schema) that the AI agent should adhere to. This is crucial for ensuring consistent and usable structured data.
  7. Test the Workflow: Run the workflow manually to verify that it fetches the data, processes it with the AI agent, and outputs the structured data as expected.
  8. Further Integration: Connect the output of the "Structured Output Parser" to subsequent nodes to store the extracted data (e.g., into a database, a spreadsheet, or another application).

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