n8n Airtable GDPR Research Contact Enrichment Workflow

Enrich GDPR-focused research contacts in Airtable—automatically, twice daily

This n8n workflow reads pending “Research Requests” from Airtable, searches for relevant people using Tavily Search, extracts structured contact details with OpenAI, enriches them with Dropcontact, and then upserts enriched contacts back into Airtable—updating each request to Completed or Failed - Low Confidence.

What this workflow does

• Runs on a schedule at 09:00 and 14:00.
• Pulls pending records from the Airtable “Research Requests” table where Status = Pending.
• Searches with Tavily Search API and filters results to keep only high-confidence matches.
• If no usable results are found, it updates the Airtable request status to Failed - Low Confidence.
• If usable results exist, it sends Tavily output to OpenAI (GPT) to extract a structured JSON list of contacts (names, organizations, roles, and interests).
• Enriches contacts with Dropcontact, waits for the asynchronous callback, then fetches the enriched results.
• Upserts into Airtable “Contact List” for each enriched contact and marks the original request as Completed.

Use cases

• Turn research prompts into a continuously updated Airtable contact database for sales, recruiting, or partnerships.
• Automate research intake: analysts submit requests in Airtable; the workflow handles search, extraction, enrichment, and status tracking.
• Keep quality high by failing fast when Tavily confidence is low (Failed - Low Confidence).

Technical details

• Integrations/steps include: Airtable, Tavily Search, OpenAI, and Dropcontact.
• n8n node types referenced: if, set, code, wait, merge, airtable.
• Setup requirements: Airtable credentials + table IDs, OpenAI API credential (e.g., gpt-5-mini), and TAVILY_API_KEY as an environment variable.