1.Detect new enrichment row
Integrate PostgreSQL and database triggers to detect new enrichment payloads and map the source row id for processing.
When new guest enrichment data lands, profile attributes can stay incomplete and slow downstream analysis. This automation creates queue entries, runs AI structured extraction, and updates PostgreSQL demographicsβso your team can rely on accurate audience data.
Integrate PostgreSQL and database triggers to detect new enrichment payloads and map the source row id for processing.
Integrate PostgreSQL and database tools to create a queue entry and associate the source row id with a queue key.
Integrate PostgreSQL and analytics tools to run a custom query that returns user lookup fields and enrichment payload.
Integrate Zapier Tables and configuration storage to retrieve prompt text and model parameters for the AI step.
Integrate ChatGPT (OpenAI) and AI parsing tools to pass guest data and capture structured JSON for demographics.
Integrate PostgreSQL and reporting databases to map parsed fields and update or create demographics tables and import records.
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Step 1
Bring your apps together so information can move automatically between the tools your team already uses.
Step 2
Choose the events that should start your workflow, like a new submission, updated record, completed task, or customer action.
Step 3
Let your workflow handle follow-ups, updates, notifications, and reporting so your team can track progress and act faster.
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