gent with Structured Data Capture & Airtable Integration
Abang Tah
AI Chat Agent with Structured Data Capture & Airtable Integration
This workflow demonstrates how I design intelligent AI-driven automations that combine real-time chat, AI reasoning, and structured data logging for later analysis.
How It Works:
Trigger → When a chat message is received, the workflow activates.
AI Processing (Gemini) → The message is passed into Google Gemini Chat Model, which generates a context-aware response.
Prompt Engineering → A new structured prompt is generated and refined through several steps:
Edit Fields – adjust inputs for clarity
Categorize & Name Prompt – classify the type of request
Auto-fixing Output Parser – ensure structured, valid output
Structured Output Parser – enforce JSON-like clean data formatting
Data Handling → The enriched and categorized prompt is added into Airtable for tracking and future workflows.
Response Delivery → Results are returned back into the chat in real-time.
Outcome:
Turns raw chat into structured, categorized data.
Automates AI response generation.
Logs every interaction for further reporting or training future models.
Can be extended with CRM, email replies, or analytics.
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Posted Aug 26, 2025
Designed AI-driven chat automation with structured data logging.
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Timeline
Jan 7, 2025 - Jun 10, 2025
