Overview
This workflow automates the collection, processing, and storage of chat data from WhatsApp or Slack into a Supabase PostgreSQL database, leveraging Gemini AI for advanced language processing and enrichment.
Key Features
- Manual Trigger: Initiate the workflow for testing or on-demand processing.
- Input Preparation: Set and update sample input variables, including user details like name and address.
- AI Processing: Utilize Gemini AI (GeminiFlash2.0) for natural language understanding and enrichment of chat content.
- Database Integration: Store processed chat data in Supabase Postgres, ensuring structured and scalable data management.
- Agent Automation: Employ a sample agent for additional automated actions or responses.
Benefits
- Centralized Data Storage: Consolidate chat interactions from multiple platforms into a single, queryable database.
- Enhanced Insights: AI-driven processing enables deeper analysis and actionable insights from chat data.
- Time Savings: Automates manual data entry and enrichment, reducing operational overhead.
- Scalability: Easily adapts to growing chat volumes and additional data sources.
Use Cases
- Customer support teams seeking to archive and analyze chat interactions.
- Businesses integrating conversational data into CRM or analytics platforms.
- Automating lead capture and enrichment from messaging platforms.
Integrations
- WhatsApp/Slack (chat data sources)
- Gemini AI (language processing)
- Supabase Postgres (data storage)
- n8n (workflow automation)
