This n8n workflow automates the process of monitoring, analyzing, and logging grid load alerts using AI and database integrations. Starting with a webhook trigger, the workflow receives grid load data, splits it into manageable chunks, and processes it through AI models to generate insights. The workflow uses OpenAI embeddings to contextualize the data, stores processed information in a Supabase vector database for easy retrieval, and leverages language models for analysis and decision making. Finally, it logs the results into a Google Sheet for record-keeping and further analysis.

The workflow is designed for utility companies or grid operators who need real-time monitoring and intelligent analysis of load data to prevent outages, optimize grid performance, and maintain operational efficiency. It demonstrates a sophisticated use of AI, database, and cloud integration to automate complex data workflows.

This setup is ideal for proactive infrastructure management, enabling teams to act on AI-generated insights promptly.

Node Count	11 – 20 Nodes
Nodes Used	@n8n/n8n-nodes-langchain.agent, @n8n/n8n-nodes-langchain.embeddingsOpenAi, @n8n/n8n-nodes-langchain.lmChatAnthropic, @n8n/n8n-nodes-langchain.memoryBufferWindow, @n8n/n8n-nodes-langchain.textSplitterCharacterTextSplitter, @n8n/n8n-nodes-langchain.toolVectorStore, @n8n/n8n-nodes-langchain.vectorStoreSupabase, googleSheets, stickyNote, webhook