Lead Scoring with MLS Data Integration

This workflow automates the process of lead scoring by leveraging MLS data, artificial intelligence, and vector similarity search. It starts with a webhook trigger, receives lead data, and processes it with language models and embeddings for advanced analysis. The key steps include extracting relevant data, generating embeddings using OpenAI, storing these in a Pinecone vector database, and performing similarity searches to identify high-potential leads. An AI agent constructs insights based on the data, which are then logged into Google Sheets for future reference. This workflow is ideal for real estate agencies or marketing teams aiming to prioritize leads based on AI-enhanced scoring, improving outreach effectiveness and decision-making.