How to Automate Lead Qualification with LLMs: A Production Guide

Manual lead qualification is the biggest time sink in most small businesses. A salesperson spends 40-60% of their time on leads that will never convert. LLMs change this equation — when deployed correctly, they qualify leads with 85%+ accuracy at 200ms response times, freeing your team to focus on the 20% of leads worth their attention. At AI Buddy , we've deployed this for dozens of Israeli businesses across e-commerce, home services, B2B SaaS, and healthcare. Here's the real implementation. What "Lead Qualification" Actually Means Before writing code, define what you're qualifying for. The BANT framework (Budget, Authority, Need, Timeline) is a reasonable starting point: Budget : Can they afford your product/service? Authority : Are they the decision maker? Need : Do they have the problem you solve? Timeline : Are they buying soon or just browsing? You need to extract these from unstructured conversation text and produce a score. Here's the data model: from dataclasses import datacla