Lead Sense AI: An Llm-Powered Machine Learning (ML) and Natural Language Processing (NLP) System for Automated Sales Email Intent Scoring

¹ Rajalakshmi Engineering College, Computer Science and Business Department, 602105 Chennai, India
² Rajalakshmi Engineering College, Computer Science and Business Department, 602105 Chennai, India
³ Rajalakshmi Engineering College, Computer Science and Business Department, 602105 Chennai, India

^* Corresponding author: This email address is being protected from spambots. You need JavaScript enabled to view it.

Abstract

Manual lead qualification is time-consuming, inconsistent, and prone to human error, especially for short-term sales teams that receive hundreds of inbound emails per day. Automation of lead prioritization and classification through Lead Sense AI, an intelligent email-intent scoring system, is proposed in this paper. The proposed framework combines Large Language Models (LLMs), semantic embeddings, and a Light GBM machine learning classifier to analyse and score incoming sales emails. The actions are as follows: the system has as input raw text from email sources; semantic intent features are extracted; salient features such as purchase intent, urgency indicators, and sentiment features are assessed and combined to output a lead score using a pipeline that enables sales decisions. Experimental evaluation proves that comprehension through the LLM based on semantic understanding dramatically outstrips the performance of keyword-based intent detection methods. The results demonstrate the effectiveness of hybrid LLM + machine learning architectures as a scalable, real-time, and objective approach to sales lead qualification.