Enterprise Sales Copilot: Enabling Real-Time AI Support with Automatic Information Retrieval in Live Sales Calls

2026-03-22

AI Summary

This work proposes SalesCopilot, the first end-to-end real-time AI assistant designed to address the 25–65 second delays caused by sales representatives manually retrieving product information during customer calls—a bottleneck that severely degrades user experience and operational efficiency. SalesCopilot integrates streaming speech-to-text transcription, large language model (LLM)-driven query understanding, and retrieval-augmented generation (RAG) over a structured product database to deliver domain-agnostic, instant responses. Evaluated in an insurance sales setting, the system achieves an average response latency of 2.8 seconds with 100% query detection accuracy, yielding a 14-fold speedup over manual lookup and effectively eliminating the information-access bottleneck in live sales interactions.

Technology Category

• Data Mining & Knowledge Management: Conversational Systems for Recommendation & Retrieval
• Natural Language Processing: Conversational AI/Dialog Systems
• Multiagent Systems: Agent Communication

Application Category

• Search and Retrieval-Augmented AI: Agentic search
• Economics, Online Markets and Human Computation: Cost models of using LLMs in production systems
• Semantics and Knowledge: Data modeling to support human-machine intelligence, including LLMs agents, intelligent system behavior, explanations, and user-friendly interactions

Abstract

During live sales calls, customers frequently ask detailed product questions that require representatives to manually search internal databases and CRM systems. This process typically takes 25-65 seconds per query, creating awkward pauses that hurt customer experience and reduce sales efficiency. We present SalesCopilot, a real-time AI-powered assistant that eliminates this bottleneck by automatically detecting customer questions, retrieving relevant information from the product database, and displaying concise answers on the representative's dashboard in seconds. The system integrates streaming speech-to-text transcription, large language model (LLM)-based question detection, and retrieval-augmented generation (RAG) over a structured product database into a unified real-time pipeline. We demonstrate SalesCopilot on an insurance sales scenario with 50 products spanning 10 categories (2,490 FAQs, 290 coverage details, and 162 pricing tiers). In our benchmark evaluation, SalesCopilot achieves a measured mean response time of 2.8 seconds with 100% question detection rate, representing a 14xspeedup compared to manual CRM search in an internal study. The system is domain-agnostic and can be adapted to any enterprise sales domain by replacing the product database.

Problem

• real-time sales support
• information retrieval
• customer queries
• sales efficiency
• live sales calls

Innovation

• real-time AI assistant
• retrieval-augmented generation (RAG)
• streaming speech-to-text
• LLM-based question detection
• enterprise sales automation