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.
Abstract (translated)
在实时销售通话中，客户常会提出详细的产品问题，这要求销售代表手动查询内部数据库和CRM系统。此过程通常每项查询需耗时25-65秒，造成尴尬的停顿，损害客户体验并降低销售效率。我们推出了SalesCopilot——一款实时AI助手，通过自动检测客户问题、从产品数据库检索相关信息，并在数秒内将简明答案呈现在代表仪表板上，从而消除这一瓶颈。该系统将流式语音转文本转录、基于大语言模型（LLM）的问题检测，以及对结构化产品数据库的检索增强生成（RAG）整合为统一的实时流水线。我们在一个涵盖50种产品、10个类别（2,490条常见问题、290项保障详情、162个定价层级）的保险销售场景中演示了SalesCopilot。在基准评估中，SalesCopilot实现了平均2.8秒的响应时间，问题检测率达100%，相比内部研究中手动CRM搜索提速14倍。该系统不限定领域，只需替换产品数据库即可适配任何企业销售领域。
URL
https://arxiv.org/abs/2603.21416
