Nova: A Real-Time AI Sales Call Assistant with Live Speech Transcription, Knowledge-Grounded Coaching and Simulated Sales Training

Gokul Raj R, Dr, D. Mohanapriya, Dr and Nithyanandh Selvam

International Journal of Scientific Research in Computer Science, Engineering and Information Technology, 2026, vol. 12, issue 5, 278-287

Abstract: Sales representatives must respond to customers in real time while recalling product details, recognizing customer needs and buying signals, and deciding on suitable follow-up actions. This paper presents Nova, a full-stack Real-Time AI Sales Call Assistant that supports representatives during customer conversations. The browser captures live speech through the Web Speech API and converts it into a running transcript, and the React frontend sends the relevant conversation data to a Flask backend through REST APIs. An AI coaching service analyzes the conversation context and returns live suggestions, namely a recommended response, a follow-up question, objection-handling guidance, detected buying signals and a next action. Lead management, call history, call notes, follow-ups, a dashboard and follow-up email workflows are supported using SQLite and SQLAlchemy with JWT-based authentication. A training module simulates customer scenarios and scores the practice session. Completed calls store the transcript, call duration, speaker information, sentiment, summary and follow-up recommendations. The implemented system supports live microphone capture, speech transcription, real-time coaching suggestions, lead management and call-history storage. The current coaching logic is built around rules, knowledge and application logic, and no formal predictive-model accuracy is reported. The system integrates live transcription, coaching, record keeping and training within a single web application.

Keywords: Sales Assistant; Real-Time Coaching; Speech Recognition; Web Speech API; Flask; React; REST API; Objection Handling; Sales Training (search for similar items in EconPapers)
Date: 2026
Note: Article URL: https://ijsrcseit.com/home/article/view/CSEIT2612550
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DOI: 10.32628/CSEIT2612550

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