Telemarketing, a conventional sales channel, faces substantial challenges, such as a persistent attrition of qualified personnel, elevated labor costs, variable sales quality, and obstacles in scaling operations, despite the widespread adoption of digital transformation in numerous industries.
At the second "2025 AI Wave: Taiwan Generative AI Applications Hackathon," the "Cloud Science Network Management" team presented an innovative solution that merges AI with user-centric design to transform telemarketing. This groundbreaking approach won the Eastern Home Shopping & Leisure's "Next-Gen Retail" competition.
The "Cloud Science Network Management" team comprises individuals from the departments of Electronics, Computer Science and Engineering, Information Management, and Electrical Engineering at National Yunlin University of Science and Technology in Taiwan. Break away from conventional thinking, they leverage their professional expertise to develop an intelligent sales system addressing market challenges. This system uses AI technology to resolve long-standing industry pain points, while maintaining a humanized interactive experience.
The team discovered that traditional telemarketing is confronted with three significant challenges following extensive market research. Initially, corporate profits are significantly impacted by the high costs of labor and training. Secondly, the performance levels of salesmen are inconsistent, and it is exceedingly challenging to standardize sales scripts. Thirdly, personnel recruitment bottlenecks that severely limit sales expansion.
Although telemarketing continues to be a significant promotional channel, market research indicates that consumers are evidently opposed to interactions that appear to be overly mechanical. Consequently, the optimal solution must strike a balance between the efficacy of AI and the humanization of interaction in order to establish a sales process that is both strategic and highly consistent, while also ensuring consumers cannot detect they are interacting with an AI system rather than a human representative.
To address these challenges, the team developed the "AI Smart Salesperson," a sophisticated solution employing AI technology for telemarketing. The system transforms AI into a highly effective telephone sales representative by incorporating data-driven analysis, real-time emotional assessment, and natural voice interaction technology. The operational workflow consists of four phases: first, inputting basic customer and product information; second, developing personalized sales strategies based on customer backgrounds; third, initiating the calling process where the customer's voice is transcribed using speech-to-text (STT) technology, dynamic responses are generated through a dialogue large language model (LLM), and these responses are converted into natural speech using text-to-speech (TTS) technology; and finally, using an analytical LLM to simultaneously assess customer emotions and responses, enabling real-time adjustments to sales strategy based on immediate feedback.
The "AI Smart Salesperson" substantially reduces labor and training expenses while ensuring uniformity and consistency in sales methodologies, significantly enhancing both sales success rate and customer experience. The system's technical architecture is built on Amazon Web Services (AWS) and leverages multiple innovative technologies to ensure reliability and natural interaction. The core technologies include large language models responsible for dialogue generation, strategy planning, and real-time analysis, along with bidirectional text-to-speech (TTS) and speech-to-text (STT) conversion capabilities.
For the AWS implementation, the team utilized Amazon Bedrock, a fully managed service that makes high-performing foundation models, to integrate models such as Anthropic Claude for dialogue generation. They employed Amazon SageMaker for machine learning model training and inference, and Amazon Transcribe for automatic speech recognition to enable speech-to-text functionality. The solution's data storage uses MySQL databases, while Amazon CloudFront handles web request delivery. The entire infrastructure runs on Amazon Elastic Compute Cloud (Amazon EC2) instances, providing the scalability and reliability needed for production deployments.
This groundbreaking approach received unanimous recognition from judges in Eastern Home Shopping & Leisure's "Next-Gen Retail" category. The system features natural voice simulation for a genuine interactive experience indistinguishable from human interaction, standardized speech, and dynamic adjustment capabilities to maintain consistent and adaptable sales quality, along with a data-driven recommendation mechanism that significantly enhances the likelihood of a successful transaction.
Furthermore, customers can promptly activate the platform by importing product information and listings, without the need to construct additional complex infrastructure, as a result of the out-of-the-box design. These benefits not only resolve the telemarketing dilemma that contemporary enterprises encounter, but they also establish a new paradigm for the application of AI throughout the entire industry.
Article edited by Sherri Wang
