Published on: April 2026
CONVERSATIONAL AI FOR CRAFT BEER RESTAURANTS: A CHATBOT- BASED CUSTOMER SERVICE AND RECOMMENDATION SYSTEM
Shreyash Dakh Harshwardhan Patil Ketan harde
Prof. Amit Nichat
Article Status
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Abstract
The latest developments in Artificial Intelligence (AI) have enabled hospitality businesses to deliver customer service in new ways. The research introduces a conversational AI chatbot system that enables craft beer restaurants to interact with customers through automated service and customised product suggestions. The system employs Natural Language Processing, machine learning, and a recommendation engine to process user input and deliver precise, real- time results. Customers can place orders independently, which reduces staff workload while providing faster, more reliable service. The system demonstrates high response accuracy and improved customer experience compared to standard manual service methods. The results demonstrate how conversational AI can be used in modern restaurants and identify which areas would benefit from adding voice recognition and deep learning technology.
How to Cite this Paper
Dakh, S., Patil, H. & harde, K. (2026). Conversational AI for Craft Beer Restaurants: A Chatbot- Based Customer Service and Recommendation System. International Journal of Creative and Open Research in Engineering and Management, <i>02</i>(04). https://doi.org/10.55041/ijcope.v2i4.411
Dakh, Shreyash, et al.. "Conversational AI for Craft Beer Restaurants: A Chatbot- Based Customer Service and Recommendation System." International Journal of Creative and Open Research in Engineering and Management, vol. 02, no. 04, 2026, pp. . doi:https://doi.org/10.55041/ijcope.v2i4.411.
Dakh, Shreyash,Harshwardhan Patil, and Ketan harde. "Conversational AI for Craft Beer Restaurants: A Chatbot- Based Customer Service and Recommendation System." International Journal of Creative and Open Research in Engineering and Management 02, no. 04 (2026). https://doi.org/https://doi.org/10.55041/ijcope.v2i4.411.
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References
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- •Peer Review Type: Double-Blind Peer Review
- •Published on: Apr 17 2026
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