Design and Deployment of a Generative AI Copilot for Veterinary Practice Management Using Azure OpenAI and RAG Architecture

Abstract

Veterinary practices are increasingly facing operational inefficiencies, particularly in administrative tasks and clinical decision-making. Despite advancements in healthcare technology, many veterinary clinics continue to rely on outdated methods for practice management, which can lead to time-consuming processes and suboptimal care. The integration of artificial intelligence (AI) offers the potential to automate routine tasks and enhance decision support systems, improving both efficiency and quality in veterinary settings (Gatla, 2020; Chinta, 2019).

The objective of this study is to develop and evaluate a generative AI copilot designed to optimize veterinary practice management. This AI system automates administrative tasks, such as scheduling and client communication, and enhances clinical decision-making by leveraging the capabilities of Azure OpenAI and Retrieval-Augmented Generation (RAG) architecture (Vanhaelen et al., 2020). By integrating these technologies, the AI copilot dynamically retrieves relevant data, providing real-time assistance to veterinarians.

The AI copilot uses Azure OpenAI’s natural language processing capabilities to understand and generate context-specific responses, while RAG allows it to access large datasets for accurate decision-making. This system aids veterinarians in managing patient records, communicating with clients, and making informed clinical decisions in a timely manner (Hou et al., 2020).