Sistema de gestión veterinaria automatizado con inteligencia conversacional y visualización de datos en tiempo real

Abstract

In outpatient veterinary clinics, manual management of appointments and inquiries through instant messaging generates delays, operational overload, and a higher risk of scheduling errors. The lack of centralized information and automated tools limits staff efficiency and negatively impacts the pet owner’s experience. In response to this issue, this project proposes the development of an integrated management and customer support platform based on automation and conversational intelligence. The hypothesis states that integrating automated flujos de trabajos and language models improves appointment administration and reduces repetitive tasks. The system was designed using a layered architecture and implemented through Docker containers. n8n was used for workflow orchestration, Evolution API enabled messaging integration, MongoDB supported transactional data storage, Qdrant functioned as a vector database, and OpenAI models were applied for message classification and entity extraction. Cloud services were incorporated for storage and deployment. Results showed an automation level above 80% in appointment management and a significant reduction in manual intervention. It is concluded that intelligent automation enhances operational efficiency and strengthens clinical service organization. Keywords: process automation, conversational intelligence, layered architecture, digital messaging, operational efficiency.