Multimodal chat assistant for universities based on RAG

In the university ecosystem, the demand for accurate and up-to-date institutional information is constant, but traditional rule-based FAQ systems often fall short when faced with complex queries or regulatory changes. An emerging approach combines large language models (LLMs) with semantic retrieval to build truly intelligent assistants. This architecture, known as retrieval-augmented generation (RAG), allows the system to consult official sources —such as academic manuals or regulations— in real time before generating a response, drastically reducing hallucinations and improving contextual coherence. By incorporating multimodal capabilities, the assistant can process both text and images; for example, a student could photograph a printed schedule and receive clarifications about subject conflicts. The practical implementation of these systems requires a scalable backend and a responsive frontend, as well as quantization techniques to deploy models on limited hardware. Q2BSTUDIO develops artificial intelligence solutions for businesses that integrate RAG, multimodal processing, and cloud services such as AWS and Azure, ensuring reliable and fast responses even in resource-constrained environments. Latency optimization is key: while text queries respond almost instantly, visual ones require more computation, although user satisfaction remains high. Furthermore, the application of AI agents allows automating recurring administrative tasks, such as verifying enrollment requirements. Cybersecurity is not neglected: by centralizing access to sensitive data, authentication and encryption controls are implemented. Universities that adopt this type of custom applications manage to reduce the workload of administrative staff and offer a 24/7 service to students and faculty. Integration with business intelligence tools such as Power BI allows monitoring the most frequent queries and adjusting institutional policies agilely. In essence, the multimodal assistant with RAG represents a qualitative leap in university information management, combining technical precision, scalability, and a natural user experience.