The limitations of traditional alarm systems arise from their time-based triggers which remain inactive during periods of user activity. Users who operate mobile devices encounter alarms which function as basic on-off switches that do not possess abilities to comprehend their requirements or detect changes in their surroundings. The paper develops a new system called "Smart Alarm Powered by Spring AI" which uses an intelligent backend system to modernize traditional wake-up calls through its interactive dialogue-based system. The system combines Spring Boot framework with Spring AI library and local Large Language Models (LLMs) which use Ollama technology to create a system that connects basic task scheduling with advanced natural language handling capabilities. The proposed architecture enables a seamless real-time voice-to-voice interaction paradigm, moving beyond the text-heavy interfaces of contemporary virtual assistants. The system uses ElevenLabs high-fidelity neural text-to-speech synthesis system to produce customized human-like speech output, which goes beyond traditional applications that depend on fixed pre-recorded sound alerts. The backend system uses MongoDB persistent metadata storage to keep user information, previous system interactions, and specific behavioral triggers, which helps the system provide continuous relevant user experiences. The AI generates context-based answers through its ability to modify voice tone according to meeting urgency and present customers with instant weather and schedule briefings. The research examines two main technical problems which include low-latency audio processing requirements and the need to establish AI systems that operate securely through local-first deployment methods.
Spring AI, Large Language Models (LLMs), Intent Classification, Local AI Orchestration, Ollama, Speech-to-Text (STT), Neural Text-to-Speech (TTS), MongoDB
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