An autonomous, privacy-first smart assistant that unifies university dashboards and communications into a single conversational interface.
TeleGAssistant was designed to solve "dashboard fatigue" for modern students. Built by a collaborative 4-person software engineering team, the app connects directly to the Canvas API to monitor schedules, maps user context via SQLite, filters data through an on-device Large Language Model (Gemma 2 2B), and pushes smart updates straight to 'Telegram'.
By prioritizing edge computing and local AI inference over cloud APIs, the system guarantees absolute data privacy while maintaining a responsive, non-blocking notification ecosystem.
Automatically aggregates Canvas data, emails, and calendars into a single operational point.
Leverages a localized LLM to generate morning briefings without data leaving the machine.
Monitors nearing task deadlines and updates tracking states to completely prevent user notification spam.
Features robust, interactive messaging commands allowing users to instantly request updates.
By routing Canvas academic records, private Gmail snippets, and local schedules strictly through a device-hosted Gemma 2 2B instance, the project introduces a highly secure alternative to traditional AI tools, keeping sensitive personal metrics completely decoupled from third-party vendor servers.
The application condenses fractured academic environments—including course updates, due dates, and grading rubrics—into a singular conversational feed. This design actively prevents platform hopping and ensures critical milestones are delivered as prioritized summaries directly to a user's phone.
The system demonstrates a robust blueprint for local-first utility middleware. Leveraging an asynchronous, event-driven monolith model running on Python and SQLite proves that powerful, personalized automation apps can be reliably sustained on low-overhead consumer hardware without cloud subscription models.
TeleGAssistant simplifies structural data parsing, API synchronization, and prompt engineering into an intuitive text-based user workflow. It bridges complex technical pipelines with conversational UX design, demonstrating how advanced AI engineering can be translated into straightforward, everyday software value.