Check In — Know Your Readiness

Each day starts with a wellness check-in capturing sleep hours, sleep quality, energy, stress, mood, exercise, meals, and screen time. These 8 signals produce a weighted Readiness Score (0–100) that tells you — and the AI — how prepared you are today.

Plan — Build Your Study Schedule

Create study plans with subjects, topics, timeslots (Morning / Afternoon / Evening / Night), target durations, and difficulty tags. The database enforces logical time ordering and the planner color-codes entries by subject for a clear visual timeline.

Execute & Log — Capture What Really Happened

After each session, log your actual start/end time, rate focus (1–5) and fatigue (1–5), select distraction types (Phone, Noise, Social Media, etc.), and add a reflection. The system validates no overlaps and no future times, then auto-triggers the AI engine.

Analyze — 8-Pattern Cognitive Pipeline

Your logs pass through a 4-node LangGraph pipeline: Validate → Build Context → Generate → Parse. Before the LLM sees anything, a ContextBuilder computes 8 cross-dimensional patterns — planning accuracy, subject×timeslot efficiency, distraction triggers, fatigue curves, deep work ratio, best/worst combos, and completion rate — ensuring insight specificity.

Act — Turn Insights into Plans

AI recommendations are generated by Groq LLaMA 3.3 70B with a strict anti-rephrase contract: every insight must cite a number from your data. When an insight includes a plan suggestion, click "Add to Plan" to create a real study schedule entry — closing the full loop from analysis to action.

Monitor — Track Progress & Prevent Burnout

The analytics dashboard visualizes your story: Planned vs Actual charts, Activity Heatmaps, Subject Priority Matrix, and Efficiency by Weekday. A Burnout Monitor blends fatigue, consistency, and focus into a risk score. A Procrastination Tracker auto-detects skipped sessions. And a Weekly Digest gives you 3 Wins, 3 Issues, and 3 Actions — exportable as a PDF.