Fudo
An AI Food Decision Optimizer Built for Everyday Eating Decisions
Team: Optipied
Hackathon: Techvoot AI Hackathon 2026
Category: AI in Health & Nutrition | Food Decision Intelligence
Fudo is an AI-powered food decision assistant that helps users make smarter, real-time eating choices based on context, goals, and available ingredients.
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What Fudo Actually Is?
What Fudo Actually Is?
Fudo is an AI-driven food decision system. It does not plan full-day meals. It does not assume perfect discipline. Instead, Fudo evaluates a user’s current situation and suggests the most suitable next food decision, with clear consequences attached.
Users input what they have already eaten, what ingredients are available, and what goal they are working toward. The system responds with practical food choices that fit that exact moment.
Why the Team Choose This Problem?
Why the Team Choose
This Problem?
Most nutrition apps focus on planning.
They ask users to decide meals hours in advance or log food after eating. Real life rarely follows those plans. Hunger, schedule changes, stress, and availability override intent.
- Decisions are made in the moment, without clarity.
- Guilt-based systems discourage recovery after mistakes.
- Static rules fail when days become unpredictable.
Team Optipied identified three recurring issues:
Fudo was built to work inside those imperfect moments, where most real food decisions happen
How the Food Decision Flow Works
Fudo operates as a decision engine rather than a diet planner.
Current State Awareness
The system first builds context around the user’s present state.
It tracks:
- Meals already consumed.
- Time since the last meal.
- Energy level (simple user input).
- Workout activity.
- Ingredients available at that moment.
This creates a real-time nutritional snapshot instead of a rigid schedule.
Decision-Level Food Suggestions
Rather than prescribing meals, Fudo presents multiple food options.
Each option reflects:
- Calorie impact.
- Hunger control.
- Issues are flagged early instead of during final review stages.
- Eat a protein-focused option now.
- Choose a lighter meal and delay hunger.
- Wait briefly and eat a balanced option later.
Example:
This allows users to choose consciously rather than react impulsively.
Regret Prediction Logic
One of Fudo’s core capabilities is consequence awareness.
For each suggested option, the system estimates:
- Likelihood of future hunger.
- Impact on evening cravings.
- Effect on daily goal adherence.
This shifts food decisions from impulse-based to outcome-aware.
Damage Control Mode
Fudo adapts when users deviate from their plan.
If a high-calorie or unplanned meal is logged, the system recalculates and suggests recovery actions. These include adjusted meals, light activity, or protein balancing later in the day.
The goal is continuity, not punishment.
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Talk to Our Experts TodayWhy Users Would Actually Use This
Why Users Would Actually Use This
Fudo fits into everyday life without demanding perfection.
- Reduced decision fatigue.
- Practical guidance during unplanned moments.
- Clear recovery paths after poor food choices.
- Better consistency across days.
Users benefit from:
It supports long-term adherence by helping users recover instead of restart.
The Technology Behind the Scenes
- Mobile Interface: Flutter
- Decision Logic: Node.js
- Food & Nutrition Data: Laravel backend
- Optional Web Demo: React / Vue
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