MenuGuard AI 🍽️ — Helping Restaurants Fix Menu Mistakes with Open-Weight AI

This is a submission for the Hacktoberfest Open-Source AI Challenge Week 2: Side Hustle.

What I Built

Imagine running a restaurant with hundreds of menu items. A duplicate listing, inconsistent price, incorrect dietary label, or misleading description could confuse customers and damage trust.

That's why I built MenuGuard AI, an AI-powered menu auditing platform designed for restaurants, cafés, and cloud kitchens.

It helps business owners identify menu mistakes, review AI-suggested corrections, and export cleaner, more consistent menus.

Key Features

• 🍽️ Restaurant Dashboard: Track menu items, detected issues, high-priority findings, and audit history.
• 📂 Menu Management: Add, edit, delete, search, and import menu items using CSV.
• 🔍 Rule-Based Auditing: Detect duplicate items, pricing conflicts, missing descriptions, and dietary contradictions.
• 🤖 AI-Powered Auditing: Analyze descriptions, ingredients, potentially misleading claims, and allergen uncertainty.
• ✅ Human-in-the-Loop Review: Approve, reject, or edit suggested corrections before applying them.
• 🔄 Before-and-After Comparison: Compare original and corrected menu information.
• 📊 Export Reports: Download cleaned menus and audit findings in CSV, Markdown, or JSON.

Who Would Pay for It?

MenuGuard AI is designed for independent restaurant owners, small cafés, cloud kitchens, and food businesses that regularly update their menus.

The potential business model is simple: affordable one-time menu audits or a low-cost monthly subscription.

Demo

🚀 Live Application: https://menuguard-ai.onrender.com/

Try the application using the built-in fictional café dataset, run a menu audit, review findings, and explore the correction and export workflow.

Code

💻 GitHub Repository: https://github.com/shettysaikumar20/Menu-Gaurd

The repository includes the frontend, backend, auditing logic, API integration, tests, and deployment configuration.

How I Built It

MenuGuard AI combines deterministic validation with open-weight language model analysis.

Technology Stack:

• Frontend: React, Vite, JavaScript
• Backend: Node.js, Express
• AI Integration: Backboard AI
• Open-Weight Model: Meta Llama 3.1 70B Instruct
• Model Provider Configuration: Featherless
• Testing: Vitest and backend API tests
• Deployment: Render
• Optional Database: MongoDB

Layer 1 — Deterministic Auditing

The rule engine identifies structural and logical issues such as missing fields, duplicate menu items, conflicting prices, and dietary contradictions.

It also distinguishes legitimate size variants from actual pricing conflicts.

Layer 2 — Open-Weight AI Auditing

Llama 3.1 is integrated through Backboard AI to examine menu descriptions and ingredient information for possible inconsistencies, misleading language, and allergen-related uncertainty.

When credentials are unavailable, the application uses an explicitly labeled mock fallback rather than silently presenting mock findings as live AI results.

Human Review

AI suggestions are not automatically applied.

Restaurant managers can inspect the evidence, compare original and proposed values, approve or reject findings, and explicitly apply accepted changes.

Testing and Verification

During development:

• 15 backend tests passed.
• 4 frontend tests passed.
• 19 automated tests passed in the combined project test run.
• Production frontend build completed successfully.
• Backend health and API endpoints were tested locally.

The application is configured to deploy its frontend and backend together as one Render web service.

Why Does Open Innovation Matter?

Open innovation is central to MenuGuard AI.

1. Model Flexibility

Using an open-weight model such as Llama 3.1 creates opportunities to experiment with different inference providers and model configurations rather than depending entirely on one proprietary model.

2. Affordable AI for Small Businesses

Restaurant owners need practical tools, not necessarily expensive enterprise software. Open-weight models provide options for building more cost-conscious AI services, although hosted inference still has operating costs.

3. Transparency and Control

The auditing workflow combines understandable deterministic checks with AI analysis. Managers can review evidence and decide which corrections should be applied.

4. Customization

Different restaurants have different cuisines, ingredients, and dietary terminology. An open-weight approach makes it possible to adapt the analysis pipeline and explore specialized models in the future.

5. Open Development

The project's source code is publicly available, making it possible for developers to inspect, improve, and extend the auditing system.

What I Learned

Building MenuGuard AI taught me how to combine traditional software validation with open-weight AI, integrate an external inference service, build human-in-the-loop workflows, test REST APIs, and prepare a full-stack application for cloud deployment.

The biggest lesson was that useful AI products need more than generated answers. They need evidence, clear limitations, and a way for users to remain in control.

Final Thoughts

MenuGuard AI started with a simple idea:

What if a small restaurant could check its entire menu for potential mistakes before customers ever saw them?

By combining rule-based auditing, open-weight AI, and human review, I built a working foundation for an affordable restaurant menu-quality service.

🍽️ MenuGuard AI — Smarter Menus. Fewer Mistakes. Better Business.

Live Demo: https://menuguard-ai.onrender.com/

GitHub: https://github.com/shettysaikumar20/Menu-Gaurd

Thanks to DEV, MLH, and the Hacktoberfest community for encouraging developers to build useful products with open AI!