Overview
DineEase is a next-generation restaurant experience platform that lets users reserve tables, pre‑order meals, and follow their order journey in real time. It combines intelligent personalization, immersive AR previews, and an operations-focused admin dashboard to streamline both guest and restaurant workflows.
Problem
Traditional reservation systems stop at booking. Guests still queue, wait for meal prep, and make uninformed choices. Restaurants juggle capacity, kitchen timing, and menu personalization manually, leading to inefficiencies.
Solution Architecture
Client: Next.js (App Router) + TypeScript + ShadCN UI for accessible, composable UI.Data / Realtime: Firebase Firestore for reservations, orders, menu, and event streams.Auth: Firebase Auth (Email/Password + Google OAuth) with server‑protected routes.Recommendations: Custom ML model (collaborative + content hybrid) generating meal suggestions per user profile & history.AR Experience: 360° table & dish preview (WebXR / Three.js integration planned) to help users visualize plating and ambience.State & Sync: Firestore listeners driving reactive UI for live order status and capacity updates.Admin Dashboard: Role-based management for menus, booking slots, live order queue, wallet adjustments.Key Features
Table reservation with capacity + time slot validationMeal pre‑ordering tied to reservation lifecycleReal-time order status (Placed → In Kitchen → Ready → Served)ML-driven personalized dish recommendationsAR-powered immersive table & meal preview (prototype stage)User wallet balance + transaction historyAdmin CRUD for menu items & pricingBooking + order analytics snapshot (daily covers, popular dishes)Tech Stack
| Layer | Tools |
|---|
| Frontend | Next.js, TypeScript, ShadCN UI, Tailwind CSS |
| Backend (Serverless) | Firebase Functions (planned), Firestore |
| Auth | Firebase Auth |
| Realtime | Firestore listeners |
| ML | Custom recommendation pipeline (model artifacts) |
| AR | WebXR / Three.js (prototype) |
| Dev Tooling | GitHub, ESLint, Prettier, Docker (experiments) |
Data Model Highlights
reservations: userId, partySize, timeSlot, statusorders: reservationId, items[], total, status, timestampsmenuItems: category, price, tags, availability, nutritional metarecommendations: userId → rankedItems[] cacheChallenges & Learnings
Designing a reactive architecture where Firestore listeners don’t over-fetch required careful query shaping and index tuning.Balancing real-time updates with client performance → batched UI state reducers.Building an extensible recommendation layer allowed future pivot to external ML API if needed.AR prototype required texture optimization to maintain mobile performance.Outcome
Reduced average check‑in to serve latency (target: −25% projected based on simulation).Increased pre‑order completion likelihood (early pilot feedback: higher user confidence in meal selection).Modular architecture supports future FCM notifications & predictive wait times.Role & Responsibilities
Architected data model & Firestore security rules.Implemented reservation + pre‑order flows & admin dashboard foundations.Prototyped recommendation engine & AR feature.Led UI system standardization using ShadCN primitives.Future Improvements
FCM push notifications (order ready, reservation reminders)Predictive wait time model (queue analytics + smoothing)Offline-friendly caching layer (service worker)Multi-restaurant SaaS onboarding flowKitchen display system integration---
This project showcases full‑stack design, real‑time data modeling, and product thinking blending ML, AR, and usability.
