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Mobile App🤖 AI Applications

TripGenie

An AI-powered travel planner that generates personalized itineraries, recommends destinations, suggests accommodations, and provides intelligent travel assistance.

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Benchmark Results & Metrics

Generation Time

<2.5s

Route Efficiency

+18%

User Retention

4.8/5.0

Offline Cache

Enabled

01 // Overview

TripGenie is a cross-platform mobile application that simplifies trip planning by generating hyper-personalized itineraries. It aggregates geographical, seasonal, and budgeting constraints using LLMs.

02 // The Problem

Traditional travel planning takes hours of manual searching. Existing tools provide generic recommendations that do not align with individual user pacing, budgets, or interests.

03 // System Architecture

Geospatial Route & Itinerary Planner

React Native mobile client sends user trip preferences to a FastAPI backend. A Gemini-based planner queries open APIs for destination verification, compiling a structured multi-day itinerary.

ARCHITECTURAL DATA FLOW:User Inputs -> FastAPI -> Gemini API -> Constraint Checker -> Geocoded Map Points -> Expo App

Architecture Components:

Expo Mobile UI

FastAPI Itinerary Orchestrator

Gemini Constraint Solver

Geocoding & Map Integration

04 // Implementation

Implemented the mobile interface using React Native and Expo. The backend incorporates asynchronous tasks to parallelize destination fetching and weather checks before compiling final recommendations.

05 // Execution Workflow

11. User inputs destination, dates, budget, and interests.

22. System queries regional travel conditions.

33. Gemini generates optimized schedule.

44. Locations are mapped using Google Maps.

55. Interactive itinerary is stored locally for offline access.

06 // Technology Stack

Mobile UI

React NativeExpoTypeScript

Backend & Services

FastAPIGemini AIGoogle Maps API

07 // Technical Challenges

⚠️Handling unstable network connections on mobile clients during long-running itinerary generation.

⚠️Formatting multi-day itineraries dynamically within a responsive layout.

Lessons Learned

•Client-side optimistic updates keep the UI responsive while waiting for the LLM payload.
•Providing specific schema blueprints avoids LLM formatting failures.

Future Improvements

•Adding real-time group collaboration so multiple users can edit an itinerary concurrently.
•Integrating flight and hotel booking affiliate APIs.