From Idea to Architecture: An AI-Powered Food Discovery Platform Development

The goal of this project was simple: help people discover new places, share honest opinions, and connect with others who enjoy the same kinds of spots.

USA

Country
2 years

Duration
8 developers

Team

About the Company

Our client is a US-based startup located in Seattle, Washington. Its goal was to build a niche social platform focused on restaurants, cafés, and similar places.

The product was led by a product owner with over 10 years of experience at Microsoft. He helped define what features to build first and how to prepare them for a real city launch.

Their main idea was to create a space where people could discover local spots, share experiences, and connect with others who have similar food preferences.

The company planned to launch the product city by city, starting with Seattle and nearby areas, and later expand to other US cities.

Challenges: API Integration, AI Implementation & More

Google Places API Integration

Restaurant data had to come from Google, which required working with paid APIs and complex data structures.
High API Costs

Discovering restaurants through Google Places could become very expensive if not controlled carefully.
Limited Launch Geography

Discovering all cities at once was not realistic, especially at the early stage of the product.
AI Data Enrichment

Raw restaurant data was not enough for personalization and needed additional AI-generated tags.
Personalized Recommendation Feed

The feed had to show different content for each user based on location and preferences.
AI Performance Delays

AI recommendations take time to generate, which can negatively affect user experience.
User Wait Time

Long loading times risked losing user engagement.

Our Solutions

Selective Google Places Discovery

Restaurants were fetched only for chosen cities instead of the entire country or world.
Client-Controlled City Selection

The client decided which cities to launch in, starting with Seattle and nearby areas.
API Cost Optimization

Data requests were carefully structured to get maximum value from each Google API call.
AI-Powered Data Enrichment

Each restaurant was processed by AI to add tags and attributes for better recommendations.
AI-Based Personalization

Recommendations were generated based on user profiles, preferences, and location.
Streaming AI Pesponses

Results were sent to users as soon as AI produced them, without waiting for full completion.
Caching Layer

Frequently used results were cached to reduce processing time and repeated AI calls.

How Does the System Work: A Brief User Journey

When joining the platform, user creates a profile that shows what kind of dining experiences they’re into. They can follow friends, leave reviews with photos and ratings, and organize their favorite spots into lists.

The feed is personalized, and it shows restaurants nearby that match users’ preferences. The search lets them find specific dishes, discover other users with similar tastes, or browse lists other people have created.

Planning to meet up? Users can create an event, choose a restaurant, and invite their friends. The platform sets up a chat automatically so everyone can talk about plans. Other people don’t even need to be on the platform yet, and they can still join the conversation.

Behind everything, AI is quietly learning what users like, processing restaurant information, and getting better at suggesting places they’ll actually want to try.

Key Deliverables

Mobile Apps

For iPhone and Android that feel smooth and natural to use
A Recommendation Engine

This engine learns what each person likes for a more personalized approach
Google Places Integration

Smart integration with Google Places that keeps costs reasonable
Event Planning Feature

Event planning capabilities with built-in group chats
Advanced Search

Search that covers restaurants, dishes, other users, and curated lists
Personal Profiles

Personal profiles that track preferences and connections
Reviews

A review system with photos, videos, ratings, and custom tags
Fast Loading

Fast loading with real-time streaming for AI suggestions

Technologies

Vercel AI SDK
React Native
Expo
Mapbox
Nest.js
Google Gemini
AWS

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Summary: Building Food Discovery Platform From Scratch

This project resulted in a production-ready mobile platform focused on restaurant and café discovery. It is planned to launch initially in Seattle and nearby cities.

The app brings together social features and AI-powered suggestions, so users can discover new places, leave reviews, save lists, and plan events with built-in chats.

One of the biggest improvements was speed. At the start, the app needed almost a full minute to show personalized recommendations. That was too slow for everyday use. After changing how results were delivered and stored, recommendations now appear in under five seconds. This made the app feel much more responsive and pleasant to use.

Overall, the project shows a practical way to build an AI-based consumer product that works well in real life. It balances personalization with fast performance and keeps costs under control, which is key for any product running in production.