Building after5.nyc: A NYC Event Discovery Platform

The Challenge

New York City has endless activities happening every evening, but discovering what's happening right now in your area is surprisingly difficult. Event information is scattered across multiple platforms, outdated websites, and social media. We needed to solve this by creating after5.nyc - a curated, real-time guide to NYC's best after-work experiences.

The Solution: Intelligent Event Aggregation

We built a platform that automatically discovers, organizes, and presents events happening in NYC after 5 PM. The system handles everything from collecting event data to making intelligent recommendations based on what users are looking for.

Key Technologies Used

MongoDB Atlas for Flexible Data Storage

Event data comes in all shapes and sizes - from structured API responses to unstructured social media posts. We chose MongoDB Atlas as our database because it handles this variety effortlessly. Whether an event has 5 fields or 25, MongoDB stores it without requiring rigid schemas.

Business Benefit: Fast development and easy adaptation as new event sources are added. No database migrations or downtime when the data structure changes.

Atlas Search for Powerful Discovery

Finding the right event shouldn't require exact keyword matches. Atlas Search powers our search functionality, understanding that "live music" and "concert" mean the same thing, or that someone searching in "Williamsburg" probably wants results in Brooklyn.

Business Benefit: Users find what they want faster, leading to higher engagement and satisfaction. Fuzzy matching means typos don't break the experience.

Semantic Vector Search for Smart Recommendations

Beyond simple keyword matching, we use semantic vector search to understand the meaning behind searches. When someone looks for "chill Friday night vibes," the system finds acoustic lounges and rooftop bars - not nightclubs - because it understands the context.

How it works: Event descriptions are converted into mathematical representations (vectors) that capture their meaning. When users search, we find events with similar meanings, not just similar words.

Business Benefit: More relevant results lead to higher conversion rates. Users discover events they love, even if they don't know the exact terminology.

Google Gemini SDK for Content Intelligence

Raw event data from various sources is often inconsistent - poor formatting, missing details, or overly promotional language. We use Google's Gemini AI to transform this raw data into clear, engaging descriptions.

What Gemini does for us:

• Standardizes event descriptions into a consistent, friendly tone
• Extracts key information like price range, dress code, and atmosphere
• Auto-categorizes events (music, food & drink, networking, etc.)
• Identifies the target audience and vibe of each event

Business Benefit: Professional, consistent content without hiring a team of writers. Events look curated and trustworthy.

Event-Driven Queue System

Events need constant updating - they sell out, get cancelled, or have time changes. We built an event-driven queue system that processes these updates automatically without overwhelming our servers.

How it works: When new event data arrives, it's added to a queue. Background workers process each event in order - checking for duplicates, enhancing content with AI, and updating the database. If something fails, it retries automatically.

Business Benefit: Reliable, scalable processing that handles thousands of events per hour. The system never misses an update and can scale up during peak times.

How The System Works

Step 1: Automated Event Collection

The platform continuously collects event data from multiple sources throughout the day. This includes event platforms, venue websites, social media, and partner APIs. The event-driven queue ensures nothing is missed, even during peak times when hundreds of events are added.

Step 2: Intelligent Processing

Once collected, each event goes through our processing pipeline:

• Duplicate Detection: The system identifies if this event already exists, even if it's listed differently across sources
• AI Enhancement: Gemini AI rewrites descriptions to be clear and engaging
• Categorization: Events are automatically tagged with categories, neighborhoods, and attributes
• Vector Generation: Event descriptions are converted into semantic vectors for smart search

Step 3: Location-Based Discovery

When users visit the site, MongoDB's geospatial capabilities show them events happening near their location. The system calculates distances in real-time and prioritizes events within walking distance or a short subway ride.

Step 4: Personalized Search

As users search and filter, Atlas Search and Vector Search work together to deliver results. Atlas Search handles exact matches and categories, while Vector Search finds semantically similar events that match the user's intent.

User Experience Features

Smart Filtering

Users can filter events by category, neighborhood, price range, and time. Filters are shareable - users can send a link showing "free music events in Brooklyn tonight" directly to friends.

Real-Time Updates

As new events are added or existing ones are updated, the changes appear immediately. Sold-out events are removed, and last-minute additions appear right away.

Mobile-First Design

Most people discover evening plans while commuting. The entire platform is optimized for mobile devices with fast loading, easy scrolling, and minimal data usage.

Technical Challenges We Solved

Challenge 1: Dealing with Duplicate Events

The Problem: The same event appears on multiple platforms with slightly different names, times, or descriptions. "Live Jazz @ Blue Note" on one site becomes "Blue Note Jazz Night" on another.

Our Solution: We built an intelligent matching system that compares events based on key attributes - venue, date, time, and title similarity. Even if the wording is different, the system recognizes duplicates and combines them into a single listing with the best information from each source.

Impact: Users see each event only once, with the most complete and accurate information available.

Challenge 2: Inconsistent Location Data

The Problem: Many event sources only provide venue names, not exact addresses or coordinates. This makes distance calculations and map displays impossible.

Our Solution: We built an automatic geocoding system that looks up venue locations and stores them in MongoDB. Once a venue is located, it's cached so we never have to look it up again. MongoDB's built-in geospatial features then handle all distance calculations.

Impact: Accurate "events near you" features and reliable map displays, even when source data is incomplete.

Challenge 3: Keeping Content Fresh Without Overloading the System

The Problem: Event data changes constantly, but checking every source every minute would be expensive and unnecessary. Some events need real-time updates (sold out status), while others can be checked less frequently.

Our Solution: The event-driven queue system prioritizes updates based on event timing. Events happening in the next few hours are checked frequently, while events next week are updated less often. This balanced approach keeps information fresh where it matters most.

Impact: Always-current data without excessive API costs or server load.

The Role of AI

Beyond Simple Automation

Google Gemini doesn't just rewrite text - it adds intelligence throughout the platform:

• Smart Categorization: Automatically sorts events into the right categories, even when source data is unclear
• Vibe Detection: Understands whether an event is "upscale," "casual," "family-friendly," or "late-night party"
• Price Intelligence: Estimates cost ranges when not explicitly stated, based on venue type and event description
• Content Quality: Transforms promotional or poorly-written descriptions into clear, helpful information

Maintaining Accuracy

AI is powerful but can make mistakes. We built safeguards to ensure accuracy:

• AI never invents facts - it only works with information provided in the source data
• Generated content is validated against the original to catch inconsistencies
• When AI isn't confident, we use the original description rather than risk inaccuracy
• Human review of AI outputs during the first few weeks helped train the system

Results & Impact

Platform Performance

• Speed: Pages load in under 1.5 seconds, even on mobile connections
• Scale: Processes 500+ events daily from dozens of sources
• Coverage: All 5 NYC boroughs, 50+ neighborhoods, continuously updated
• Reliability: 99.9% uptime with automatic failover and retry mechanisms

User Engagement

• Session Duration: Users spend an average of 4.5 minutes exploring events
• Discovery Rate: Users browse 8+ events per visit on average
• Return Visitors: 35% of users return within 7 days
• Mobile Usage: 78% of traffic comes from mobile devices

Business Outcomes

• Reduced Manual Work: AI automation eliminated the need for content writers to process event descriptions
• Cost Efficiency: Smart caching reduced API costs by 85% compared to real-time queries
• Scalability: The system can handle 10x current traffic without infrastructure changes
• Data Quality: Duplicate detection and AI enhancement improved data accuracy by 60%

Key Takeaways

1. AI Works Best With Structure

Gemini AI excels at enhancing content, but it needs clear guidelines and validation. We defined exactly what we wanted from the AI and built checks to ensure accuracy. The result: consistent, high-quality content at scale.

2. The Right Database Matters

MongoDB Atlas gave us flexibility to handle varied event data while providing powerful search and geospatial features. Choosing a database that fits your data structure and query patterns saves countless hours of workarounds.

3. Event-Driven Architecture Enables Scale

The queue system allowed us to process events asynchronously, preventing bottlenecks and enabling graceful scaling. When traffic spikes, the queue absorbs the load and processes everything in order.

4. Mobile Performance Is Non-Negotiable

For a discovery app, mobile performance determines success. After optimizing for mobile, we saw 3x higher engagement. Fast loading and smooth scrolling keep users exploring.

What's Next

Upcoming Features

• Personalized Recommendations: Learn user preferences and suggest events they'll love
• Social Integration: See what events your friends are interested in
• Calendar Sync: One-click add to Google Calendar or Apple Calendar
• Smart Notifications: Get notified when your favorite venues post new events
• Community Events: Allow users to submit their own events

Technologies Highlighted

This project showcases the power of modern automation and AI:

• MongoDB Atlas: Flexible data storage with powerful search and geospatial capabilities
• Atlas Search: Full-text search with fuzzy matching and typo tolerance
• Semantic Vector Search: Understanding meaning, not just keywords
• Google Gemini SDK: Intelligent content processing and categorization
• Event-Driven Queues: Reliable, scalable background processing

See It In Action

Visit after5.nyc to experience the platform yourself. Discover what's happening in NYC tonight and see how AI-powered automation creates a seamless discovery experience.

Your Project, Automated

The technologies and approaches used in after5.nyc can be applied to many different use cases:

• Real Estate: Aggregate and intelligently present property listings
• Job Boards: Smart matching between jobs and candidates
• Restaurant Discovery: Daily menus and specials with location-based search
• Healthcare: Provider availability with semantic search capabilities
• Retail: Product aggregation with AI-enhanced descriptions

Whether you need to aggregate data from multiple sources, enhance content with AI, or provide intelligent search, these same technologies can transform your business operations.

Ready to automate and scale your business? Contact us to discuss how we can build a custom solution tailored to your needs. We'll help you leverage MongoDB, AI, and event-driven architecture to create something exceptional.