general

Pitchside Ai

Problem

The problem

High Latency: Page load times averaged 4.5 seconds during live match windows. Conversion Friction: A clunky, non-responsive mobile UI led to a 60% bounce rate among users checking stats on the go. Scalability Bottlenecks: During peak match times, the server frequently crashed, resulting in significant revenue loss during high-traffic broadcast windows.

Goals

What needed to happen

Reduce Latency: Bring Largest Contentful Paint (LCP) under 1.2 seconds. Mobile First: Rebuild the interface to prioritize mobile users, who account for 85% of traffic. Infrastructure Resilience: Implement a microservices architecture to ensure 99.99% uptime during peak concurrent load. Conversion Optimization: Improve the lead-to-subscription funnel via a simplified, high-converting UX.We transitioned Pitchside AI from a legacy monolithic stack to a modern, serverless-first architecture: Frontend: Migrated to Next.js 15 for superior SSR (Server-Side Rendering) and ISR (Incremental Static Regeneration). Backend: Decoupled data processing into a Node.js/TypeScript microservices cluster using Redis for sub-millisecond caching of live sports data. Design System: Adopted a "Mobile-First" component library, focusing on touch-friendly interactive charts and minimal friction.

Strategy

The strategy

We transitioned Pitchside AI from a legacy monolithic stack to a modern, serverless-first architecture: Frontend: Migrated to Next.js 15 for superior SSR (Server-Side Rendering) and ISR (Incremental Static Regeneration). Backend: Decoupled data processing into a Node.js/TypeScript microservices cluster using Redis for sub-millisecond caching of live sports data. Design System: Adopted a "Mobile-First" component library, focusing on touch-friendly interactive charts and minimal friction.

Execution

What we shipped

Month 1 (Audit & Design): UX overhaul and database schema optimization. Month 2 (Engineering): Migrating data pipelines to edge computing to place data closer to the UK-based user base. Month 3 (Optimization): Implementing AI-driven predictive caching to pre-load match data before the user requests it.

Results

The result

Latency: LCP reduced from 4.5s to 0.8s. Conversion: Paid subscription conversion rate increased by 42%. Performance: Achieved a consistent Core Web Vitals "Pass" across all templates. Resilience: Zero downtime recorded during the most recent FA Cup Final peak traffic surge.

Metrics

4.5s

Avg. LCP

0.8s

60%

Mobile Bounce Rate

5,000 req/s

Concurrent Load Capacity

50,000+ req/s

2.1%

Conversion Rate

3.0%

Timeline

1

Discovery & Architecture Blueprint (Weeks 1-2)

Development & Integration (Weeks 3-10)

UAT & Performance Stress Testing (Weeks 11-12)

Live Migration & Monitoring (Week 13)

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