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Case Study

Setwise

AI-powered football card marketplace

Built for Personal Project
30 days
Personal Project
React
TypeScript
Node.js
PostgreSQL
Mastra
OpenAI
MCP

7,000+

Cards Catalogued

£2M+

Sales Tracked

13

AI Tools

01

The Problem

I collect football cards. Yes, really. The market data was everywhere - eBay, forums, price guides. Fragmented. Impossible to track. I wanted something that didn't exist: real-time market intelligence without hiring analysts.

02

The Solution

Built an autonomous market intelligence system. AI identifies popular cards, aggregates comprehensive data (trends, investment analysis, player profiles), and caches results for instant API responses. 13 custom AI tools working together. What would take analysts hours runs in the background daily.

03

Key Features

Autonomous Market Intelligence

AI agents that run daily, identifying trending cards, aggregating comprehensive market data, and caching results for instant API responses. What would take analysts hours happens automatically in the background.

13 Custom AI Tools

Specialized tools working together: trend analysis, investment scoring, player profile generation, price prediction, rarity assessment, and more. Orchestrated through Mastra for reliable multi-agent coordination.

Real-Time Price Tracking

Aggregates sales data from eBay and other sources. £2M+ in tracked transactions. Historical price charts, market trends, and investment indicators for every card in the catalogue.

Sub-50ms Response Times

Intelligent caching layer ensures instant responses. Connection pooling, circuit breakers, and health monitoring provide production-grade reliability. 70% cost reduction vs. manual analysis.

Comprehensive Card Catalogue

7,000+ football cards catalogued with player profiles, card variants, rarity scores, and market valuations. Searchable, filterable, and continuously updated by AI agents.

04

Gallery

Setwise screenshot 1
Setwise screenshot 2
Setwise screenshot 3
Setwise screenshot 4
Setwise screenshot 5
05

The Results

7,000+ cards catalogued. £2M+ in tracked sales. Sub-50ms response times. 70% cost reduction vs. manual analysis. I don't just tell people how to build. I build for myself too. Same methodology. Same standards. Same speed.

06

The Build Story

Old me would have hired a team. Spent 6 months. £50K+.

New me: I'll just build it myself. 30 days. Solo.

The hardest part was the AI orchestration - getting 13 different tools to work together reliably. Connection pooling, circuit breakers, health monitoring. AI agent infrastructure needs the same reliability patterns as traditional microservices.

That's what I learned. And that's what I apply to client builds now.

07

Build Timeline

Week 1

Core Platform

Card catalogue, search, filtering, PostgreSQL database architecture

Week 2

AI Integration

Mastra framework, 13 custom AI tools, market intelligence agents, trend analysis

Week 3

Infrastructure

Connection pooling, circuit breakers, health monitoring, caching layer

Week 4

Launch

Performance optimization, sub-50ms responses, production deployment

08

Integrations

OpenAI
Mastra
eBay API
PostgreSQL
MCP

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