Sponsor Match is a production-grade multi-agent AI system that analyzes sponsorship fit between companies and sports clubs. It demonstrates enterprise-level AI engineering with real complexity—not just prototypes.
Architecture: 5 specialized agents orchestrated by a manager (SearchAgent for multi-query web research, AnalysisAgent for autonomous PDF extraction, FitAgent for 8-factor weighted scoring, SecurityValidator for defense).
Real Complexity: Unlike toy projects that hallucinate, Sponsor Match actually finds and parses financial PDFs from the internet, extracts real numbers (e.g., Nike revenue €46.7B from 4 sources), and normalizes them to standard formats. Web research includes 5 parallel Tavily queries with sentiment analysis and source credibility scoring.
Enterprise Features: Comprehensive logging (26MB+ traces), intelligent caching (98x performance improvement on cached runs), graceful error handling (1 PDF failure ≠ system crash), performance monitoring, and transparent quality metrics that explain why a score is 0.75 (data quality insight, not LLM black box).
Security: 70+ prompt injection attack patterns (English + German) detect system prompt extraction, role override, prompt chaining, encoding attacks, and code execution attempts. IP-based rate limiting (10 requests/min, 100/hour) prevents abuse.
Real-World Value: Makes sponsorship decisions faster and more transparent. Score 0.75 with 82% confidence means something—quality metrics show which data is missing, allowing informed human decisions.
Built with LangChain, LangGraph, Streamlit, pdfplumber, Tavily, OpenRouter. MIT license, GitHub public. Production-ready.
