The Problem: Static AI Benchmarking Falls Short

• Traditional benchmarks use static tests that don't reflect real-world AI usage
• Developers and researchers lack dynamic, interactive evaluation tools
• Result: Incomplete insights lead to suboptimal AI model selection and deployment
• Cost: Increased development time, poor model fit, and missed performance issues