Benchspan

{"problem_it_solves": "Five problems: 1) Benchmarks require custom interface glue code 2) Running benchmarks takes hours/days sequentially 3) Failures are expensive with no resume capability 4) Results lack reproducibility across machines/configs 5) Results disappear into disconnected spreadsheets/CSVs", "target_customer": "AI agent developers and teams who need to evaluate and track performance of their AI agents", "use_cases": ["AI agent performance evaluation", "Benchmarking agent improvements over time", "Team-wide benchmark result sharing", "Reproducible benchmarking across environments"], "differentiator": "One-time onboarding then every benchmark run is fast, reproducible, and shared with the team", "why_now": "Current benchmarking is slow, expensive, fragile, and impossible to collaborate on - teams are bottlenecked by evaluation velocity", "traction": {"notable_metrics": "28 benchmarks available in library"}}