BenchAI watches your model releases, runs benchmarks continuously, and alerts you when performance drifts. Stop running eval pipelines by hand. Start shipping faster.
Point BenchAI at your GitHub repo, HuggingFace model, or model registry. One line of config.
Pick from 47 pre-built benchmarks (TVR, QVHighlights, VALUE, TVQA, SAM) or add your own eval harness in minutes.
Every new commit triggers an eval run. Results land in your dashboard with regression alerts before you even open your laptop.
Track performance across all model checkpoints over time. Watch metrics shift across every commit. Spot the exact commit that broke performance.
Get notified the moment a new checkpoint drops below your baseline. No more discovering regressions weeks later in a retrospecto.
TVR, QVHighlights, VALUE, TVQA, SAM, ClipBERT and 42 more. All wired up. Add your custom harness in YAML.
See all your models ranked side-by-side. Compare across commits, tags, or branches. Export to CSV or push to your internal wiki.
Run the same benchmark suite across your model family simultaneously. See which architecture wins on your specific task.
Drop a benchai.yaml into your repo. That's it. BenchAI auto-discovers your eval harness and starts running on every push.
Every AI lab has the same problem: benchmarks get run before major releases, not continuously. A model regresses on Tuesday, the team ships on Friday, users notice on Monday. BenchAI closes that gap. It's the CI/CD pipeline your ML infrastructure always needed but never had.
Stop discovering regressions the hard way. Start shipping with confidence, every commit, every checkpoint, every time.