Measure What
Matters in AI
End-to-end evaluation pipelines for language models. Automate benchmarks, compare models, and track quality regressions across every deployment.
How It Works
From evaluation design
to production monitoring
A unified workflow for teams who take model quality seriously.
01
Define Evaluations
Specify eval suites in YAML or through the UI. Set scoring criteria, test sets, and quality thresholds per task type.
02
Run Benchmarks
Execute evaluations at scale across any model or API endpoint. Parallel runs complete in minutes, not hours.
03
Compare Models
Side-by-side analysis across all benchmarks. Statistical significance testing built in — no cherry-picked results.
04
Track Quality
Monitor production quality over time. Get alerted on regressions before they reach users. Close the loop automatically.
Live Dashboard Preview
See how your models
actually perform
Track accuracy, reliability, and regressions across every benchmark that matters.
| Model | MMLU | HumanEval | GSM8K | ARC-C | TruthfulQA | Avg |
|---|
Capabilities
Everything you need to evaluate
AI systematically
Built for teams who ship models to production and need to know they work.
Automated Test Suites
Define evaluation suites in YAML. Run them on every model update. Get reproducible results in minutes, not days.
Cross-Model Comparison
Side-by-side performance analysis across any combination of models, benchmarks, and prompt strategies.
Regression Detection
Automatic alerts when model performance drops. Statistical significance testing built in — no false alarms.
Custom Benchmarks
Build domain-specific evaluation sets. Import your own test data. Define custom scoring rubrics for any task type.
CI/CD Integration
Plug into your deployment pipeline. Block releases that fail quality gates. Works with GitHub Actions, GitLab CI, Jenkins.
Real-time Monitoring
Production quality dashboards. Track latency, accuracy, and cost per query across all endpoints in real time.
Methodology
Built on rigorous evaluation science
We don't just run tests — we make sure the tests are worth running.
01
Reproducibility
Every evaluation run is versioned, logged, and reproducible. Share configurations, not just results. Anyone on your team can replicate any score from any point in time.
02
Statistical Rigor
Confidence intervals, significance testing, and effect size reporting on every comparison. We surface what's meaningful, and clearly mark what isn't. No cherry-picked numbers.
03
Transparency
Open methodology documentation, public benchmark specifications, and fully auditable scoring pipelines. Every score has a traceable source.
Get Started
Ready to systematize
your AI evaluation?
Join the teams who ship with confidence. Get a guided demo of EvalEngine, BenchmarkHub, and QualityTracker.