How BlanEval Works
A systematic approach to AI evaluation and compliance documentation. From dataset definition to regulatory-ready evidence reports in six steps.
Evaluation Flow
Choose Your Dataset
Use our pre-built evaluation datasets for one-click testing, or upload your own custom test cases.
- Pre-built datasets for common use cases
- One-click testing with curated benchmarks
- Import custom data from CSV, JSON, or API
- Version control for all datasets
- Tag and categorize test cases
Pre-built Datasets
Configure Evaluators
Select which quality dimensions to measure. Choose from built-in evaluators or create custom ones for your domain.
- Relevance, factuality, hallucination risk
- Custom evaluator definitions
- Confidence thresholds
- Pass/fail gate criteria
Run Evaluation
Execute automated evaluation across your test cases. Run red-team attacks to surface risk signals.
- Parallel execution for speed
- Deterministic, reproducible runs
- Real-time progress tracking
- Automatic retry on failures
812
Passed
35
Failed
Analyze Failures
Drill into failed cases to understand root causes. Compare against baselines to identify regressions.
- Detailed failure breakdowns
- Side-by-side comparisons
- Regression detection
- Statistical significance testing
Test case #847: Fabricated citation
Test case #923: Off-topic response
Calibrate with Human Review
Validate automated scores with human judgment. Build calibration datasets to improve evaluator accuracy.
- Human-in-the-loop review workflows
- Calibration score tracking
- Evaluator accuracy metrics
- Feedback loop for improvement
Human Review Queue
Export Evidence Report
Generate comprehensive reports for stakeholders and compliance teams. Document findings, scores, and regulatory evidence.
- PDF and CSV exports
- Executive summaries
- Detailed finding logs
- Shareable report links
- EU AI Act documentation
- Compliance-ready templates
evaluation_report_v3.pdf
Generated Jan 15, 2024
Ready to start evaluating?
See how BlanEval can fit into your AI development workflow.