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Comprehensive AI Evaluation

Automated evaluators, benchmarking, red-team testing, compliance support, and audit trails. Everything you need to evaluate AI systems and meet regulatory requirements.

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Pre-Built Evaluation Datasets

Start evaluating in seconds with our curated dataset library. One-click testing for common AI use cases—no data preparation required.

  • Customer support conversation datasets
  • RAG & knowledge retrieval test suites
  • Safety & red-team attack libraries
  • Multi-turn dialogue benchmarks
  • Domain-specific datasets (legal, medical, finance)
  • Adversarial edge case collections

Dataset Library

Customer Support QA

2,500 cases

Popular

RAG Factuality Benchmark

1,800 cases

Popular

Red-Team Attack Suite

850 cases

Security

Multi-Turn Dialogue

1,200 cases

New

Medical QA Evaluation

950 cases

Industry

Automated Evaluators

Multi-dimensional scoring with confidence intervals. Understand not just what failed, but why.

Relevance

Measures how well responses address the user's query and context.

Signal types:

Query alignmentContext utilizationCompleteness

Factuality

Assesses accuracy of claims against source documents and knowledge.

Signal types:

Source attributionClaim verificationContradiction detection

Hallucination Risk

Detects fabricated information not grounded in provided context.

Signal types:

Unsupported claimsInvented entitiesFalse citations

Robustness

Tests consistency across paraphrased inputs and edge cases.

Signal types:

Input variation stabilityEdge case handlingFormat consistency

Benchmarking & Regression Detection

Track performance over time with versioned datasets. Catch regressions before they reach production.

  • Version-controlled test datasets
  • Historical performance tracking
  • Automated regression alerts
  • Baseline comparison reports
  • Statistical significance testing
  • Trend visualization dashboards

Performance Over Time

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Latest: v10+9% vs baseline

Red-Team Testing

Adversarial testing to surface risk signals. Categorized findings with severity levels and evidence.

Prompt Injection

Critical

Tests for susceptibility to malicious prompt manipulation.

Jailbreak Attempts

Critical

Evaluates resistance to attempts to bypass safety guidelines.

Data Extraction

High

Probes for potential leakage of training data or system prompts.

Bias & Toxicity

High

Identifies harmful, biased, or inappropriate outputs.

Adversarial Inputs

Medium

Tests behavior with malformed, ambiguous, or edge-case inputs.

Consistency Attacks

Medium

Checks for contradictory responses to semantically similar queries.

Regulatory Compliance Support

Generate documentation and evidence for EU AI Act, SOC2, ISO 27001, and industry-specific requirements.

EU AI Act

Risk assessment templates, conformity documentation, and transparency reports aligned with EU AI Act requirements.

SOC2 Type II

Evidence collection for AI system controls, monitoring documentation, and audit-ready reports.

ISO 27001

AI risk management documentation, security control evidence, and continuous monitoring reports.

HIPAA

Healthcare AI compliance documentation, PHI handling assessments, and audit trails for medical AI systems.

Financial Services

Model risk management (SR 11-7), fair lending assessments, and explainability documentation.

Custom Frameworks

Build custom compliance templates for your industry-specific regulatory requirements.

BlanEval provides compliance tooling and documentation — not legal certification. Consult legal counsel for regulatory decisions.

Evidence Report

Evaluation SummaryRun #247 • 2024-01-15
Model:GPT-4 Turbo
Dataset:v3.2.1
Test Cases:1,247
Duration:23m 14s

Audit Trail & Reporting

Every evaluation run is logged with full provenance. Export evidence reports for compliance audits and stakeholder review.

  • Complete run history with timestamps
  • Configuration snapshots for reproducibility
  • Detailed finding breakdowns
  • PDF and CSV export formats
  • Compliance-ready documentation
  • Shareable report links
  • API access to all historical data

Built for MLOps

Integrate evaluation into your existing ML pipelines. Automate quality gates and catch regressions before deployment.

GitHub Actions

Run evaluations on every PR or commit

GitLab CI

Integrate with your GitLab pipelines

Jenkins

Add evaluation stages to Jenkins jobs

Airflow

Orchestrate evaluations in DAG workflows

Kubeflow

Run evaluations in ML pipelines

MLflow

Track experiments and log evaluation metrics

Weights & Biases

Sync results to W&B dashboards

REST API

Trigger from any system via API

Example: GitHub Actions Integration

Add BlanEval to your CI pipeline with a single workflow file. Fail builds when quality gates aren't met.

- name: Run BlanEval
  uses: blaneval/evaluate@v1
  with:
    dataset: customer-support-qa
    model: ${{ env.MODEL_ENDPOINT }}
    fail-on: critical-findings

Under the Hood

Built for engineering teams who care about reliability and reproducibility.

Deterministic Runs

Reproducible evaluation results with fixed seeds and versioned configurations.

Versioned Evaluators

Track evaluator changes over time. Compare results across evaluator versions.

API-First Architecture

Integrate evaluations into your CI/CD pipeline with our REST and Python APIs.

LLM Stack Integration

Works with OpenAI, Anthropic, Google, Cohere, and open-source models.

See BlanEval in action

Schedule a demo to see how BlanEval can help your team ship AI with confidence.

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