Every evaluation task flows through a purpose-built ML pipeline that decides the optimal combination of automated metrics and human judgment.
API, SDK, or UI. Agent traces, model outputs, content.
Hallucination, coherence, safety, toxicity. Instant signals.
Smart orchestrator assigns auto, human, or hybrid eval.
Certified humans + LLM-as-Judge score against rubrics.
Weighted consensus. Confidence scores. Actionable results.
Every layer of the EvalML stack is purpose-built for AI evaluation. From embedding-based similarity detection to multi-model consensus, the infrastructure compounds in capability with every evaluation processed.
Automated metrics return in under 200ms for real-time CI/CD integration.
Auto-eval alignment with expert human evaluators across all domains.
No single-model bias. Multiple LLMs cross-validate every automated judgment.
Human eval data feeds back to improve auto-eval calibration in real time.
Multi-model evaluation using Claude, GPT-4, and Gemini as automated judges. Custom rubric prompts, cross-model consensus, bias detection. No single-model dependency.
Embedding-based factual grounding against source documents. Detects fabricated claims, unsupported assertions, and confident-but-wrong outputs with 96% precision.
Multi-axis toxicity, bias, and content safety classification. OWASP LLM Top 10 coverage. Red team playbook integration. Continuous monitoring for production models.
ML-powered orchestrator analyzes task complexity, domain, and confidence thresholds to route each evaluation to the optimal mix of automated metrics and human experts.
Measures logical flow, argument structure, and contextual relevance. Goes beyond surface-level grammar to assess whether AI outputs are genuinely coherent and useful.
Continuously aligns automated scores with expert human judgments. Tracks inter-annotator agreement and auto-adjusts model weights to maintain >0.85 IAA consistency.
The EvalQA Python SDK gives you programmatic access to the full ML pipeline. Run automated evals in CI/CD, submit hybrid evaluations, and get results via webhook — all from your existing workflow.
The orchestrator isn't a simple router — it's an ML system trained on millions of evaluation tasks. It knows when automated metrics are sufficient, when human judgment is critical, and how to combine both for optimal cost and quality.
| ML Capability | EvalML | Galileo | Arize | Braintrust | DeepEval |
|---|---|---|---|---|---|
| LLM-as-Judge (multi-model) | ✓ 7 models | 2 models | Limited | 1 model | Yes |
| Hallucination detection | ✓ Embedding-based | Yes | Basic | No | Prompt-based |
| Human + auto hybrid | ✓ Core | Auto only | Auto only | Auto only | Auto only |
| Smart routing / orchestration | ✓ ML-powered | Manual | N/A | N/A | N/A |
| Agent trajectory eval | ✓ Step-by-step | Partial | Traces only | Basic | No |
| Continuous calibration | ✓ Human-in-loop | No | No | No | No |
| Python SDK | ✓ Full | Yes | Yes | Yes | Yes |
We replaced three separate eval tools with EvalML's pipeline. Hallucination detection alone caught issues our previous stack missed entirely. The hybrid routing means we only pay for human eval when it actually matters.