Detect – Check every response generated by AI.

Identify AI mistakes as they happen—whether it’s from a hallucination, missing context, or knowledge gap. Cleanlab ensures trustworthy answers with a clear, actionable score.

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Diagram of Cleanlab’s AI data quality platform

Integrations

Arize
Langfuse
Langtrace
mlFlow
NVIDIA

Detect AI response issues.

Even the best-engineered AI systems aren’t perfect. LLMs, search systems, and knowledge bases all contain uncertain elements that lead to unreliable or unhelpful responses.

Hallucinations

Your AI makes up an answer regardless of whether our not the AI agent found the right context.

User asks when iPadOS 18 support ends. AI Agent replies with a specific date, Dec 31, 2029. A red warning label below the response says ‘Hallucination’.

Wrong Context

Your knowledge base has the answers, but your AI agent can’t find it, resulting in an incorrect response.

User asks if they can return a pack of opened toothbrushes. AI Agent replies that products can be returned within 30 days of purchase. No warning label is shown. A red warning label below the response says ‘Wrong Context’.

Knowledge Gaps

Your AI agent returns ‘I don’t know’ answers when your knowledge base doesn’t have the context.

User asks if they can pay their monthly bill using Apple Pay. AI Agent responds that it couldn’t find any information. A red warning label below the response says ‘Knowledge Gap’.

Escalate untrustworthy AI responses at the right time.

Trustworthiness scores help your AI agents decide when to respond confidently and when to hand off to a human or fallback flow.

Screenshot of a customer service chat showing Angela asking about returning earrings she doesn't like. An AI Agent responds that unworn earrings in original packaging may be returnable, but this is flagged as incorrect with a trustworthiness score of 0.53. A correction note explains that earrings cannot be returned for hygiene reasons per the Free Returns Policy, regardless of condition. The issue is detected and escalated to a human operator.

Proven best at detecting AI hallucinations.

Hallucination Detection Effectiveness by Method

Horizontal bar chart showing AUROC scores for different AI evaluation metrics. Cleanlab leads with 0.89 (dark bar), followed by LLM-as-a-judge at 0.78, RAGAS Faithful at 0.70, Hallucination at 0.61, G-Eval Correctness at 0.56, and RAGAS Answer Relevancy at 0.53. All bars except Cleanlab are shown in light gray, with scores displayed on the right side of each row.Default LLM set to gpt-4o-mini. Benchmarks are the averaged results evaluated over four datasets: CovidQA, DROP, FinanceBench, and PubmedQA. Learn more

One metric built from proven and tested operations.

Multiple common operations are combined with proprietary methods into a single, cost-efficient, and reliable metric with a clear explanation.

Diagram illustrating methods used by an LLM to detect uncertainty in its responses. Center icon represents detection using Cleanlab. Surrounding labels include: ‘Self-reflection – LLM evaluates the response and assesses its confidence’; ‘Proprietary Aleatoric Uncertainty Methods’; ‘Consistency – Generates multiple plausible responses and checks for contradictions’; ‘Probabilistic Measures – Analyzes word likelihood from LLM’s auto-regressive token probabilities’; and ‘Proprietary Epistemic Uncertainty Methods’.

Easy to integrate.

Just a few lines of code gets you started with Cleanlab. See what it can do to improve your AI agent’s performance and reliability.

Real-time guardrails, optimized for accuracy in production.

  • Delivers the highest accuracy across all latency and cost profiles with 15+ supported evaluation models and 5 quality settings.
  • Pre-optimized to save engineering time. Choose a faster models for lower latency or high quality settings for better accuracy.
  • Optimize for response times as low as 300 ms.
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