What it does
Cleanlab Detect checks AI outputs as they are produced and returns a trustworthiness score with an explanation. It targets three failure types: hallucinations, where the model invents an answer; wrong context, where retrieval misses information the knowledge base actually holds; and knowledge gaps, where the answer is simply not available. Teams use the score as a real-time guardrail, deciding when an agent should answer confidently and when it should escalate to a human or a fallback flow. The site says the score combines several established techniques with proprietary methods into one metric, supports 15+ evaluation models, and offers five quality settings so you can trade speed against accuracy. The site publishes benchmark results for question answering, RAG applications and tool-using agents; these are the vendor's own claims. Integration is described as a few lines of code, with documentation and a demo request available. A banner on the site notes that Cleanlab has been acquired by Handshake AI.
Capabilities
- Trustworthiness score with a clear explanation for each response
- Detects hallucinations, wrong context and knowledge gaps
- Real-time guardrail for escalating to a human or fallback flow
- 15+ evaluation models and five quality settings
- Covers LLMs, RAG systems and tool-using agents
When to use it
- Gating RAG chatbot answers before they reach customers
- Deciding when an AI agent should hand a conversation to a person
- Auditing AI responses to find gaps in a knowledge base
