Cleanlab AI focuses on improving the reliability of generative AI by automatically detecting and correcting AI hallucinations, ensuring outputs are safe, compliant, and trustworthy.
Cleanlab AI mainly tackles the 'hallucination' problem of generative AI (e.g., large language models), i.e., producing inaccurate or fabricated content, and aims to enhance the reliability and credibility of AI outputs through automated detection and remediation.
The Cleanlab Remediate platform provides real-time error detection and benchmarking, automated hallucination remediation, domain-expert interventions for fixes, and comprehensive AI agent monitoring with prompt adherence verification.
Its clients include BBVA, Tencent, Amazon, Google, Oracle, Red Hat, iRobot, Databricks, Tesla, JPMorgan Chase, Microsoft, and more, spanning startups to large tech and financial firms.
Through the platform's human-in-the-loop collaboration, domain experts can intervene to guide and fix. Case studies show production AI agent accuracy rising from 72% to 90% after intervention.
Cleanlab AI goes beyond merely cleaning training data; its Cleanlab Remediate platform emphasizes real-time monitoring, hallucination detection, and immediate fixes for deployed generative AI in production, i.e., AI reliability operations.
Users can request a product demo on its official website to learn more about the specific features and services of its AI reliability solutions.