Our Customers

“Cleanlab accurately detects AI issues and scales to real-world enterprise workloads. After a successful pilot at Google, it became a go-to for high-quality AI development.”

“Cleanlab helped us make our AI models more efficient and reliable, improving customer experience while reducing manual effort.”

“With Cleanlab, we could uncover hidden issues caused AI mistakes and address how to make our models more trustworthy and production-ready.”

“Cleanlab helps identify hidden root cause issues that can lead to AI mistakes, especially critical in regulated domains like anti-money laundering.”

“At PwC, we're embedding AI into real-world workflows. Cleanlab scores each LLM response for trust and makes fallback responses easy to automate and integrate.”

“We used Cleanlab to detect issues and improve our RAG systems. Both our technical and non-technical teams found it intuitive, and it helped us save time while improving results.”

“Tencent's work in medical imaging demonstrated how Cleanlab can extract value from low-quality data, improving model performance in complex healthcare scenarios.”

“Using Cleanlab, we improved the accuracy of our disaster classification system to 85%, strengthening the reliability of predictions in critical real-world scenarios.”

“The research behind Cleanlab sets a strong foundation for building more trustworthy AI systems.”

“Cleanlab provides a robust platform for monitoring and guardrails that helps teams build and operate more reliable AI systems.”

“What stood out to me about Cleanlab is that it's not just smart in theory, it actually works in practice.”

“Cleanlab helped us scale across tens of millions of clinical data points while improving model reliability for patient-facing applications.”

“At Statistics Canada, we work with complex and imperfect real-world data. Cleanlab presents a promising way to improve AI accuracy in complex government data workflows.”

“Cleanlab helps us catch issues early, before they turn into downstream AI mistakes. We're making it a standard part of our workflow to ensure more reliable outputs for our clients.”

“Cleanlab's founders helped launch the data-centric AI movement by showing how errors in real-world datasets distort model performance.”