CleanData AI
The Intelligent Data Hygiene Layer for Autonomous GTM Operations
Cleanlab is the industry-leading platform for data-centric AI, built on the foundations of 'Confident Learning' to automatically identify and fix errors in datasets. By 2026, Cleanlab has solidified its position as an essential layer in the AI development stack, particularly for teams fine-tuning Large Language Models (LLMs) and deploying Retrieval-Augmented Generation (RAG) systems. Unlike traditional MLOps tools that focus on model architecture, Cleanlab treats the data as the primary lever for performance, using sophisticated algorithms to detect mislabeled examples, outliers, and near-duplicates across text, image, and tabular data. The technical architecture includes both an open-source library for programmatic data cleaning and 'Cleanlab Studio,' a no-code SaaS environment that automates the training of multiple diagnostic models to score data reliability. This dual approach allows organizations to drastically reduce the manual labor associated with data auditing while simultaneously increasing model accuracy by 10-30% simply by removing noise from the training and evaluation sets. Its integration with major data warehouses like Snowflake and Databricks makes it the go-to solution for enterprise-grade data governance in the generative AI era.
A mathematical framework for identifying label noise based on joint distributions of noisy labels and true labels.
Verified feedback from the global deployment network.
Post queries, share implementation strategies, and help other users.
Unified interface for cleaning text, images, and tabular data simultaneously.
Automatically trains a suite of models to assess the data, rather than requiring the user to specify a model.
Uses specialized NLP models to identify sensitive information within training datasets.
Scores the reliability of LLM outputs and RAG retrieval documents using uncertainty quantification.
Ranks which data points a human should label next based on maximum uncertainty and potential error.
Allows data cleaning to occur directly within the Snowflake warehouse via Snowpark.
Mislabeled reviews (e.g., 5-star ratings with negative text) were degrading model performance.
Registry Updated:2/7/2026
Retrain BERT model on the cleaned data
Radiologists' labeling disagreements were causing high false negatives in a lung cancer detection model.
Hallucinations in customer support bots due to poor-quality internal documentation.