Don't let your data do you dirty.
Cleanlab adds automation and trust to every data point going in and every prediction coming out of AI and GenAI solutions.
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Ambiguous146
Near duplicate75
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Experience GenAI that doesn't hallucinate.
Automatically detect and fix data issues that negatively impact your revenue, and significantly reduce the time and cost associated with improving analytics, LLM, and ML/AI solutions built on imperfect data.
80%
Time saved
Cut down data quality management time and reduce labeling costs by 5x to 50x.
10x
Faster production
Instantly assure data quality with AI-powered checks for every datapoint.
50%
More output
Increase the output of your team for the same level of effort.
Cleanlab Studio Features
Turn your dataset into a Cleanset.
Cleanlab Studio is an AI-powered data curation platform that automates essential data science and engineering tasks for AI model and data improvement. It refines and curates your data by correcting information errors, addressing common issues, and automatically adding intelligent metadata to each data point, improving reliability for tasks like training ML models, business intelligence, and analytics.
Automatically improve your dataset. No code required.
Our AI automatically detects label errors, outliers, PII, NSFW, near duplicates, drift, low-quality image examples like dark/blurry, under/over-exposed, and more.Explore interactive demo
Data curation
Automatically improve your dataset. No code required.
Our AI automatically detects label errors, outliers, PII, NSFW, near duplicates, drift, low-quality image examples like dark/blurry, under/over-exposed, and more.Explore interactive demo
Detect hallucinations
Detect hallucinations in production-ready GenAI systems with reliable trustworthiness scores for every LLM output.
Cleanlab’s Trustworthy Language Model (TLM) produces higher quality outputs than the leading LLMs using built-in hallucination detection, observability, and trustworthiness scores for every response-- enabling production-grade automation with LLMs where hallucinations are a show-stopper. Learn more about TLM
Automated labeling
AI-automated data labeling.
Our AI-automated data labeling is domain-specific and we guarantee better results than third-party data annotation tools. Cleanlab automatically labels most of your data using Foundation model confidence-scores, and then suggest which data is best to label or re-label next using active learning.Get started for free
Analytics
Explore analytics, summaries, and specific issues within your datasets.
Find the classes in your dataset with the most label issues and explore the entire heatmap of suggested corrections for all classes in your dataset. Estimate consensus and annotator-quality for datasets labeled by multiple annotators.Get started for free
Model deployment
Automatically train, tune, and deploy robust models via the world’s most advanced AutoML.
Automated pipeline does all ML for you: data preprocessing, foundation model fine-tuning, hyperparameter tuning, and model selection. ML models are used to diagnose data issues, and then can be re-trained on your corrected dataset with one click.Read tutorial
Pioneered at MIT and trusted by hundreds of top organizations.
Pioneered at MIT
Cleanlab’s Chief Executive Officer, Curtis Northcutt, invented Confident Learning during his PhD at MIT while working with the inventor of the quantum computer.
Built on the world’s cutting edge AutoML @ AI layer
Prior to Cleanlab, Chief Scientist Jonas Mueller developed Amazon's AutoML platform, used today to train and deploy many models on AWS SageMaker.
Designed for security and scalability for enterprise from the ground up
Cleanlab’s Chief Technology Officer, Anish Athalye, is well cited for his PhD work at MIT in the world’s top systems lab (PDOS).
Founded by the instructors of the MIT course on Data-centric AI
View Course Amazon AWS Principal Solutions Architect Cher Simon & Chief Evangelist Jeff Barr published a that features Cleanlab in hands on exercises.
“Manually inspecting and fixing potential label errors can be time-consuming. We can train a better model using Cleanlab to filter noisy data.”
-Amazon AWS Principal Solutions Architect at Amazon
Google used Cleanlab to find and fix label errors in millions of speech samples across different languages, to quantify annotator accuracy, and provide clean data for training speech models.
“Cleanlab is well-designed, scalable and theoretically grounded: it accurately finds data errors, even on well-known and established datasets. After using it for a successful pilot project at Google, Cleanlab is now one of my go-to libraries for dataset cleanup.”
-Senior Software Engineer at Google
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One of the largest financial institutions in the world, Banco Bilbao Vizcaya Argentaria, uses Cleanlab to .
“Cleanlab helped us reduce the uncertainty of noise in the tags. This process enabled us to train the model, update the training set, and optimize its performance. The goal was to reduce the number of labeled transactions and make the model more efficient, requiring less time and dedication. With the current model, we were able to improve accuracy by 28%, while reducing the number of labeled transactions required to train the model by more than 98%.”
-Expert Data Scientist at BBVA
Berkeley Research Group using Cleanlab Studio.
“We've started relying on Cleanlab to improve our ML and AI models at Berkeley Research Group LLC for over a month... I have to say, I'm impressed. Here's what we found: Increased model accuracy by 15%, Improved explainability & addressed performance impediments, Cut out training iterations by one-third, Overall performance improvement for our Data Science team.”
-Senior Managing Consultant Data Scientist at Berkeley Research Group
Recognized industry leaders in AI.
Cleanlab has been featured as an industry leader by Forbes and CB Insights.
2024
AI 50
Listed among the 50 most innovative firms driving advancements and commercial applications in AI.
2024
AI 100
Listed among the most promising private companies applying AI across industries and around the world.
2023
GenAI 50
Listed among the top 50 private companies leading advancements in generative AI technology.
Enterprise-ready
integration.
Cleanlab Studio interfaces directly with your data, no matter how it is stored.
Local Data Files
Programmatically
Data Warehouse
Cloud Storage
Enhanced security for sensitive data.
Some datasets require privacy beyond Cleanlab’s already top-tier security. Cleanlab Studio is deployable within your Virtual Private Cloud (VPC) for you to manage regular, rigorous security testing and isolated network environments, minimize exposure and provide granular control over network configurations and access permissions.
Learn more 
Add trust to every data point.
Start your 2-week free trial today. No credit card needed.
Cleanlab Open-Source
GitHubLimited Python API Access
Automatically detects issues
No auto-fix
Learn more Cleanlab Studio
Free trial
No code /ML engineering needed
Web interface and API access
Auto-fix data issues
Image, text, document, and tabular data
AI-automated data labeling
Trustworthy Language Model (TLM)
Analytics
AutoML model training/deployment
Contact sales Cleanlab Studio Enterprise
Contact salesEverything in free trial
More ML and data correction tasks
Project-optimized AutoML
Image segmentation
Object detection
VPC and cloud integration
Hosted deployment / inference
Priority for new feature requests
Scale to massive datasets
Dedicated support engineer
Book demo 
I got significantly better results using Cleanlab Studio than the cleanlab open-source package, mainly because it’s so much easier to use.
Lukas Lodes | AI Researcher at AIMotion Institute
Question: There’ve been many Model-Centric breakthroughs that have excited and inspired the field. What are some of your favorite examples of Data-Centric breakthroughs or wins that will inspire the field?
Answer: “The Cleanlab stuff out of MIT”
Andrew Ng, Keynote talk at ICML 2023 Workshop on Data-Centric Machine Learning
Cleanlab Studio is a very effective solution to calm my nerves when it comes to label noise!
Fredrik Olsson | PhD. Head of Data Science at Gavagai
I got significantly better results using Cleanlab Studio than the cleanlab open-source package, mainly because it’s so much easier to use.
Lukas Lodes | AI Researcher at AIMotion Institute
Question: There’ve been many Model-Centric breakthroughs that have excited and inspired the field. What are some of your favorite examples of Data-Centric breakthroughs or wins that will inspire the field?
Answer: “The Cleanlab stuff out of MIT”
Andrew Ng, Keynote talk at ICML 2023 Workshop on Data-Centric Machine Learning
Discover.
Deep dive into resources to learn more.
Featured by CB Insights as one of the 50 most innovative Generative AI companies
Research publications by Cleanlab team
Cleanlab and Confident Learning have been cited in hundreds of academic papers
See thousands of label errors found by Cleanlab in the top ten ML datasets
Learn how AI can now improve the data itself
Cleanlab Open Source: The most popular library for Data-Centric AI
Cleanlab featured in MIT Technology Review
Get started with Cleanlab Studio tutorials