About Cleanlab

At Cleanlab, we pioneer a future where AI works reliably with messy, real-world, human data. We envision a world where AI empowers people even in domains where error-prone data makes building AI systems extremely challenging, whether automating error correction in a hospital’s medical code data, helping a self-driving car company train navigation AI on noisy data, or assisting a software engineer in automating data cleaning for an ML pipeline.
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What we do and how we do it
Cleanlab is the data reliability layer checking every input and output of your modern AI stack. Our flagship product Cleanlab Studio is an enterprise grade data curation platform that adds trust and reliability to data-driven solutions like LLMs, ML models, analytics, product catalogs, and data warehouses. It integrates like a Brita filter on the data going in and the predictions flowing out of your data-driven systems, to find and fix errors in data, auto-label data accurately and cheaper with AI, and train more accurate state-of-the-art ML models for you in a single click using the improved data.
Cleanlab Studio offers three standout features that set it apart from other platforms: data-agnostic — it works equally well for text, tabular, image, structured, and unstructured data, domain-specific — it learns what good looks like for each customer's data/models, and intuitive — web app, API, and both model training and data improvement in one-click.
Our story

The three Cleanlab founders did their PhDs together at MIT, conducting over a decade of computer science research and amassing over 15,000 citations.

Their mission was simple: empower any organization to automatically find and fix every major issue in datasets to automate reliability for data-driven solutions (e.g. Large Language Models (LLMs), AI models, analytics systems).

The team invented the theory and algorithms that became a sub-field of machine learning called confident learning eventually realized as the most popular software for systematically improving the value of any dataset.

We envision a future where anyone can build reliable AI solutions to solve hard tasks with low quality data, in hours, not months.
Curtis Northcutt
Curtis Northcutt CEO
MIT | PhD in CS (ML) - 1,500+ citations

MIT thesis award. PhD in CS from MIT. Invented confident learning, the foundation of Cleanlab.

Jonas Mueller
Jonas Mueller Chief Scientist
MIT | PhD in CS (ML) - 4,000+ citations

Built Amazon Web Services AutoML service used by thousands of companies.

Anish Athalye
Anish Athalye CTO
MIT | PhD in CS (Systems) - 7,000+ citations

30,000+ stars across GitHub projects. ICML best paper winner.

The Cleanlab Mission
Our mission is to empower people with the first enterprise solution for automated data-centric ML ops: from data cleaning to training reliable models on real-world, human, noisy labeled data. At Cleanlab, we strive to combine the good and avoid the bad from what we've learned while working across the world's top technology and AI organizations to build a company culture that cares: about our customers, our impact on the world, and each other. Every Cleanlab product is built with four pillars in mind:
Cleanlab provides enterprise customers with SaaS encrypted solutions and VPC-first solutions.
Works for all major data types (text, visual, tabular, audio, etc) regardless of model/platform
Solutions for small datasets and massive datasets.
Trust and Reliability
Trust and Reliability
Add trust and reliability to every datapoint with 20+ dimensions of smart metadata.