Cleanlab
cleanlab.ai
Actively hiring
AI Evaluation & Observability
AI Training Data & Annotation
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Funding and backers
Total raised
$30M
2 rounds on record
Latest round
$25M
Series A · Oct 2023 · 3 yr ago
Team
9
US Form 5500 (plan participants) · Dec 2025
Backers
4
Samsung Next, Menlo Ventures
The money's age
Long since its last round
36 months since the last round · not hiring
Momentum
0/100
no fresh signals
Last sign of life
this month
a posting or a news item
The story so fareach stage dated by its source: the company's releases, the press, registers, filings
Oct 10, 2023
Raised a round Series A $25M · Forbes
Jul 20, 2023
Raised a round Seed $5M · Venturebeat
Aug 17, 2022
Launched on Hacker News Show HN: Cleanlab Vizzy – automatically find label errors and bad data
2021
Founded
Tractionpublic signals, read monthly
Web presence
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Funding rounds
| Round | Date | Amount | Investors | Source |
|---|---|---|---|---|
| Series A | Oct 2023 | $25M | — | Forbes |
| Seed | Jul 2023 | $5M | — | Venturebeat |
Backed byfrom the funds' portfolios and accelerator catalogs
Samsung Next 
Menlo Ventures 
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Overview
Cleanlab is a company founded in 2021 by researchers from the Massachusetts Institute of Technology who developed confident learning, a method for finding label errors in datasets. Its open-source library and commercial studio automatically detect mislabelled, duplicated and outlier examples so teams can fix data instead of endlessly tuning models. The company has extended the approach to scoring the trustworthiness of large language model answers.
News
Cleanlab CEO: Agentic AI won't really gel until 2027
Curtis Northcutt, whose startup focuses on agentic orchestration and finding ways to reduce hallucinations, says companies are already scrambling to get ahead of the fast-evolving tech.
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Report
Prevent LLM Hallucinations with the Cleanlab Trustworthy Language Model in NVIDIA NeMo Guardrails
As more enterprises integrate LLMs into their applications, they face a critical challenge: LLMs can generate plausible but incorrect responses…
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OpenAI's o1 surpassed using the Trustworthy Language Model
See results from using the Trustworthy Language Model to: detect hallucinations/errors from the o1 model and improve its response accuracy.
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CROWDLAB: The Right Way to Combine Humans and AI for LLM Evaluation
CROWDLAB improves your team's LLM Evals process by automatically producing reliable ratings and flagging which outputs need further review.
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Report
Cleanlab Saves 90% on GPU Costs with Inferless Serverless Inference Inferless
Learn how Cleanlab cut GPU costs by 90% and boosted performance with Inferless. Discover the benefits of faster cold starts, efficient cost management, and seamless environment separation in their transition to serverless GPU inference.
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Report
Overcoming Hallucinations with the Trustworthy Language Model
TLM scores the trustworthiness of outputs from any LLM in real-time via state-of-the-art uncertainty estimation.
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Report
Cleanlab Announces Billion-Dollar Breakthrough in Detecting AI Hallucinations
Trustworthy Language Model (TLM) eliminates main obstacle to business use of LLMsSAN FRANCISCO, April 25, 2024 (GLOBE NEWSWIRE) -- A startup born in a quantum computing lab has unveiled the solution to one of generative AI's biggest problems. Cleanlab tod
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2024 AI 50 Listed among the 50 most innovative firms driving advancements and commercial applications in AI. svg:block &svg:align-middle &svg:stroke-1.65px text-text-primary"
Listed among the 50 most innovative firms driving advancements and commercial applications in AI.
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An open-source platform to catch all sorts of issues in all sorts of datasets
With cleanlab v2.6, the most popular library for Data-Centric AI now offers more comprehensive data audits including new checks for underperforming groups, null values, imbalanced classes, and more.
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Detecting Annotation Errors in Semantic Segmentation Data
Introducing new methods for estimating labeling quality in image segmentation datasets.
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Offices
Where the company hires and what each office is for
Cities
1
Hiring now
0
Countries
1
San Francisco
United States
HQ
Contact us
10-50 est. staff
75 Hawthorne Street, Suite 560, San Francisco, CA 94105