Cleanlab Raises $25M Series A to Automatically Increase the Value and Accuracy of the World’s Enterprise Data Used by AI, ML, and Analytics SolutionsShare This ArticleMore than 10% of Fortune 500 Companies Use Cleanlab’s Automated Data Curation Platform to Overcome the Biggest Time and Cost Hurdle for Analytics, LLM, and AI Teams: Reliability.SAN FRANCISCO, October 10, 2023-- Cleanlab, the company behind the automated data curation solution used to increase the dollar value of every data point in enterprise artificial intelligence (AI), large language model (LLM), and analytics solutions, has secured $25 million in Series A funding.

Cleanlab筹集2500万美元系列A，以自动提高AI，ML和Analytics Solutions使用的全球企业数据的价值和准确性分享本文“财富”500强企业中超过10%的公司使用Cleanlab的自动化数据管理平台来克服最大的时间和成本障碍分析，LLM和AI团队：可靠性.SAN FRANCISCO，2023年10月10日-Cleanlab，自动化数据管理解决方案背后的公司用于增加企业人工智能（AI），大型语言模型（LLM）和分析解决方案中每个数据点的美元价值，已获得2500万美元的A系列资金。

This financing round was co-led by Menlo Ventures and TQ Ventures; Menlo Ventures' Matt Murphy and TQ’s Schuster Tanger will join the board. Existing investor Bain Capital Ventures (BCV) and new investor Databricks Ventures participated in this funding round, which brings Cleanlab’s total funding to $30 million..

这轮融资由Menlo Ventures和TQ Ventures共同领导；Menlo Ventures的Matt Murphy和TQ的Schuster Tanger将加入董事会。现有投资者Bain Capital Ventures（BCV）和new investor Databricks Ventures参与了这一轮资助，使Cleanlab的总资金达到3000万美元。。

Cleanlab helps drive profitability. For today’s businesses, revenue is directly tied to data-driven analytics decisions and generative AI solutions. Bad data costs the U.S. alone over $3 trillion1, and 80 percent of time spent by enterprises is manually improving the data quality.2 Cleanlab is the first enterprise solution that reliably adds smart metadata automatically, removing the vast majority of the work and turning messy, real-world data into useful inputs for various models.

Cleanlab有助于提高盈利能力。对于当今企业而言，收入与数据驱动的分析决策和生成的AI解决方案直接相关。糟糕的数据仅使美国花费超过3亿美元1，企业花费的80%的时间都在手动提高数据质量.2 Cleanlab是第一个可靠地自动添加智能元数据，消除绝大多数工作并转向混乱的企业解决方案，将现实世界的数据转化为各种模型的有用输入。

This process increases the reliability and profit margin of enterprise analytics, LLM, and AI decisions. Cleanlab also automatically identifies the majority of a dataset containing no issues, increasing the profit margins of enterprise pipelines by avoiding expensive data quality and annotation for the majority of data..

此过程提高了企业分析，LLM和AI决策的可靠性和利润率。Cleanlab还可以自动识别大多数不包含问题的数据集，通过避免昂贵的数据质量和大多数数据的注释来提高企业管道的利润率。。

Cleanlab’s novel AI algorithms were developed in-house by the founders, all of whom are PhDs in Computer Science from MIT and published researchers. The team’s proprietary approach to automated data curation builds upon the “confident learning” field created by the Cleanlab team, enabling them to pioneer an enterprise-ready product..

Cleanlab的新型AI算法由创始人内部开发，他们都是麻省理工学院计算机科学博士和已发表的研究人员。该团队专有的自动数据管理方法建立在Cleanlab团队创建的“自信学习”领域的基础上，使他们能够开创企业就绪的产品。。

Today, over 10% of Fortune 500 companies (including AWS, JPMorgan Chase, Google, Oracle, and Walmart) and a variety of innovative startups (like ByteDance, HuggingFace, and Databricks) use Cleanlab to find and fix problems in sizable structured and unstructured visual, text, and tabular datasets. Whether building an LLM for enterprise, tagging intents in chatbot text data, or objects in visual navigation data, Cleanlab increases the dollar value of every data point in your dataset by automatically analyzing and correcting outliers, ambiguous data, and mislabeled data..

今天，“财富”500强公司（包括AWS，JPMorgan Chase，Google，Oracle和沃尔玛）和各种创新创业公司（如ByteDance，HuggingFace和Databricks）中超过10%使用Cleanlab在相当大的结构化中查找和解决问题和非结构化的视觉，文本和表格数据集。无论是为企业构建LLM，在chatbot文本数据中标记意图还是视觉导航数据中的对象，Cleanlab都可以通过自动分析和纠正异常值，模糊数据和错误标记的数据来增加数据集中每个数据点的美元价值。。

The company is also announcing that its flagship automated data curation platform, Cleanlab Studio, has launched several new features that address unreliable LLM outputs. Cleanlab’s Trustworthy Language Model (TLM) produces high-quality LLM outputs like ChatGPT, Falcon, and similar LLMs. It also adds a trustworthiness reliability score to all LLM outputs.

该公司还宣布其旗舰自动数据管理平台Cleanlab Studio推出了多项新功能，以解决不可靠的LLM输出问题。Cleanlab值得信赖的语言模型（TLM）可生成高质量的LLM输出，如ChatGPT，Falcon和类似的LLM。它还为所有LLM输出添加了可信度可靠性评分。

Cleanlab Studio identifies and fixes issues in all types of datasets, including text, image, and tabular data. TLM extends Cleanlab Studio’s capabilities to add intelligent metadata to help automate reliability and quality assurance for systems that rely on LLM outputs, synthetic data, and generated content.

Cleanlab Studio识别并修复所有类型数据集中的问题，包括文本，图像和表格数据。TLM扩展了Cleanlab Studio添加智能元数据的功能，以帮助自动化依赖LLM输出，合成数据和生成内容的系统的可靠性和质量保证。

Cleanlab’s Trustworthy Language Model is available to try in Beta today with Cleanlab Studio at cleanlab.ai..

Cleanlab值得信赖的语言模型今天可以在Cleanlab.ai的Cleanlab Studio上试用Beta。。

“After working with companies like Microsoft and Tesla to get their AI-driven products to function better and helping MIT and Harvard detect cheating, it became clear that mislabeled and poorly curated data was the core issue behind these challenges,” said Cleanlab Co-Founder and CEO Curtis Northcutt.

Cleanlab联合创始人兼首席执行官Curtis Northcutt说：“与微软和特斯拉等公司合作，让他们的人工智能驱动产品更好地发挥作用，帮助麻省理工学院和哈佛大学发现作弊行为，很明显，标签错误和管理不善的数据是这些挑战背后的核心问题。”。

“It's the culmination of over a decade of work to introduce Cleanlab Studio, which reimagines what AI and analytics can do for people and enterprises now that we can automate data curation and reliability.”.

“引入Cleanlab Studio是十多年工作的高潮，它重新想象了AI和分析可以为人和企业做些什么，现在我们可以自动化数据管理和可靠性。”。

“While most of the investment in generative AI is chasing the biggest, baddest, and best model, the reality is that there is a massive complimentary opportunity that can shave billions off those efforts and lead to a better outcome. That is Cleanlab,” said Matt Murphy, Partner at Menlo Ventures. “Cleanlab’s amazing team of ML researchers and practitioners has built a data curation platform that fundamentally improves models via better, cleaner data.”.

“虽然生成人工智能的大部分投资都在追逐最大，最糟糕和最好的模式，但现实情况是，有一个巨大的补充机会可以削减数十亿的努力并带来更好的结果。那就是干净实验室，”Menlo Ventures的合作伙伴Matt Murphy说。“Cleanlab卓越的ML研究人员和从业人员团队建立了一个数据管理平台，通过更好，更清洁的数据从根本上改进模型。”。

“We are thrilled to partner with Curtis, Jonas and Anish, the eminent authorities on data-centric AI,” said Schuster Tanger, Co-Managing Partner of TQ Ventures. “They have developed a solution to a large and pressing problem for enterprises across almost all industries: namely, ambiguous and wrongly labeled data.

“我们很高兴与Curtis，Jonas和Anish合作，后者是以数据为中心的人工智能的知名权威机构，”TQ Ventures的共同管理合作伙伴Schuster Tanger说。“他们为几乎所有行业的企业制定了一个解决方案，即模糊和错误标记的数据。

In addition to an exceptional team and superior technology, Cleanlab also has real world results from customers that point to Cleanlab’s effectiveness around percent accuracy improvement, percent reduction in labeled transactions required to train models, and dollar reduction in enterprise costs.”.

除了出色的团队和卓越的技术外，Cleanlab还拥有来自客户的真实结果，这些结果表明Cleanlab在提高准确度百分比，培训模型所需标记交易减少百分比以及企业成本降低美元方面的有效性。

“Cleanlab is well-designed, scalable, and theoretically grounded: It accurately finds data errors, even on well-known and established datasets,” said Patrick Violette, Senior Software Engineer at Google, “After using it for a successful project at Google, Cleanlab is now one of my go-to libraries for dataset cleanup.”.

谷歌高级软件工程师帕特里克·维奥莱特（Patrick Violette）说：“Cleanlab设计良好，可扩展，理论基础良好：即使在知名和成熟的数据集上，它也能准确地发现数据错误。”在Google上成功开展项目后，Cleanlab现在是我去图书馆进行数据集清理的一个“。

About Cleanlab

关于Cleanlab

Pioneered at MIT and trusted by hundreds of top organizations, Cleanlab turns unreliable data into reliable models and insights by automatically finding and fixing errors in both structured and unstructured datasets, such as visual, text, and tabular data. Based in San Francisco, Cleanlab was founded in 2021 by three PhDs in Computer Science from MIT..

Cleanlab率先在麻省理工学院工作并受到数百家顶级组织的信任，通过自动查找和修复结构化和非结构化数据集（如可视化，文本和表格数据）中的错误，将不可靠的数据转变为可靠的模型和见解。Cleanlab总部设在旧金山，2021年由麻省理工学院计算机科学三博士创立。。

About Menlo Ventures:

关于Menlo Ventures：

Menlo Ventures is a venture capital firm that strives to have a positive impact on everything we do. That’s why we support businesses, including Anthropic, Carta, Chime, Harness, Poshmark, Pillpack, Pinecone, Roku, Rover, Uber, and Warby Parker, that are reimagining life and work for the better. Over 47 years, we’ve grown a portfolio that includes more than 80 public companies, over 165 mergers and acquisitions, and currently have $5.6 billion under management.

Menlo Ventures是一家风险投资公司，致力于对我们所做的一切产生积极影响。这就是为什么我们支持企业，包括Anthropic，Carta，Chime，Harness，Poshmark，Pillpack，Pinecone，Roku，Rover，Uber和Warby Parker，正在重新想象生活并为更好的工作做准备。47年来，我们已经发展了一个包括80多家上市公司，165多家兼并和收购的投资组合，目前管理下的投资额为56亿美元。

We invest at every stage and in every sector, with expertise in Consumer, Enterprise, and Healthcare. From developing market strategies to creating communities, we provide real impact where entrepreneurs need it most. When we’re in, we’re ALL IN. www.menlovc.com @MenloVentures.

我们在每个阶段和每个部门进行投资，拥有消费者，企业和医疗保健方面的专业知识。从制定市场战略到创建社区，我们为企业家最需要的地方提供真正的影响。当我们进来的时候，我们都在.www.menlovc.com@MenloVentures。

About TQ Ventures:

关于TQ企业：

Based in New York City, TQ Ventures is a venture capital firm led by Schuster Tanger and Andrew Marks. The firm is generally agnostic on industry vertical and geography, and instead prioritizes partnering with extraordinary founders across the software complex (B2B and B2C). Across our more than 80 global investments, we believe the differentiated support and networks we provide our founders has fueled our reputation and in turn performance record.

总部设在纽约市，TQ Ventures是由Schuster Tanger和Andrew Marks领导的风险投资公司。该公司通常在行业垂直和地理上是不可知的，而是优先考虑与软件公司（B2B和B2C）的非凡创始人合作。在我们80多项全球投资中，我们相信我们为创始人提供的差异化支持和网络提升了我们的声誉，进而提升了业绩记录。

Founded in 2018, TQ has approximately $1 billion under management and is currently investing out of its third fund. www.tqventures.com.

TQ成立于2018年，管理层约有10亿美元，目前正在投资第三笔资金。www.tqventures.com。

¹Harvard Business Review. (2016, September). “Bad Data Costs the U.S. $3 Trillion Per Year.” Harvard Business Review. https://hbr.org/2016/09/bad-data-costs-the-u-s-3-trillion-per-year

哈佛商业评论。（2016年9月）。“不良数据每年花费美国3万亿美元。”哈佛商业评论。https://hbr.org/2016/09/bad-data-costs-the-u-s-3-trillion-per-year

²Ng, A. (2021, March 24). '80 percent of our work is data preparation.' The Batch: AI at Work (Issue 84). deeplearning.ai. https://www.deeplearning.ai/the-batch/issue-84/Contact:

²Ng，A。（2021年3月24日）。'我们80%的工作是数据准备批次：AI在工作（问题84）。deeplearning.ai。https://www.deeplearning.ai/the-batch/issue-84/Contact:

Allison Braley

Allison Braley

Bain Capital Ventures

贝恩资本风险投资公司

abraley@baincapital.com

abraley@baincapital.com