Cleanlab Raises $25M in Series A to Elevate Enterprise Data Quality
Cleanlab, the groundbreaking company at the forefront of automated data curation, has secured $25 million in Series A funding. The company aims to enhance the quality and value of enterprise data utilized by AI, Machine Learning (ML), and Analytics solutions. Menlo Ventures and TQ Ventures jointly spearheaded this strategic financing round, with Matt Murphy from Menlo Ventures and Schuster Tanger from TQ Ventures joining the company’s board.
Data Quality and Profitability
Cleanlab stands at the forefront of a data-driven revolution. In today’s business landscape, data-driven analytics and generative AI solutions are closely tied to revenue. Furthermore, it’s crucial to highlight that subpar data quality incurs an annual cost of over $3 trillion to the U.S. economy. A staggering 80% of companies spend their time manually enhancing data quality.
Cleanlab is providing the first enterprise solution that automatically enriches data with intelligent metadata. This eliminates the lion’s share of manual work and transforms untidy, real-world data into valuable inputs for various models. Moreover, this process significantly amplifies the reliability and profit margins of enterprise analytics, ML, and AI decisions. Cleanlab can also identify a dataset with no issues, cutting costs by bypassing expensive data quality and annotation for most data.
Cleanlab’s groundbreaking AI algorithms are an in-house development, created by the company’s founders. Cleanlab builds its proprietary approach to automated data curation upon pioneering work in the “confident learning” field. This approach positions Cleanlab as a fully enterprise-ready product.
Trustworthy Language Models
Cleanlab is also proud to unveil new features within its flagship automated data curation platform, Cleanlab Studio. This upgrade aims to enhance the trustworthiness of LLM-generated content by addressing unreliable large language model (LLM) outputs. Cleanlab Studio’s Trustworthy Language Model (TLM) ensures high-quality LLM outputs, offering a reliability score for all LLM-generated data. Therefore, it effectively identifies and rectifies issues across various datasets, including text, images, and tabular data.
Cleanlab’s Co-Founder and CEO Curtis Northcutt emphasized the significance of automating data curation,
“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.”
Cleanlab’s Series A funding success signifies a great step toward enhancing data quality. By automating data curation and bolstering data reliability, Cleanlab offers cost savings and empowers data-driven decision-making. With innovative AI algorithms and the Trustworthy Language Model, Cleanlab is set to reshape data quality.
