Anomalo Raises $33M to Expand its Machine Learning Solution for Data Quality
Anomalo, a startup that uses machine learning to solve data quality problems inherent in large data sets, has announced a $33 million Series B funding round, bringing its total raised to $72 million. The round was led by Index Ventures, with participation from existing investors Accel and Sequoia Capital.
Anomalo was founded in 2018 by former Instacart engineers, who saw the need for a simpler and more scalable way to ensure data quality, especially for data-driven applications such as generative AI models. Anomalo’s solution uses machine learning to automatically detect and correct anomalies, errors, and inconsistencies in data, without requiring manual rules or human intervention.
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Anomalo claims that its solution can reduce the time and cost of data quality by up to 90%, and can improve the accuracy and reliability of data by up to 50%. Anomalo’s customers include companies such as Netflix, Spotify, Airbnb, and Instacart, as well as public sector organizations such as the US Census Bureau and the World Bank.
Anomalo plans to use the new funding to expand its product offerings, grow its team, and enter new markets. The company also aims to become the leader in the emerging field of data quality for AI, as data quality becomes a critical factor for the success and trustworthiness of AI applications.
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Anomalo’s co-founder and CEO Elliot Shmukler said, “We believe that data quality is the foundation of AI, and that machine learning is the best way to achieve data quality. We are excited to partner with Index Ventures and our existing investors to bring our solution to more customers and industries, and to help them unlock the full potential of their data.”
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