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Quantexa
From Wikipedia, the free encyclopedia
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Quantexa is a UK-based software company that develops artificial intelligence-based applications for data analytics and decision-making.[1][2] The company was founded in 2016 and is headquartered in London, with operations in North America, Europe, and the Asia-Pacific region.[3] Its software is used by organisations in sectors such as financial services, telecommunications, insurance, oil and gas, and government for fraud detection, risk assessment, talent acquisition, and regulatory compliance.[1][4][5]
As of 2025, Quantexa has been valued at $2.6 billion[6] and operates in over 70 countries.[7] Investors include Warburg Pincus, HSBC, and the Ontario Teachers’ Pension Plan.[8]
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History
Quantexa was founded in London in 2016 by a group of co-founders including Jamie Hutton,[9] Richard Seewald, Imam Hoque,[1] Felix Hoddinott, and Vishal Marria, who also serves as the company's chief executive officer.[3][10]
The company was established to address limitations in traditional data analysis methods, particularly in identifying hidden connections across large datasets.[11][12] The name "Quantexa" is derived from the company's focus on quantitative methods and data analysis.[10]
In April 2023, the company completed a Series E funding round, raising $129 million at a valuation of approximately $1.8 billion, marking its entry into "unicorn" status.[13][14] The same month, the company exceeded an annual recurring revenue (ARR) of $100 million, which led to Quantexa achieving Centaur status.[15]
In early 2025, Quantexa was selected to participate in the World Economic Forum’s Unicorn Program aimed at supporting high-growth technology companies.[16]
In March 2025, Quantexa completed a Series F funding round of $175 million, led by Teachers' Venture Growth, the venture arm of the Ontario Teachers' Pension Plan.[8][17]
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Products
Quantexa’s main product is a Decision Intelligence Platform which applies artificial intelligence to create context from disparate data sources.[3][18] The platform combines technologies such as entity resolution, graph analytics, and probabilistic modeling to construct networks of related individuals, companies, and events.[10] These networks are used to support use cases such as fraud detection, customer profiling, and network risk analysis.[10][5]
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References
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