We are a young big data analytics company that helps businesses, whether Fortune 500 or SMEs or startups, to accelerate their digital transformation journey. Quadratyx contributes to improvements in operational efficiency and encourages innovation by enabling businesses to leverage the untapped potential of data.
Founded in 2014, Quadratyx started its operations in a small office with a core team of 4 data science professionals, inspired by the vision of our founder and CEO Dr. Sreerama Murthy. The journey has been exciting and rewarding. Today, we’ve grown to a team strength of over 75 data scientists with three offices – in India, New Jersey and Colombia.
Companies trust us as their key partner because of the depth of our functional and technical knowledge in AI and ML. Our journey in Machine Learning started in 1991, with Dr. Murthy completing a master’s thesis in Next Generation Expert Systems from IIT-Madras in Computer Science, followed by PhD thesis from Johns Hopkins University in 1995 on “Automatically building mega-catalogs from Hubble Space Telescope images,” which is used till date in score of countries. Since then, there has been no looking back. Today, Dr. Murthy, is listed amongst India’s Big Data Rock Stars and Top 10 Most Prominent Analytics Academicians.

Recognized as a Great Place to Work®, reflecting our commitment to an inclusive and high-performance workplace.

Featured among the Top 10 Most Influential Analytics Leaders.

Recognized as one of the Best Places to Work in AI for fostering innovation and technology excellence.

Ranked among the Top 10 Fastest Growing Big Data Companies for enterprise AI and analytics innovation.

Recognized among the Top 25 Most Promising Big Data Companies in India for excellence in big data and AI.

Dr. Sreerama Murthy was recognized among the Top 10 Most Prominent Analytics Academicians in India.
Quadratyx combines the power of AI technology and advanced analytics to solve complex business problems - by applying ML models and flexible Big Data platforms to reduce human bias and build end-to-end data management structure that can handle large datasets to ensure that the right interpretation is done at a fraction of the cost of traditional solutions.
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