HomeNewsSequoia India’s Surge Leads TrueFoundry Raised $2.3 Million Seed Fund

Sequoia India’s Surge Leads TrueFoundry Raised $2.3 Million Seed Fund

The seed funding round for TrueFoundry, a developer platform for machine learning (ML), was directed by Sequoia India & Surge from Southeast Asia. The round brought in a total of $2.3 million. The investment round also included participation from investors such as Eniac Ventures & famous angels like Naval Ravikant, who was a co-founder of AngelList. The funds that have been received will be put toward the expansion of the specialist technical team as well as the continued development of products.

TrueFoundry was born out of the idea that no business – big or small – should miss out on the opportunities of machine learning. With our automated platform, data scientists and engineers are able to deploy machine learning models at the speed and maturity of big tech, cutting their production timelines from several weeks to a few hours. Data is the new oil, and we want to enable companies to use machine learning faster and generate greater business value. Our investors and team share the belief that TrueFoundry is paving the way for innovation that will propel businesses for the future ahead, and their participation in our pre-launch funding is a great testament to that,” said the co-founder & CEO of TrueFoundry, Nikunj Bajaj.

TrueFoundry’s goal is to automate boring, repetitive operations in the machine learning pipeline, such as infrastructure deployments, so that ML engineers & data scientists may devote their attention to higher-value, more creative endeavors. According to the statement made by the corporation, this makes it possible for enterprises to continuously upgrade old models and produce new ones in order to obtain a competitive edge. 

TrueFoundry is platform independent, easily integrates with your existing stack, and was established in June 2021 by Anuraag Gutgutia, Abhishek Choudhary, and Nikunj Bajaj. TrueFoundry was designed to facilitate seamless execution. It speeds up the deployment of machine learning and monitors live endpoints by automating repetitive processes in the machine learning pipeline.


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