iTuring raises $5 M Series A funding from Dallas Venture and Mela Ventures
Dallas Venture Capital (DVC) and Mela Ventures led a $5 million Series A funding round for iTuring.ai, an enterprise-grade AI/ML platform for the Banking, Financial Services & Insurance (BFSI) industry.
The business had previously raised $1.19 million in the same round, which was co-led by SenseAI and Pentathlon Ventures and included Ghosal Ventures.
According to a press release from iTuring, the money raised will be utilized to hasten the launch of its in-house, zero-code platform, which allows banks and insurers to automate each step of the data science and machine learning deployment process.
iTuring.ai, which was co-founded in 2018 by Suman Singh, Amit Kumar, Mohammed Nawas, and Srivalsan Ponnachath, simplifies the whole data science and machine learning lifecycle, allowing businesses to quickly create, implement, oversee, regulate, and operationalize AI applications at scale. Throughout the entire model development, deployment, and operation process, the company strives to provide transparency, accountability, compliance, and dependability.
By combining sophisticated automation for data preparation, feature engineering, model development, deployment, and continuous monitoring in a single environment that satisfies the industry’s stringent regulatory and audit requirements, iTuring tackles the complexity of the sector.
“Our vision from day one was to empower financial institutions to automate the full lifecycle of AI model development, deployment, and governance with a transparent, explainable, and audit-ready solution,” said Suman Singh, Founder & CEO of iTuring,” said Suman Singh, founder & CEO of iTuring.
According to the company, its AI/ML platform is provided to customers in the US, South Africa, and India. One of the top payment platforms in the world and the US have forged a strategic alliance that will help them increase customer revenue and cut costs significantly.
iTuring asserts that its BFSI clients have experienced quantifiable improvements in predictive performance along with quick project delivery and substantial manual labor reductions.
