Innefu Labs Raises $30 Million to Enhance National Security with AI

Innefu Labs, a prominent player in the artificial intelligence sector within National Security, has successfully secured $30 million during its Series B funding round, which was led by Panthera Growth Partners. This investment, achieved through a mix of primary and secondary transactions from Panthera’s second fund, positions Innefu on a path toward an IPO and propels its ambitions for expansion into international markets, following successful ventures in the Middle East.

The funds will be allocated to support the next stage of global market development, alongside advancing deep-tech research and development, centred on its AI-first, sovereign capabilities, as stated in a press release by Innefu Labs.

About Innefu Labs

Founded in 2010 by Tarun Wig and Abhishek Sharma, Innefu Labs is dedicated to enhancing national and cyber security through artificial intelligence. With over 100 installations spanning the Indian Subcontinent, Middle East, and South East Asia, Innefu has developed indigenous platforms and multi-modal fusion systems that are currently in operation across defence, intelligence, law enforcement, revenue intelligence, and extensive enterprise clients.

Diverse Clientele and Deployments

The company serves a varied clientele, including Defence and Intelligence organisations, Law Enforcement Agencies, Financial Intelligence Units, BFSI, and numerous Fortune 500 companies. Innefu’s operations encompass several high-profile national scale AI intelligence fusion centres, such as the national terrorism data fusion centre, operational intelligence fusion centre, revenue intelligence fusion platforms, predictive policing platforms, and an open-source intelligence (OSINT) and deep web fusion platform tailored for law enforcement and defence entities.

Future Innovations

Innefu Labs aims to further develop its proprietary Agentic AI platform, establish a dedicated Physical AI (robotics) division, and create sovereign AI infrastructure featuring secure, domain-specialized language models designed for high-trust environments.