Consumer AI startup Equal AI has raised $30 million in a Series B funding round co-led by Prosus Ventures and Tomales Bay Capital. The two investors had also co-led the company's $10 million Series A round in November 2024.
The round also saw participation from Valiant Fund, Think Investments, PhonePe founder Sameer Nigam, Airtel family office member Zubin Bharti Mittal, Skyflow co-founder Anshu Sharma, Meta India and Southeast Asia vice president Sandhya Devanathan, and CtrlS Datacenters chairman Sridhar Pinnapureddy.
Equal AI had previously raised $10 million in a Series A round led by Prosus Ventures and Tomales Bay Capital in November 2024.
The fresh capital will be used to expand Equal AI's consumer offerings and strengthen its AI assistant capabilities across communications, financial services, lifestyle, shopping, and concierge services.
Founded in 2022 by Keshav Reddy, a former venture capitalist who backed startups such as Cred, Upstox, Hive, Genies, and Chipper Cash, Equal initially operated as a consent-based data-sharing and identity infrastructure platform serving enterprises across banking, lending, insurance, telecom, and digital services. The company entered the consumer AI segment in October 2025 with the launch of its AI-powered call assistant.
According to the Hyderabad-based company, Equal AI has crossed 1 million monthly active users and 350,000 daily active users within eight months of launch. The platform helps users identify callers, block spam calls, understand caller intent, and perform actions on behalf of users.
Equal claims its enterprise business serves more than 350 customers and processes over 1 billion transactions annually through its identity and data-sharing infrastructure.
With the latest funding, the company plans to deepen its presence in the consumer AI market as it looks to build a broader AI assistant platform for Indian smartphone users.
The investment comes amid growing investor interest in AI-native consumer applications in India, particularly voice-based products tailored to local use cases.