According to a recent study by Truecaller, more than 76% of Indian consumers prefer talking to businesses over the phone. As voice remains consumers’ preferred means of communication, there remains a huge opportunity to use voice AI to automate support and outreach calls in the country. Voice AI startup Ringg, which already handles 20 million call attempts per month, is betting that call volumes will continue to rise in the coming months, and has just raised more funding based on that belief.
The company today announced that it has raised $10 million in Series A extension from Peak XV Partners. The company raised a $5.5 million Series A round earlier this year, bringing the total round to $15.5 million.
Ringg started as a text-to-speech startup called DesiVocal, but training their own voice models proved expensive, so the founders moved up the ladder and instead built voice AI agents for enterprises. Indian fintech company Cred was its first customer, and Ringg has since signed deals with Indian startups such as Flipkart, Practo, Groww, and PolicyBazaar.
“Initially, we were doing high-volume, low-complexity use cases like outbound calling, lead qualification, and loan collection. We quickly realized that these are not sticky use cases, so it’s always going to be a price war,” Siddharth Tripathi, co-founder of the startup, told TechCrunch.
While Ringg still addresses some of these simple use cases, it has set its sights on more complex workflows such as appointment booking for medical clinics, abandoned cart recovery for e-commerce sites, and onboarding/KYC (“Know Your Customer”) checks for fintech apps.
Tripathi said Ringg’s voice agents are currently live in 1,200 clinics on the healthcare app Practo, helping patients book appointments and follow up with next steps after a visit.
While voice calls still account for more than 70% of Ringg’s business, the startup has started expanding into other channels such as chat and WhatsApp. Some clients, such as Shell, also automate browser-based support requests.
“We’re trying to position ourselves as a platform for agents who get results and get things done, rather than being a voice for businesses,” Tripathi said.
Most of Ringg’s customers are based in India, with a few in the Middle East and the United States. But the startup isn’t trying to sell directly to U.S. companies. Instead, the company wants to partner with so-called global capability centers in India, offshore hubs that multinationals increasingly rely on for back-office and support operations, to sell automation capabilities alongside human support.
Tripathi said the company is building its own speech recognition and generation models and hopes to eventually own the complete speech stack, including infrastructure and deployment. But for now, that’s too expensive, so the product acts as an orchestration layer, routing tasks to different models depending on the use case.
Rishen Kapoor, principal at Peak XV, said Ringg started as a research institute building its own models, so its technical depth shows in the complex use cases it is currently working on.
“Thanks to their technical capabilities, they can actually execute hard-won enterprise workflows end-to-end. They can complete these higher-value tasks like merchant onboarding, like L1 and L2 support, with quality and consistency,” Kapur told TechCrunch.
Voice AI in India is a crowded field. Model makers like Deepgram, Celebrities, Cartesia, and local companies like Sarvam and Smallest.ai are vying for pole position. Orchestration-focused startups like Bolna and Blue Machines are chasing the same demographic that Ringg occupies, while sector-focused players like Gnani and Arrowhead are focused on finance.
This layered stack—modelers, orchestrators, and application layer players all trying to anchor enterprise workflows—is a story in itself. Money and defense will increasingly depend on customer relationships and ownership of results.
Ringg currently has 40 employees and has hired more than 15 people in the past three months. The startup is recruiting for the role of a forward-deployment engineer who combines technical chops and product management skills, along with researchers focused on reducing the cost of running models.
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