Revolutionizing Customer Engagement: Sarvam AI’s Voice Technology for Businesses in India

Sarvam AI, often likened to India’s version of OpenAI, has unveiled software tailored for businesses to engage with customers through spoken conversations rather than just text. Leveraging data from 10 indigenous Indian languages, the technology is priced at one rupee per minute in a strategic move to capture market share. India has been striving to match the global AI frenzy since the advent of ChatGPT nearly two years ago, but the development of chatbots has been hindered by a scarcity of data in many of the country’s diverse languages. While urban residents can communicate with chatbots in English, a majority of India lacks the proficiency to do so. This has led to a surge in startups betting on voice bots integrated with regional language data to tap into a broader audience within India and potentially resonate with users in other nations. By incorporating AI-powered voice capabilities, technology companies aim to offer more interactive, conversational services that can engage users verbally in real-time and automate various tasks. This trend is already evident in a wide array of consumer and business applications across India. For instance, Gnani AI, backed by Samsung, handles millions of voice interactions daily for major Indian banks, insurers, and automobile companies. CoRover AI provides voice bot services in 14 Indian languages to entities like IRCTC and a local police department. Additionally, Haloocom Technologies’ voice bot supports five Indian languages for customer service interactions and aiding in screening job applicants. Ankush Sabharwal, the co-founder and CEO of CoRover, believes that while the world has transitioned from a digital-first to a mobile-first to an AI-first approach, voice remains the most instinctive way to interact with technology. Despite some reservations, India’s AI startups remain bullish on the potential of this technology. Ganesh Gopalan, the co-founder and CEO of Gnani, asserts that AI tailored for specific use cases, languages, and audiences is not only more precise but also more cost-effective to operate, while significantly reducing errors.