Gnani.ai

Gnani.ai is a frontier voice AI company that builds foundational speech models from the ground up and the enterprise agents that run on them, rather than adapting general purpose language models to voice. Founded in Bengaluru in 2016 by Ganesh Gopalan and Ananth Nagaraj, the company serves more than two hundred enterprises across banking, insurance, healthcare, telecom, and government, processing over thirty million voice interactions a day. It was selected under the IndiaAI Mission to build a 14 billion parameter voice foundation model, raised a 10 million dollar first tranche of a Series B led by Aavishkaar Capital in March 2026, and was already EBITDA profitable in its prior fiscal year.

The differentiator is a full, in house model stack trained on fourteen million hours of real telephonic audio across more than forty languages, the noisy, accented, code switched calls that studio trained models struggle with. Its lineup spans speech to text, text to speech, and a direct speech to speech model, plus its own language models, and it ranks first across eight of nine Indian languages on a noisy call center benchmark and first on the Berkeley function calling benchmark for agentic voice. On top of the models sit enterprise products: voice agents, real time agent assist for human staff, speech analytics with automated quality assurance, and voice biometrics for authentication, unified in its Inya agent platform.

For regulated buyers, the deployment story matters as much as accuracy. Gnani supports cloud, private cloud, on premise, and air gapped installation so voice data stays where compliance requires, and it carries SOC 2, ISO 27001, GDPR, HIPAA, and PCI DSS. Because the whole pipeline is built in house with no third party dependencies, it offers deep localization and strong margins, and the same models are available via API for developers and OEMs to build on. For an enterprise in a regulated, multilingual market that needs production grade voice automation with data control, Gnani is a category leading option; teams wanting to bring their own third party models will find it a vertically integrated stack instead.