Gnani.ai launches Vachana STT model as core IndiaAI infrastructure
Gnani.ai, the Bengaluru‑based speech‑technology firm, has just put its newest speech‑to‑text engine into the hands of businesses. The model, named Vachana, arrives as the inaugural component of a broader VoiceOS platform the company says will underpin its IndiaAI mission. By exposing the service through an API today, Gnani.ai is giving enterprise customers immediate access to a tool that promises to handle the linguistic diversity of the subcontinent without the need for extensive customisation.
While many vendors tout multilingual capabilities, few have built a system from the ground up that reflects the way everyday Indians converse across regions, dialects and informal registers. The move also signals a shift toward treating speech recognition as a shared utility rather than a niche add‑on. For firms looking to embed voice interfaces in call centers, mobile apps or IoT devices, the timing feels deliberate.
As the rollout begins, Gnani.ai positions Vachana as the backbone of its next‑generation voice stack—an infrastructure layer designed for cross‑channel deployment.
"Vachana STT is built as core infrastructure, trained on how India actually speaks, and designed to operate across channels."
"Vachana STT is built as core infrastructure, trained on how India actually speaks, and designed to operate across channels." The Bengaluru-based company said Vachana STT forms the first release in its upcoming VoiceOS stack and is available immediately via API for enterprise customers. According to the company, early adopters will receive one lakh free minutes of usage. Gnani.ai said in a statement that the model has been trained on proprietary multilingual datasets spanning more than 1,056 domains.
It supports real-time and batch transcription and is already deployed across banking, telecom and customer support operations, collectively processing about 10 million calls per day with a P95 latency of 200 milliseconds. In internal and public dataset evaluations, Vachana STT recorded 30-40% lower word error rates for low-resource Indian languages and 10-20% lower error rates for the eight most-used languages in India, the company said.
Gnani.ai’s Vachana STT arrives as the first component of its promised VoiceOS stack, and the company has made it accessible immediately through an enterprise‑focused API. Trained on more than one million hours of real‑world voice data, the model claims to handle multiple Indian languages across sectors, positioning itself as “core infrastructure” rather than a simple localisation fix. “Speech recognition in India is not a localisation problem.
It is a foundational systems problem,” Ganesh Gopalan said, underscoring the firm’s strategic framing. Yet, without independent benchmarks, it’s unclear whether Vachana’s accuracy will meet the diverse acoustic realities of India’s many dialects. The rollout is tied to the government‑backed IndiaAI Mission, which may ease adoption for public‑sector projects, but private‑sector uptake remains uncertain.
If enterprises integrate the API, the model could become a de‑facto standard for Indian‑language speech interfaces; if not, its impact may stay limited to pilot deployments. As the first release, Vachana STT sets a baseline, but further data will be needed to assess its true utility across the country’s linguistic landscape.
Further Reading
- Gnani.ai Releases Vachana STT Trained on 1M Hours of Voice Data - CIOL
- Voice AI Revolution: Gnani.ai Launches Voice-to-Voice - Gnani.ai
- Building India's AI Agents for Voice, Text and Video with Gnani.ai - ET Digital
- Gnani.ai Unveils India's First AI-Generated Digital Human to Drive Financial Inclusion - CXO Media
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Common Questions Answered
What is the Vachana STT model and how does Gnani.ai position it within its VoiceOS platform?
Vachana STT is Gnani.ai's newest speech‑to‑text engine, launched as the first component of its upcoming VoiceOS stack. The company markets it as core infrastructure designed to handle India's linguistic diversity across channels, rather than a simple localisation add‑on.
How can enterprise customers access Vachana STT and what initial usage incentive is offered?
Enterprise customers can access Vachana STT immediately through an API that Gnani.ai has made publicly available. Early adopters are granted one lakh (100,000) free minutes of usage to evaluate the service.
What data was used to train Vachana STT and how extensive is this training set?
The model was trained on proprietary multilingual datasets that include more than one million hours of real‑world voice data from across India. This extensive training enables it to recognize multiple Indian languages and dialects accurately.
Why does Gnani.ai claim that speech recognition in India is a foundational systems problem rather than a localisation issue?
Gnani.ai argues that the challenge lies in building infrastructure capable of handling India's vast linguistic variety, not just translating existing models. By creating Vachana STT as core infrastructure, the company aims to solve the systemic complexities of Indian speech recognition.