How SLNG powers Indic voice AI at scale with Sarvam
Sarvam has become a clear option for how we serve Indic languages at SLNG. It's rare to find a provider that nails both the depth of language coverage and the quality of output. Bulbul and Saaras gave us production-grade Text to Speech and Speech to Text across Hindi, Telugu, Kannada, Marathi, and more, without the latency tradeoffs we saw elsewhere. Combined with genuinely excellent support, Sarvam has directly accelerated how quickly we can bring India-focused clients onto our platform.
Ismael Ordaz
CPO and Co-Founder of SLNG
Background
SLNG is the execution layer for voice AI. It routes, optimizes, and manages voice calls across any speech model and any region, so enterprises and SMBs can run production workloads at scale, in-region, and in compliance. Millions of calls a day flow through SLNG to reach their end customers.
The Problem
As SLNG grew its footprint in India, one requirement kept surfacing in customer conversations. Clients wanted voice agents that could speak and understand Indian languages well, and they wanted the models serving them to run in-region.
SLNG builds on a cascade architecture: Speech to Text, then a language model, then Text to Speech. For that cascade to work for Indian customers, every layer has to handle Indic languages properly.
As clients deployed voice AI across the country, demand for strong Indic support kept rising. Many enterprise customers required in-region speech services, while also expecting natural speech experiences across languages such as Hindi, Telugu, Kannada, Marathi, and others.
Although several global providers offered speech services, they did not consistently meet SLNG's expectations for Indic-language quality. This limited the company's ability to provide comprehensive language support for India-focused deployments.
SLNG needed a single provider that could deliver high-quality speech across multiple Indian languages, run in-region for enterprise production workloads, maintain low latency without giving up output quality, and integrate cleanly with its existing voice AI architecture.
Why Sarvam
SLNG first learned about Sarvam through conversations with customers, who consistently highlighted its strong performance for Indic-language speech.
The team evaluated Sarvam alongside providers including Cartesia, ElevenLabs, and open-source alternatives. Through internal benchmarking focused on speech quality and output accuracy, Sarvam emerged as the strongest choice for Indic languages.
Several factors influenced the decision:
- Top-ranked Indic-language quality in SLNG's internal benchmarks.
- In-region infrastructure aligned with enterprise compliance requirements.
- Low latency without compromising output quality.
- Smooth integration into SLNG's existing pipeline with minimal engineering lift and responsive support.
SLNG began with Bulbul for Text to Speech. The quality it delivered gave the team the confidence to expand to Saaras for Speech to Text as well, bringing both ends of the cascade onto Sarvam.
For SLNG, Sarvam complemented its strategy of integrating best-in-class models across every layer of its voice AI stack.
The Solution
Today, SLNG runs Bulbul, Sarvam's Text to Speech model, and Saaras, Sarvam's Speech to Text model, in production. Together, they power voice agents across a range of Indian languages.
Sarvam fills the Indic-language role in SLNG's cascade. Saaras handles transcription at the front of the pipeline, and Bulbul handles speech generation at the end, both tuned for the range of Indian languages SLNG's clients need. Because Sarvam offers in-region access, SLNG can meet the compliance and locality requirements its enterprise customers care about without giving up quality.
The integration also aligned well with SLNG's modular infrastructure, allowing the team to incorporate Sarvam's speech capabilities with minimal effort while maintaining production-grade performance.
The Impact
~1M
API requests per month in production
Faster
Onboarding of India-focused clients
Positive
End-user response to latency and output quality
Looking Ahead
SLNG expects its use of Sarvam to continue growing as demand for multilingual voice AI increases.
Over the next 12 months, SLNG plans to scale to tens of millions of minutes of voice workloads running on Sarvam, expanding it across a growing share of its production pipeline.
Beyond speech, SLNG has expressed interest in Sarvam's full stack, looking to bring more of the platform into its voice AI pipeline as its needs grow. This deepening partnership will further strengthen SLNG's ability to deliver high-quality, multilingual voice experiences for enterprise customers.