Sarvam's Saaras v3 Powers Equal AI's 285M Monthly Indian Calls
Sarvam's Saaras v3 speech recognition now transcribes roughly 34 million audio minutes a month for Equal AI's call-screening assistant across nine Indian languages.
- Sarvam is now powering Equal AI's Personal Call Assistant, which screens incoming calls for users.
- Equal processes roughly 34 million audio minutes per month through Sarvam's Saaras v3 speech model.
- Volume grew about 420x from October 2025 to May 2026, hitting a 285M calls/month run rate by June.
- Reliability sits at 99.98% success across ~129 million recent calls, with Saaras v3 handling 85% of traffic.
- Transliteration mode dominates at 97% of calls, converting Indic speech into Roman script for downstream systems.
- Saaras v3 supports 22 Indian languages plus English, trained on 1M+ hours of Indian audio.
Answering the phone in India is a linguistic minefield. A single caller might slip between Hindi, English, and Tamil inside one sentence, on an 8 kHz telephony line, with a background of traffic or a call center behind them. That is the environment where Equal AI's Personal Call Assistant operates, and it now runs its entire speech-to-text stack on Sarvam.
The assistant picks up incoming calls on behalf of users, figures out who is calling and why, then hands back a summary and a recording once the call ends. It handles over a million live calls every day across nine Indian languages, according to Equal CEO Akhilesh Damaraju. Under the hood, every one of those calls is transcribed by Saaras v3, Sarvam's flagship speech recognition model.
Why an India-first ASR was the only option
Global speech models tend to buckle on Indian audio. Hindi speakers routinely switch to English and back within a single sentence, Tamil is agglutinative so word boundary detection is much harder, and Indian telephony audio is often 8kHz with heavy background noise, nothing like the clean studio recordings global ASR models are optimized for. A call assistant that misfires on any of these fails silently, mislabeling who called and why.