Build WhatsApp and voice bots in major Indian languages. Low-latency, India-hosted inference with native Indic models, not English models with a translation layer.

Many high-volume Indian WhatsApp workflows run in Hindi, Tamil, Telugu, or other regional languages. English-first models miss the mark.
Voice bots need sub-second response. Cross-border routing adds noticeable latency that breaks the conversational experience.
High-volume WhatsApp bots burn through tokens fast. USD billing with forex markup makes unit economics unsustainable at scale.
Full stack of Indic-native models (STT, LLM, and TTS) accessible via a single OpenAI-compatible API.
Whisper and IndicConformer models tuned for Indian languages. Handles accents, code-mixing, and noisy audio.
Whisper Large V3 (Hindi-tuned), IndicConformer
Natural-sounding voice synthesis in major Indian languages. Multiple voices per language.
Indic Parler TTS, AI4Bharat VITS
Indic-native and multilingual models trained on Indian languages, not just translated from English. Native understanding of context and idiom.
Sarvam-30B, Krutrim-2, Qwen3-8B
India-hosted GPUs mean lower network latency for Indian users. Economy models optimized for high-throughput serving.
Economy tier: 7B-12B models
Models that understand Hindi, Tamil, Telugu, Bengali, Marathi, Gujarati, Kannada, Malayalam, and more, including code-mixed variants.
Server-sent events for real-time token streaming. Essential for voice bots that need to start speaking before the full response is ready.
Economy tier models designed for chatbot scale. INR billing so your unit economics work at 100K+ conversations/month.
Inference processing designed to stay in India. Critical for BFSI, healthcare, and government WhatsApp deployments.
Indic STT, TTS, and multilingual chat models, all on India-hosted infrastructure.
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