Customer Service Automation
CoRover.ai needed a scalable way to serve multilingual AI agents across India's 22 official languages while reducing latency, raising throughput, and meeting enterprise-grade security and compliance requirements for population-scale conversational services.
Reported impact
CoRover improved time to first token by 70 percent, increased output token throughput by 20 to 25 percent, and created a modular stack that can serve over a billion individuals with lower operational cost and better support for Indian languages.
CoRover.ai built a population-scale conversational AI stack for India by tuning BharatGPT for 22 Indic languages and deploying it on NVIDIA's model-development and speech stack. The company uses NVIDIA NeMo to build and customize the multilingual foundation layer, runs training and inference on NVIDIA DGX systems with NVIDIA A100 and H100 Tensor Core GPUs, and uses NVIDIA Riva to handle speech interactions across voice channels. NVIDIA AI Enterprise provides the supported software environment for enterprise deployments, while TensorRT and Triton optimizations are used to improve throughput and latency for real-time conversations. This modular architecture lets CoRover deliver text, voice, and video agents with stronger language coverage, lower time to first token, and the scalability required for millions of users and heavily regulated enterprise or public-sector deployments.
Customer text, voice, and video requests are routed into BharatGPT models customized in NeMo and served on DGX infrastructure. Riva handles speech interactions, while TensorRT and Triton optimize and serve real-time inference on A100 and H100 GPU resources for multilingual responses across enterprise and public-facing channels.
CoRover.ai developed a conversational generative AI platform that leverages BharatGPT, a large language model fine-tuned for 22 Indic languages.
CoRover.ai utilizes NVIDIA A100 and H100 Tensor Core GPUs for efficient training and inference. The AI solutions are enhanced by NVIDIA NeMo for conversational AI and NVIDIA Riva for speech AI.
Time to first token reduced by 70%, and output token throughput increased by 20%-25%, enabling real-time responses at scale.
With 22 officially recognized languages and a multitude of regional dialects, understanding and processing these linguistic nuances remains a complex task.
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Scoring methodology
Customer Reference Value measures usefulness as a customer reference. Evidence Confidence measures the quality and traceability of available evidence, not independent verification.
Traceability
Field coverage shows which facts each source supports. Dates are shown only when documented by the source.
The Organization JSON-LD on the profile lists "numberOfEmployees":{"@type":"QuantitativeValue","value":51}, which maps to SMB under the 0-249 rule.
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