AI use cases in India
3 documented implementations
Scaling Real-Time Lending and Analytics for Vivriti Capital
Vivriti Capital, an Indian fintech NBFC providing structured debt solutions, migrated from Amazon Redshift and an internally built open-source data platform (Spark, Airflow, custom orchestration) to Databricks in an eight-week phased migration covering roughly 20 workflows and 50+ pipelines. Databricks SQL now powers analytics and regulatory reporting separated from transactional lending systems, with governed lakehouse tables providing built-in lineage and time-travel for RBI/SEBI audit requirements. Vivriti uses Databricks Genie for semantic search and conversational analytics, replacing an in-house text-to-SQL solution, and is building toward AI-assisted underwriting, automated credit checks, fraud detection and borrower de-duplication within real-time latency requirements. The company reports roughly 25-30% lower total cost of ownership and about 20% faster SQL performance after the migration.
Vivriti Capital· IndiaDatabricks SQL · Databricks Genie · AI/BI
Zoho Corporation scales agentic AI adoption on Dell AI Factory with NVIDIA
Zoho uses the Dell AI Factory with NVIDIA, featuring Dell PowerEdge XE-Series servers with NVIDIA accelerated computing, NVIDIA NeMo and NVIDIA Quantum InfiniBand switches, to train and deploy AI solutions with Dell ProSupport providing infrastructure management. Zoho delivers AI solutions such as conversational AI, Zia Agents, Agent Studio and Zia LLM to 130 million+ users across 150+ markets.
Zoho Corporation· IndiaDell PowerEdge XE-Series · NVIDIA NeMo · NVIDIA Quantum InfiniBand +1
Delhivery achieves 160 ms latency for high-precision geocoding using Amazon EKS
Delhivery, a logistics provider in India, implemented a fine-tuned open-source Llama 3.2 1B large language model on Amazon EKS to support high-volume geocoding of pickup and drop-off addresses. The system processes up to 8,000 requests per minute at 160 milliseconds latency using NVIDIA A10G GPU-backed G5 Xlarge instances and the vLLM framework. Delhivery cut model-serving costs by approximately 80 percent and accelerated prototyping cycles from two days to under six hours, working with the AWS Prototyping and Cloud Engineering (PACE) team.
Delhivery· IndiaLlama 3.2 1B · Amazon Elastic Kubernetes Service · Amazon EC2 G5 Instances +3