AI use cases in India
9 documented implementations
Tata 1mg expands healthcare accessibility across India with Databricks
Indian healthcare company Tata 1mg migrated from a legacy data warehouse to the Databricks Data + AI Platform with Unity Catalog and Delta Lake, reducing ETL costs by 50% and enabling over 1,000 non-data business users to access insights for location analytics, delivery route optimization and predictive maintenance.
ElasticRun helps small businesses improve supply chain with Databricks
Indian B2B e-commerce platform ElasticRun rebuilt its data infrastructure on Databricks with Delta Lake, Spark Declarative Pipelines and MLflow to manage over 10,000 machine learning models for supply chain and demand forecasting, cutting data pipeline slowdowns by 90% and IT costs by 33%.
Simpl ensures frictionless online payments for shoppers with Databricks
Indian checkout platform Simpl used Databricks SQL and Lakeflow Jobs to consolidate siloed data and speed up ML model training from several weeks to a single day, cutting time to market for ML models by 90% and improving analytics team turnaround time by 35%.
Tamilnad Mercantile Bank uses agentic AI to automate finance, HR, and customer service
Tamilnad Mercantile Bank unified finance, HR, sales, and customer service on Oracle Fusion Cloud Applications, chosen for embedded agentic AI capabilities. Expense management, customer service, and payroll are now fully automated, and employees handle twice as many service requests in half the time.
Tamilnad Mercantile Bank· IndiaOracle Fusion Cloud ERP · Oracle Fusion Cloud HCM · Oracle CXFueling personalized travel with real-time insights
Adani Digital Labs replaced legacy systems with the Databricks Data + AI Platform to power real-time, AI-driven personalization on the Adani OneApp, launching 15+ production use cases including geospatial analysis and ML-driven product recommendations, cutting operational costs by 29% and development time by 20%.
Tailoring credit products to the diverse needs of customers
HDFC Bank's Credit Risk Analytics and Innovation department migrated from a 16-node Hadoop cluster to the Databricks Data + AI Platform on Azure, using Delta Lake and Unity Catalog to power fraud control, marketing optimization and credit risk model building, significantly reducing query time and accelerating data pipelines for downstream risk management.
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/BIZoho 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