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LatentView Analytics
LatentView Analytics has been helping enterprises make smarter, data-driven decisions for 20+ years. Founded in 2006 by Venkat Viswanathan and Pramad Jandhyala, the company has grown into one of the world’s leading digital analytics firms — and became the first analytics company in India to go public in 2021.
LatentView brings deep expertise in end-to-end data engineering, business analytics, GenAI, and predictive modeling to 50+ Fortune 500 clients across tech, retail, financial services, and CPG. The company’s work spans everything from AI-powered sales forecasting and customer lifetime value modeling to large-scale data platform builds and GenAI implementation.
A publicly traded company serving the US, India, Canada, Europe, and Singapore, LatentView has been recognized in Forrester’s Customer Analytics Service Providers Landscape and among India’s Best Workplaces for Millennials. In 2024, the company expanded its capabilities through the acquisition of Decision Point Analytics, adding Revenue Growth Management and GenAI product solutions to its portfolio.
LatentView’s insights are shaped by real client engagements across industries — from building ML frameworks for telecom network reliability to developing guest lifetime value models for leading cruise lines. The perspectives shared on this blog draw from that hands-on experience working at the intersection of data, AI, and business strategy.
Learn more at latentview.com.
Blogs
How Predictive Analytics Is Transforming Football Scouting
Analytics has been used extensively in the football industry for over a decade, especially during scouting.
Deploying AI at Scale: Building Reliable Model-Serving Pipelines with Databricks
Deploying ML models at enterprise scale on Databricks involves packaging trained models as serverless REST API endpoints that auto-scale with
AI in Customer Experience: Use Cases & Strategy
AI in customer experience (CX) uses artificial intelligence to personalize interactions, automate support, and predict user needs in real time.
Agentic AI In Healthcare: Use Cases, Challenges & Best Practices
Agentic AI in healthcare leverages autonomous, goal-directed agents that plan, sequence, and execute complex clinical and administrative tasks independently. Use
Databricks vs Azure Synapse: Compare Key Differences (2026)
Choosing between Azure Synapse and Databricks depends on workload priorities. Synapse excels as a unified data warehouse for SQL-based analytics
Transportation Analytics: Use Cases of Data Analytics in Transport Industry
With the growth of multi-modal transport, the need for segment-wise analysis is essential for efficient management.
Intelligent Automation in Payment Integrity
Payment integrity in healthcare and financial services benefits from intelligent automation that merges AI, machine learning, and RPA to validate
Handling Data at Scale Using One-Line EDA Libraries
One-line EDA are easy-to-use libraries that provide a better overview of data.
From Dashboards to Decision Agents: The Rise of Agentic AI in Payment Operations
Agentic AI in payment operations deploys autonomous agents that execute real-time transaction decisions and process payments independently. Core capabilities include
Agentic AI for Customer Analytics: Use Cases, Examples & Best Practices
Agentic AI for customer analytics employs autonomous, goal-oriented agents that ingest data, interpret behavioral patterns, and execute multi-step analytical workflows
