Outcreating AI-Ready Enterprise Productivity with India’s First Raptor DB Pro Migration

Our Client

The client is a global IT services and consulting organization headquartered in India, serving enterprise customers across industries with a workforce of over 90,000 employees.

The organization operates a large-scale ServiceNow ecosystem spanning Enterprise AI, ITSM Pro+, ITOM, ITAM, HRSD, SPM, SecOps, IRM, BCM, and TPRM workflows. As AI adoption accelerated across the enterprise, the ServiceNow platform became a strategic backbone for productivity, governance, and digital operations at scale.

Market Context

Enterprises are rapidly modernizing data architectures to support AI-driven operations, real-time analytics, and large-scale digital workflows. As organizations expand adoption of Enterprise AI, generative AI assistants, and advanced analytics, traditional transactional databases are increasingly becoming performance bottlenecks.

Legacy architectures designed primarily for operational workloads often struggle to support concurrent analytical processing, rising data volumes, and real-time inferencing requirements. This creates growing pressure on infrastructure costs, query performance, and scalability across enterprise platforms.

Modern HTAP (Hybrid Transactional and Analytical Processing) architectures are emerging as a strategic enabler for AI-first enterprises by eliminating fragmented data movement, reducing operational complexity, and enabling analytics directly on live operational datasets.

Key Challenges

The organization’s expanding ServiceNow ecosystem introduced several scalability and operational challenges across business and platform teams:

• Platform engineering teams faced exponential data growth, increasing storage consumption and recurring infrastructure costs.
• Business users across ITSM, HRSD, SPM, and risk workflows experienced slower query and reporting performance, impacting productivity.
• AI and ML workloads demanded higher throughput, greater concurrency, and real-time analytics on live operational data.
• Data engineering teams relied on ETL pipelines and replicated analytical stores, increasing complexity and operational overhead.
• Enterprise architects needed to prepare the platform for Enterprise AI, Now Assist, and Knowledge Graph use cases without incremental storage expansion.
• Existing database architecture lacked sufficient scalability to support future Gen AI inferencing and enterprise-scale analytical workloads.

These challenges created operational and strategic constraints, requiring a modernized data foundation aligned to enterprise AI adoption and long-term scalability.

LTM’s Solution

LTM implemented India’s first migration to ServiceNow Raptor DB Pro, a modern HTAP database engine capable of supporting transactional and analytical workloads on the same live dataset.

The engagement combined platform modernization, performance engineering, and phased transformation execution to improve scalability while ensuring uninterrupted business operations.

Key Solution Elements Included:

• Implemented an HTAP architecture to unify transactional and analytical workloads on a single platform.
• Introduced column-store indexing and parallel query execution to accelerate reports, analytics, and high-volume queries.
• Reduced database size from 15.5 TB to 7.2 TB through advanced compression and storage optimization techniques.
• Eliminated ETL pipelines by enabling operational and analytical workloads directly on live datasets.
• Established an AI-ready data foundation for Enterprise AI, Now Assist, and future Knowledge Graph initiatives.
• Executed phased migration, structured cutovers, and post-migration performance tuning to maintain operational continuity.

Workloads Modernized on Raptor DB Pro

• Enterprise AI and Now Assist inferencing
• ITSM Pro+, ITOM, and ITAM workflows
• Strategic Portfolio Management (SPM)
• SecOps, IRM, BCM, and TPRM operations
• HRSD Pro+ workflows
• Enterprise-wide performance analytics, reporting, and list views

This transformation established a scalable, AI-ready ServiceNow data foundation aligned to long-term enterprise modernization goals.

Business Benefits

• Reduced production database size by 53%, releasing 8.3 TB of storage capacity.
• Achieved $2.43 million in annual cost avoidance by minimizing infrastructure expansion requirements.
• Improved server response times by 12.5%, accelerating user productivity across enterprise workflows.
• Realized 3X ROI on released storage capacity through optimized database utilization.
• Eliminated ETL pipelines and replicated analytical stores, simplifying architecture and reducing operational overhead.
• Strengthened scalability for Enterprise AI, Now Assist, and future AI-driven workloads.
• Improved analytical performance through parallel query execution and column-store optimization.
• Established a future-ready HTAP architecture capable of supporting growing enterprise-scale data and AI workloads. Voice of the Customer

“Data was growing fast, queries were slowing down, and costs were climbing. AI and ML workloads made things harder by demanding more storage and processing power. A smarter, modern platform solution was needed to keep up and run efficiently.”

— Prabhakar Cherukuri, Head – Enterprise Platforms, LTM Limited

Conclusion

LTM successfully delivered India’s first Raptor DB Pro migration, establishing an AI-ready, cost-optimized ServiceNow data foundation for a global IT organization operating at enterprise scale.

The transformation improved platform responsiveness, reduced infrastructure overhead, and simplified enterprise data operations while enabling future AI scalability. By modernizing the underlying data layer, the organization is now positioned to support the next generation of Enterprise AI, analytics, and intelligent workflow automation without compromising performance or operational efficiency.