A large manufacturing conglomerate had spent over a decade running mission-critical operations on aging on-premises infrastructure completely incompatible with the AI capabilities their competitors were already deploying. orangemantra was brought in to change that. Over 180 days, our cloud migrations services executed a phased, zero-downtime migration of 47 legacy applications, rebuilt their data architecture from the ground up, and delivered a cloud environment that now runs predictive maintenance, AI-driven demand forecasting, and real-time supply chain intelligence across three continents.
Manufacturing & Supply Chain
Cloud & Migration
The client’s on-premises data centers were consuming over 40% of their IT budget just to stay operational. Scaling up for seasonal demand spikes meant weeks of procurement lead time. Every AI pilot they attempted ran into hardware limitations that forced engineers to compromise on model complexity or abandon experiments entirely. Their infrastructure was not a foundation but a bottleneck.
Operational data lived across SAP ERP, a proprietary MES platform, three separate warehouse management systems, and dozens of spreadsheet-driven workflows. None of these systems spoke to each other in real time. The data quality team was spending 60% of their time just reconciling reports between systems. Running any kind of unified analytics or AI model across this landscape was practically impossible.
Eighteen months before we were engaged, the client had attempted a self-managed cloud migration that stalled at 30% completion. A critical production system went offline during a cutover and took 11 hours to restore. That incident had made the leadership team extremely cautious and eroded confidence in cloud adoption internally. Winning the trust of both the IT team and senior leadership was as important as the technical work itself
The client operated in three regulated markets with different data residency requirements. Their existing on-premises setup had grown organically over years, accumulating custom configurations and undocumented dependencies. Before a single workload could move, we needed to build a governance framework, map every dependency, and design a multi-region cloud architecture that satisfied compliance requirements across all jurisdictions.
The client had good operational fundamentals, but their infrastructure was actively blocking growth. Here is what we found when we started the discovery process
Nearly half the IT spend went toward keeping aging servers running rather than building new capabilities.
Reports were 24–72 hours stale. Operational decisions were being made on yesterday’s numbers.
Data residency obligations in the EU and APAC regions were difficult to enforce on a centralized on-prem setup.
Every ML pilot stalled due to compute limitations. Three separate AI projects had been shelved in 12 months.
After 180 days, the numbers told a clear story. Here is what changed for the client across infrastructure, operations, and AI capability:
Eliminating on-premise hardware maintenance, consolidating vendors, and right-sizing cloud resources reduced total infrastructure spend by over half within the first six months post-migration.
What previously took six to eight weeks of infrastructure procurement now happens in hours. Three new AI-powered applications were deployed in the first month after migration completed capabilities that were impossible before.
Data residency obligations in the EU and APAC regions were difficult to enforce on a centralized on-prem setup.
Most enterprise AI initiatives fail not because of bad ideas but because the infrastructure underneath cannot support them. If your team is hitting the same ceiling, we can help you break through it.
