Four representative engagements across banking, telecom, and manufacturing. Each follows the same structure: the technical challenge, the solution architecture, and the measurable business impact.
The bank's AML monitoring was fragmented across business units, each running its own rule sets and case-management workflows on ageing SAS 9.4 infrastructure. Regulatory reporting required manual reconciliation between units, and alert volumes were producing a false-positive rate that was consuming investigator capacity faster than the bank could hire.
ZIDEA consolidated the fragmented environment into a single SAS multi-tenant AML framework, standardising scenario logic across business units while preserving unit-specific thresholds where regulation required it. We rebuilt the alert-scoring layer using SAS Fraud Framework and Visual Investigator for case management, and ran a structured rule-tuning programme against twelve months of historical alert data before go-live.
A national telecom operator's enterprise data warehouse, feeding customer intelligence and demand forecasting for hundreds of millions of subscribers, was running on ageing on-premise infrastructure that could no longer scale with data volume growth, and could not be taken offline for the length of time a conventional migration would require.
ZIDEA designed a phased migration to a multi-cloud footprint, moving workloads in dependency order so that live reporting SLAs were never breached during cutover. Big-data pipelines were re-platformed onto Spark and Kafka for streaming ingestion, with the warehousing layer distributed across cloud-native storage to remove the single point of failure that on-premise infrastructure had created.
The bank's risk and reporting functions were built on a decade of accumulated SAS 9.4 code: thousands of jobs with undocumented interdependencies. Vendor end-of-support timelines forced a move to Viya 4, but the bank's compliance calendar left no room for a reporting blackout during the transition.
ZIDEA ran a full code and jobs inventory before writing a line of migration code, classifying each job by risk, complexity, and business criticality. We migrated in parallel-run waves (old and new environments producing the same reports side by side) so discrepancies surfaced and were resolved before the legacy environment was decommissioned, not after.
The manufacturer needed a predictive demand-forecasting capability across its dealer network, but a fully onshore analytics build was outside the programme's budget envelope, and the internal team lacked bandwidth for governance if delivery moved fully offshore.
ZIDEA delivered under a hybrid onshore-offshore model: a small onshore team handling stakeholder alignment and requirements, backed by our Pune delivery centre building the forecasting models and data pipelines. Structured technical project governance kept both sides working against the same backlog and acceptance criteria throughout.