Client Overview
A top-tier U.S. financial institution engaged 3Qi Labs to modernize its Commercial Card Expense Reporting system, which serves corporate customers and internal finance teams across multiple lines of business.
Business Challenge
The bank’s legacy reporting stack—built on traditional data warehouse technologies—could not keep up with the exponential growth in transaction volumes or the demand for real-time analytics. Key pain points included slow reporting performance, fragmented data, inflexible schema, and high maintenance overhead.
Objective
Deliver a modern, real-time analytics platform that could handle tens of millions of transactions per hour, provide sub-second search and visualization, offer 360° spend and merchant visibility, integrate with multiple enterprise systems, and remain secure and cloud-scalable.
About 3Qi Labs
Founded in 2009, 3Qi Labs is a boutique System Integrator and Elastic Reseller specializing in Data Engineering, Automation, and DevOps. As an Elastic Partner, 3Qi Labs delivers architecture design, implementation, and managed services around Elasticsearch-based analytics platforms for Fortune 500 financial institutions.
Solution Architecture
3Qi Labs designed and implemented a modern data and analytics stack centered on the Elastic (ELK) Stack—Elasticsearch, Logstash, and Kibana—augmented with complementary technologies to enable enterprise-grade performance and extensibility.
Implementation & Outcomes
3Qi Labs followed a phased approach — assessment, architecture design, MVP, full rollout, and enablement — to ensure alignment with the bank’s operational and compliance requirements.
Key Performance Indicators
- 10+ Million Transactions / Hour processed
- <1 Second Query Latency across terabytes of data
- 1 Million+ Active Users accessing reports
- 100+ Dashboards and Analytical Scenarios
- 8 External APIs integrated for Merchant and Customer Insights
- 99.99% Uptime and Zero Data Loss post-migration
Operational Benefits
Unified merchant and spend view across business units; elastic scaling; proactive alerting on anomalous spend patterns; and reduced report generation time from hours to seconds.
