Case Studies
Real-world success stories showcasing measurable results
Proven Results Across Industries
We've delivered production-grade data engineering and analytics solutions that drive measurable business impact. Here are examples of how we've helped organizations transform their data infrastructure and decision-making processes.
Healthcare PBM Analytics Platform
The Challenge
Leadership at a major Pharmacy Benefit Manager (PBM) couldn't get a straight answer on claims trends, cost drivers, or member utilization, because the data behind those questions was scattered across 8+ different legacy systems with no unified reporting. Claims processing, member data, and pharmacy networks each lived in their own database, with no single place to see the full picture.
Our Solution
- Consolidated 8 source systems into a single Azure Synapse-based data warehouse
- Processed 5M+ records daily through automated Azure Data Factory pipelines
- Gave leadership claims analytics, cost management, and forecasting through Power BI dashboards
- Ensured HIPAA compliance with encryption, access controls, and audit logging
- Cut manual reporting work from 40 hours to 2 hours weekly with automated reporting
Results Achieved
Technology Stack
Location-Based Network Insights Automation
The Challenge
Business teams needed a reliable way to spot interactions happening outside the approved service network and quickly recommend nearby approved alternatives, especially during onboarding and expansion phases. The process for doing this was manual, slow, and difficult to scale.
Our Solution
- Analyzed historical interaction data to flag non-preferred network usage across large datasets
- Enriched user and provider data with geospatial coordinates
- Calculated precise proximity and ranked approved providers by distance
- Selected the top three nearest approved alternatives per case
- Delivered insights through a Power BI report with row-level security
- Automated the workflow to support recurring refreshes with no manual intervention
Results Achieved
Technology Stack
FMCG Supply Chain Optimization
The Challenge
A national FMCG distributor with 150+ distribution centers was caught in a costly inventory bind: frequent stockouts on one side, overstock on the other. Sales, inventory, and logistics data existed in separate systems with no integrated analytics, and forecasting was done manually in spreadsheets, resulting in just 40% forecast accuracy.
Our Solution
- Integrated ERP, WMS, and TMS systems into a single AWS Redshift data warehouse
- Implemented real-time ETL pipelines using AWS Glue and Apache Spark
- Delivered inventory visibility and demand forecasting through DOMO dashboards
- Created ML-based demand prediction models using Python and historical sales data
- Automated daily inventory reports sent to 150+ distribution center managers
Results Achieved
Technology Stack
Smart Manufacturing OEE Platform
The Challenge
An automotive parts manufacturer with 50+ production lines was flying blind on equipment performance: OEE (Overall Equipment Effectiveness) was still calculated manually weekly using spreadsheets. Unplanned downtime was costing $50K+ per incident, and quality issues weren't caught until batch completion.
Our Solution
- Collected real-time data from 200+ PLC controllers using Azure IoT Hub
- Processed 10M+ sensor readings daily through a new Azure Synapse data warehouse
- Delivered real-time visibility into OEE, quality metrics, and alerts through Power BI dashboards
- Implemented predictive maintenance models using historical failure data
- Integrated with SAP for production planning and inventory management
Results Achieved
Technology Stack
Drug Assistance Program: Vendor-to-In-House Migration
The Challenge
One of our healthcare clients had handed a critical drug assistance program to a third-party vendor. It worked, but the arrangement carried high costs, limited transparency, and little flexibility to adapt business rules. The client wanted the program back in-house, without losing functionality or disrupting the member experience.
Our Solution
- Reverse engineered the vendor's solution by analyzing outputs against input datasets, including member details, prescriptions, claims, and eligibility, to decode the underlying decision logic
- Rebuilt the program using SQL and ETL pipelines on the client's own infrastructure, leveraging Snowflake, Databricks, and Azure
- Developed BI reporting to support ongoing program management and visibility
- Validated that outputs matched 100% with the vendor's results to ensure zero disruption during cutover
- Optimized the program logic to improve member savings and give the client full transparency to adjust business rules going forward
Results Achieved
Technology Stack
Common Success Factors
While each project is unique, these key factors contribute to successful outcomes:
Clear Business Goals
Every project starts with defining measurable KPIs and success metrics aligned with business objectives.
Iterative Delivery
We deliver value in phases with regular demos and feedback, not waiting months for a "big bang" launch.
Knowledge Transfer
We train your team to maintain and extend solutions, providing documentation and hands-on training.
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