AI-Augmented Engineering Accelerates Pharmacy Procurement Platform Expansion
About the Client
A national provider of an expert-built pharmacy procurement and inventory management software platform that automates compliance, centralizes workflows, optimizes savings, and provides real-time visibility into sites. They empower independent and long-term care pharmacies across the United States to foster better patient care and experience.
- Industry: Technology
The Challenge
As the pharmacy procurement platform expanded, the client's internal IT team faced mounting operational and technical pressure across the following areas due to reliance on legacy dependencies and manual processes.
- Feature Development: Growing feature enhancement demands across procurement and inventory management modules increased the difficulty of maximizing development speed while maintaining code quality.
- Modernization: Transitioning messaging systems from JBoss MQ to ActiveMQ and integrating Amazon S3 to expand cloud storage without service disruption became difficult. In addition, transforming Struts-based UI components to React was also resource-intensive. The team also faced challenges with slow pull request review cycles during active modernization efforts.
- Validation: Validating multiple complex database tables, as well as verifying and visualizing API responses at scale added operational burden on the QA experts.
- Testing: Creating manual test cases became a time-intensive affair, slowing validation cycles and impacting overall productivity.
- Documentation: Validating and summarizing Jira stories led to slower documentation cycles, affecting sprint readiness and cross-team clarity.
Our Solution
We adopted an AI-augmented engineering model that acted as a productivity accelerator throughout the procurement software expansion initiative.
Feature Development
AI tools, including Cursor, ChatGPT, and CodeRabbit, were leveraged during feature development to reduce repetitive engineering effort and shorten delivery timelines.
Modernization
CodeRabbit, ChatGPT, and Cursor AI tools were used to accelerate JBoss MQ-to-ActiveMQ migration, Amazon S3 integration, and Struts-to-React UI transformation while maintaining system stability. Pull request quality checks were also accelerated using CodeRabbit to shorten review cycles and streamline modernization workflows.
Validation
ChatGPT was leveraged for generating complex, nested SQL queries in DbVisualizer to optimize database validation processes. Additionally, Postman scripts were created via Postbot to improve API response validation efficiency.
Test Case Creation
ChatGPT was used to transform Jira stories into structured test cases, generate edge-case coverage, and refine validation scenarios. Recently, Claude was introduced to convert Jira stories into structured test cases on Qase.io, document bugs and defects, and expediate Playwright automation scripting. These approaches improved testing depth and reduced manual effort.
Documentation
ROVO was used for summarizing and clarifying Jira stories to accelerate documentation, sprint cycles, and cross-team understanding.
Technologies Used
Advanced technologies and AI-powered tools were strategically used to accelerate the delivery lifecycle.
-
Apache ActiveMQ
-
Amazon Simple Storage Service (S3)
-
ChatGPT
-
Claude
-
CodeRabbit
-
Cursor
-
DbVisualizer
-
Java
-
JBoss MQ
-
Kotlin
-
Postman
-
React
-
Rovo
-
Spring MVC
-
Qase.io
Business Impact
The AI-augmented engineering model drove measurable improvements across the procurement platform expansion lifecycle.
Here’s What Was Achieved
-
Accelerated Feature Delivery Cycles
By reducing repetitive engineering effort with AI-assisted development.
-
Enhanced Modernization Efficiency
Through automated migration, integration, transformation, and pull request review processes.
-
Improved Database Validation Accuracy
With AI-driven generation of complex SQL queries.
-
Optimized API Validation Efficiency
By reducing the time required to format validation scripts using AI.
-
Supercharged Bug Resolution Cycles and QA Preparedness
By using AI to reduce test case preparation time per sprint, eliminate manual query-based debugging, generate structured and actionable bug reports, and improve validation depth.
-
Streamlined Sprint Readiness and Team Collaboration
By automating the process of breaking Jira stories into clear summaries that ensured consistent and easy-to-understand documentation.
Other Case Studies
AI-Augmented Engineering and Testing Accelerate Grants Management Platform Modernization
I wanted to take a moment to highlight and commemorate the efforts from our dedicated PIO team. To preface, the projects we work on here are complex, with rigid objectives, budgets, and timelines. Expectations are always high and are ever-changing. From the start of our relationship, the IO team was able to exceed expectations and make our business wishes a reality.
Over the past year I have worked with them, they have worked very hard to understand our highly customized system and troubleshoot things with little or no documentation while managing to keep the business up and running normally. Without the hard work and dedication they have shown, I know we would have had some issues causing downtime or lost production. I am looking forward to continuing working them in the next year as well.
I have been working with Programmers IO for more than 5 years now and I have been pleased with all projects and developers that I have worked with. We have had a few issues here and there but they have always fixed and made it right. They have been an excellent addition to our business.
Let’s Build Your AI-Readiness Roadmap Together
Contact us for a free strategy session with our experts.
Talk to an AI Expert
