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Accelerating Medical Affairs Platform Development With an AI-Enabled Engineering Pod

At a glance

  • Client
Global technology and medical affairs company
  • Industry
Life Sciences / Pharmaceuticals
  • Solution Provided
AI-Enabled Engineering Pod, Software Development Acceleration, AI in the SDLC
  • Technologies Used
GitHub Copilot, Cursor, Custom GPTs (PR Reviewer, Epic-to-Story Converter, QA Automation, Bug Reporter), Jira, REST APIs, Domain-Driven Design
The Challenge

A medical affairs and compliance software client is building a next-generation SaaS platform for medical affairs designed to elevate and synchronize medical information, improve compliance, and accelerate decision-making across complex life sciences workflows. The platform had been developed using in-house teams, but the client sought a partner to:

  • Accelerate delivery velocity without compromising quality
  • Introduce best practices for deploying AI across the software development lifecycle (SDLC)
  • Improve consistency and scalability across engineering, QA, and product delivery as the platform evolved

The challenge was not just to add capacity, but to modernize how software was built, reviewed, tested, and delivered using AI-enabled workflows.

The Solution

Gorilla Logic formed a dedicated AI-enabled Pod that embedded directly with the client’s team and delivery model. The Pod included:

  • Product Owner
  • Scrum Master
  • Senior and Mid-Level Developers
  • QA Automation Engineers
  • UX Designer

The engagement focused on transforming the SDLC with AI from the ground up, introducing AI tooling across engineering, QA, and product workflows while maintaining alignment with the client’s in-house teams.

AI-Enabled Engineering
  • Automated code suggestions, refactoring proposals, and real-time syntax validation
  • AI-generated pull request summaries, logic error detection, and optimization recommendations
  • Standardized REST API design using Domain-Driven Design principles to reduce duplication and improve scalability
AI-Driven QA Productivity
  • AI-generated test cases mapped to Jira stories and acceptance criteria
  • Structured, higher-quality bug reporting with duplicate detection and test log linkage
  • Automated QA documentation to standardize frameworks and speed onboarding
Product & Delivery Efficiency
  • Automated Jira story creation from epics, including acceptance criteria, QA approach, and complexity estimates
  • AI-generated meeting summaries, action items, and delivery reports to improve stakeholder alignment

The Result

In an ongoing partnership, Gorilla Logic’s AI-enabled Pod is delivering measurable improvements in productivity, quality, and delivery efficiency, including:

Faster Development Cycles
  • 40–50%: acceleration in development cycles through AI-assisted coding, refactoring, and review workflows
  • 28%: sprint velocity increase, delivering 60 story points in Sprint 4 while simultaneously reducing bug counts from 6 to 2
Improved Code Quality & Review Efficiency
  • Shorter pull request review cycles with reduced dependency on senior reviewers
  • More consistent API design, improving maintainability, scalability, and backward compatibility across the EnvisionOne v2 platform
QA Efficiency Gains
  • 35–45%: reduction in manual QA effort through AI-generated test cases and structured defect reporting
  • Improved test coverage, defect clarity, and faster triage