Success Story – Energy & Utilities

Enabled 40% Faster Development Cycles and 30% Fewer Defects Through AI-powered Mobile Modernization and Intelligent Automation Across Development Workflows

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Engagement Background

Client’s customer-facing mobile application, central to discovery, routing, and charging, had begun to face constraints in feature velocity, scalability, and testing effort as EV adoption accelerated. At the same time, the GPTC initiative aimed to evolve this into a unified customer digital experience platform.

As the program progressed, Apexon also expanded the scope to a full platform modernization, accelerating timelines and aligning with the GPTC vision.

This resulted in ~20–30% product acceleration, reducing development cycle times, improving quality, and optimizing delivery costs, while enabling faster time-to-market.

About the Client

The client is a leading provider of electric vehicle (EV) charging infrastructure and digital charging solutions, operating one of the largest networks of independently owned charging stations globally.

The company enables businesses, fleets, and drivers with an integrated ecosystem that combines hardware, software, and cloud services to deliver seamless charging experiences.

At the center of this ecosystem is client’s customer-facing digital platform, which connects users to charger discovery, route planning, payments, and charging session management, making it a critical driver of customer engagement and long-term value creation.

The Challenge

Client’s customer-facing mobile application was central to user engagement, but the experience had become fragmented and feature-led, limiting its evolution into a scalable digital platform and constraining future revenue opportunities.

KEY CHALLENGES:

Fixed Deadline with High Business Impact
Fixed Deadline with High Business Impact

Legacy Architecture Slowing Innovation The existing mobile architecture made it difficult to introduce new features and enhancements, increasing development complexity and slowing time-to-market.

Complex Legacy Codebase with a New Team
Complex Legacy Codebase with a New Team

High Effort for Feature Development Adding new capabilities required significant engineering effort due to tightly coupled components and limited flexibility in the existing system.

Parallel Modernization and Cloud Migration
Parallel Modernization and Cloud Migration

Manual Testing Impacting Velocity and Quality Heavy reliance on manual testing cycles slowed releases, reduced efficiency, and increased the risk of defects post-deployment.

Backlog Creation Bottlenecks
Backlog Creation Bottlenecks

Limited Scalability for Future Growth The legacy structure lacked the scalability needed to support growing user demand and evolving product requirements.

Maintaining Enterprise-Grade Quality Under Pressure
Maintaining Enterprise-Grade Quality Under Pressure

Need for Modernization Without Disruption As a mission-critical application, modernization had to be executed without impacting user experience or service continuity.

The Solution

Apexon introduced an AI-assisted engineering approach to modernize the customer-facing app, embedding intelligence across the software development lifecycle to accelerate delivery, improve quality, and enable continuous innovation.

GitHub Copilot was embedded into daily workflows across engineering and QA. This enabled faster coding and refactoring, AI-generated unit tests, accelerated QA cycles, and more consistent code through AI-assisted reviews, all while supporting continuous agile delivery.

Why Apexon: Apexon’s ability to operationalize AI within real-world engineering workflows, combined with deep expertise in mobile modernization and agile delivery, enabled client to adopt AI safely, effectively, and at scale without disrupting existing systems.

KEY COMPONENTS OF THE APEXON SOLUTION:

AI-Assisted Development
AI-Assisted Development

Accelerated coding and refactoring using GitHub Copilot, improving engineering productivity

AI-Generated Unit Testing

Increased test coverage and reduced manual effort through automated test creation

AI-Supported QA Cycles

Streamlined testing processes to enable faster and more reliable releases

AI-Driven Code Reviews

Improved code consistency and quality through intelligent review mechanisms

Continuous Agile Delivery Enablement

Established a scalable, AI-powered SDLC supporting ongoing modernization

Key Results

Apexon’s AI-led modernization delivered 20–30% product acceleration, improving time-to-market, quality, and cost efficiency.

Accelerated Development and Delivery

Accelerated Development and Delivery
  • Achieved 40% faster requirements-to-development cycle
  • Reduced unit test creation effort by 50%
  • Enabled faster release cycles with streamlined QA processes

Improved Quality and Reliability

Improved Quality and Reliability
  • Reduced post-release defects by 30%
  • Strengthened code consistency through AI-assisted reviews
  • Increased test coverage and reliability of releases

Scalable, Future-Ready Engineering Model

Scalable, Future-Ready Engineering Model
  • Achieved 25% productivity uplift across teams
  • Established a repeatable AI-enabled engineering model
  • Enabled continuous modernization without disrupting user experience