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AI-Assisted Adobe Commerce Engineering for a Multi-Country Pharmacy Platform
Headless GraphQL/PWA performance optimization, checkout engineering, and AI-assisted development and QA across a five-market Adobe Commerce platform with ~830 custom modules.
Project Summary
Dr. Max Group operates a shared Adobe Commerce backend across five markets, with roughly 830 custom modules and around 70 market-specific payment integrations layered on top. That density made checkout and performance improvements inherently high-risk: a change to the shared codebase could affect all five country-specific deployments. Elogic Commerce embedded Adobe Commerce engineering capacity into Dr. Max’s existing team to isolate performance bottlenecks, optimize the shared checkout flow, and introduce AI-assisted development and QA practices into the engineering workflow. Page response time improved from 2.3s to 1.4s during the performance engineering work, while development cycle time was 30% shorter during the engagement.
Key Outcomes
5
European markets from one shared Adobe Commerce backend
~830
custom Adobe Commerce modules built on top of the platform core
39%
faster page response time
30%
shorter development cycle time
About the Client
Dr. Max Group is a major European pharmacy and healthcare retailer operating across Central and Eastern Europe, with legal presence in 17 countries. Its ecommerce platform supports five core markets from a single shared Adobe Commerce backend and headless PWA architecture, integrating a custom Order Service, FarmIS as the source of product data, Mirakl for marketplace operations, and a hybrid Elasticsearch/Algolia search setup, across a catalog of 10,000+ SKUs.
Why the Engagement Was Complex
One Adobe Commerce backend serves five countries, built into separate per-country images, so any change had to be validated across all five before release.
~830 custom modules and around 70 market-specific payment integrations sit on top of the platform core, meaning narrowly scoped changes could have wide blast radius.
The Magento backend exposes data via GraphQL to a separately maintained PWA frontend, so performance and checkout work often spanned two repositories.
Order management, PIM, pricing, offers, marketplace, search, identity, payments, and logistics are all handled by distinct connected services, each with its own data ownership boundary.
What Elogic Commerce Changed
Embedded an Adobe Commerce engineer directly inside Dr. Max’s existing sprint, QA, code-review, and Azure DevOps deployment processes
Analyzed and optimized the shared checkout flow for friction and bottlenecks, preserving compatibility across the five-market codebase and its connected order, PIM, search, and marketplace systems
Identified and resolved performance bottlenecks across the headless GraphQL/PWA architecture
Ran multi-market regression testing and release validation to protect against cross-country regressions
Introduced AI-assisted development practices as part of the engineering workflow, including code generation, refactoring, test creation, code analysis, and debugging, alongside Dr. Max’s existing quality tooling (PHPStan, PHPUnit, Magento Coding Standard, PHP-CS-Fixer)
Outcomes & Engineering Impact
39%
faster page response time, from approximately 2.3s to 1.4s, following performance bottleneck remediation within the headless GraphQL/PWA architecture
30%
shorter development cycle time during the engagement
AI-assisted engineering
embedded into the existing delivery process, with production changes remaining under developer and senior-engineer review
AI Engineering Approach
Elogic Commerce incorporated AI-assisted engineering into the existing Adobe Commerce development lifecycle rather than treating it as a standalone experiment. AI tooling supported code generation and refactoring, test creation, code analysis, debugging, and validation, operating alongside Dr. Max’s existing quality tooling, while engineers retained responsibility for architecture, code review, testing, and production deployment.
Capabilities Demonstrated
01
Adobe Commerce (Magento) performance engineering
02
Headless GraphQL/PWA architecture
03
Adobe Commerce checkout optimization
04
Shared-codebase, multi-image commerce architecture
05
Complex ERP/OMS integration (custom Order Service, IPF Eshop API)
06
PIM integration (FarmIS)
07
Hybrid Elasticsearch/Algolia search architecture
08
Marketplace integration (Mirakl)
09
AI-assisted software engineering, including code generation, refactoring, and debugging
10
AI-assisted QA and test automation
11
Embedded engineering team extension
12
Multi-market regression testing and release validation
When This Solution Is a Good Fit
This approach is ideal for:
01
Large multi-country retailers on a shared Adobe Commerce codebase, especially those with headless or highly integrated architectures02
Those who need checkout and performance improvements delivered carefully across live markets03
Those who want flexible engineering capacity, augmented by AI-assisted development practices, embedded directly in their existing team