Your AI-Built Integration Is Breaking Things. We Fix It.
Sound Familiar?The moment you realise the AI code isn't production-ready.
Worked fine in staging. Failed on your first big trading day.
The integration looked solid in testing. Then real order volumes hit, concurrent requests stacked up, and it started dropping transactions. AI writes code that satisfies a use case. It can't account for your real concurrency, your order volumes, or the edge cases that only appear at scale.
Inventory stopped syncing after a routine API update.
Your ERP vendor shipped an update. Your payment gateway changed its authentication flow. The AI-built connector had no error handling for version changes. It just silently stopped working.
Orders were being lost for days before anyone noticed.
No alerts. No error logs your team could read. Just a gap in fulfilment that customers noticed before you did. AI-generated integrations rarely include the monitoring and alerting that production systems require.
The developer who built it has moved on. No documentation. No tests.
The integration runs. Nobody on your team understands it well enough to change it safely. Any modification risks breaking something else, and there's no test coverage to tell you what that something is.
Why AI-generated Code Fails
ChatGPT, GitHub Copilot, Cursor, and Windsurf write ecommerce integration code fast. Code that ships in days because the AI doesn't need to understand your Shopify store, your Adobe Commerce configuration, your BigCommerce setup, or how your specific ERP batches inventory. It just needs to match the API documentation it was trained on. That gap, between what the documentation describes and what your production environment actually does, is where vibe-coded integrations fail.
No awareness of your systems
AI tools generate code against generic API documentation. They don't know that your ERP batches inventory updates every 15 minutes, or that your WMS confirms picks asynchronously, or that your POS expects real-time stock data. Integrations built without this context create data mismatches and sync failures across your operations, on Shopify, Adobe Commerce, BigCommerce, and every platform in between.
No handling of real-world load
Code that runs cleanly at low volume often breaks under peak trading conditions: concurrent orders, rapid inventory changes, API rate limits. AI tools optimise for the expected case. Your busiest trading days are almost entirely edge cases, and vibe-coded integrations were never tested against them.
No production-grade error recovery
When a payment authorisation succeeds but a downstream system fails, the integration needs to know how to reverse, retry, or escalate without creating orphaned transactions or double charges. AI-generated code rarely accounts for the full range of failure states your integration layer will encounter in production.
No ongoing compatibility
Your ERP vendor ships an API update. Your payment gateway changes its authentication flow. A shipping provider deprecates an endpoint. AI-generated integrations are built against a snapshot. They don't adapt when the systems around them change, and they often break silently.
The EvidenceHow AI is Driving Tech-debt in Ecommerce
Multiple independent research programmes have measured the quality difference between AI-generated code and human-written code in production environments. The findings are consistent.
Veracode tested 100+ LLMs across four languages. AI-generated code failed security benchmarks 45% of the time.
Source: Veracode, 2025 GenAI Code Security Report.
470 real-world pull requests analysed. AI code also showed 1.75x more logic errors and nearly 8x more excessive I/O operations.
Source: CodeRabbit, "State of AI vs Human Code Generation," December 2025.
Google's DORA report also found that higher AI adoption correlates with increased software delivery instability.
Source: Google Cloud, 2025 DORA Report.
Two outages in one week, both traced to AI-assisted code changes. Amazon imposed a 90-day code safety reset across 335 systems.
Source: VentureBeat, April 2026.
Fontis closes the gaps AI tools leave behind
Fontis doesn't replace AI-generated code wholesale. We assess what you've built, identify where it falls short of production requirements, and engineer the fixes that make it reliable. The goal is working systems, not a rewrite for the sake of it.
Integration architecture review
We map how your systems actually communicate - your ERP, WMS, POS, CDP, payment gateway, and ecommerce platform - and test your AI-generated integration code against that reality. Where the code assumes conditions that don't match your production environment, we flag it and scope the fix.
Production hardening
We stress-test integration logic against peak trading conditions, concurrent transaction loads, and the async behaviours your systems actually exhibit. Code that needs to survive Black Friday gets engineered for Black Friday, not for a quiet Tuesday.
Security and data integrity
We audit AI-generated code for authentication gaps, input validation failures, and data handling practices that could expose customer information or create compliance risk. Where your integration layer moves customer data between systems, we verify it's doing so safely and accurately.
Documentation and maintainability
AI-generated code that nobody on your team understands is a liability. We document integration logic, add test coverage, and structure the codebase so that it can be maintained and extended without guesswork.
Start with a conversation. Understand the risks. Then decide.
Fontis works with ecommerce brands to audit, remediate, and maintain the integration infrastructure that AI tools are now generating code for.
Discovery Call
30 minutes with a Fontis engineer. No pitch deck. No sales process. A technical conversation about your stack, what you've built with AI tools, and where you're seeing problems or concerns.
Written Report
Fontis engineers review your integration code against production conditions. The deliverable is a written report: every risk rated by severity, with a prioritised remediation roadmap. The report is yours to keep, regardless of next steps.
Remediation & Ongoing Support
Rewriting critical integration logic, implementing proper error handling, hardening for peak trading, adding test coverage, and documenting everything for your team. For complex stacks, Fontis offers retainer-based access to expert integration engineers.
Track Record Trusted by Australian Retailers
Fontis has delivered integration engineering across ERP, WMS, POS, CDP, and ecommerce platform environments for Australian retailers managing multi-channel operations. Our work spans platform migrations, middleware development, API orchestration, and the resolution of integration failures that other agencies couldn't diagnose.
Common Concerns About AI Integration Risk
Straight answers to the questions ecommerce teams ask when they're weighing whether to act on AI-generated integration risk.
We can fix it ourselves. The developer who built it is still available.
The developer who vibe-coded the integration with AI tools may not have the integration engineering experience to identify what production-grade systems require. AI-generated code looks plausible; the failure modes are subtle. A fresh perspective from engineers who have seen these patterns across hundreds of production environments finds things the original developer won't be looking for.
It's mostly working. We'll clean it up when we have time.
Integrations that are "mostly working" tend to fail at the worst possible moment - during a peak trading event, after an upstream API update, or when order volume crosses a threshold the code was never tested against. The longer AI-generated code runs unreviewed in production, the more dependent your operations become on it. Cleaning it up gets harder, not easier, over time.
We don't have budget for a full engagement right now.
The discovery call is free, and the audit is a fixed-scope deliverable with a known cost before you commit to anything. If the audit finds no significant issues, that's a valuable result as you have documented confirmation that the integration meets production standards. If it finds risks, the report gives you a prioritised roadmap so you can address the highest-severity items first, within your budget constraints.
We'll just rebuild the whole integration from scratch.
A full rebuild takes time, carries its own risk, and may not be necessary. In most cases, AI-generated integrations have specific, identifiable failure points rather than wholesale structural problems. An audit tells you exactly what needs to change, and often the scope is far smaller than you expect. A targeted fix is faster, cheaper, and less risky than rebuilding systems that are currently running.
Why Fontis
Real engineers.
Real production systems.
Get Started30 minutes with a Fontis engineer. No pitch. No obligation.
Book a discovery call and we'll talk through your stack, your concerns, and whether a deeper assessment makes sense. The call is with an engineer, not a sales team.
Fontis has been engineering ecommerce integration infrastructure for over 20 years. The systems that AI tools are now generating code for, ERP connections, warehouse management synchronisation, POS integration, and customer data pipelines, are all systems Fontis engineers have been building, maintaining, and troubleshooting across hundreds of production environments.
Fontis is independently owned and vendor-agnostic. When we recommend a solution, it's because it's the right fit for your architecture.
If your next peak trading event is within 90 days, now is the time to understand where the risks sit. Audit lead times depend on stack complexity.