A real-world case study in collaborative problem-solving

When a client came to me with an intermittent WooCommerce checkout problem that had stumped WP Engine support twice, I knew I needed to bring out the big guns. What followed was a fascinating example of how human expertise and AI capabilities (man and machine) can combine to crack a tough technical case.

The Problem

My client runs a small eCommerce site selling hiking guidebooks. Customers were reporting “cart failed” errors when trying to check out — but not every time. Some orders went through fine. The error message was cryptic: “cURL error 28: Operation timed out after 30001 milliseconds with 0 bytes received.”

She’d already contacted WP Engine support twice. They’d found some issues, applied some fixes, but the problem persisted. One customer had tried to order over three days using every payment method available — Stripe, PayPal, Apple Pay, Google Pay — and failed every time with the same error.

My initial instinct? Payment gateway configuration issue. That’s usually where checkout problems live.

I was wrong.

Enter the AI Assistant

I’ve been experimenting with using Claude (Anthropic’s AI) as a collaborative partner for complex troubleshooting. For this case, I fed it everything I had: two WP Engine support chat transcripts, server error logs, WooCommerce debug logs, and customer correspondence.

What happened next genuinely impressed me.

The Collaboration in Action

Round 1: Initial Analysis

I uploaded the support transcripts and server logs. Within minutes, Claude had identified something I’d glossed over in the WP Engine chat: a “KILLED QUERY” event from Jetpack that was 20,778 characters long. WP Engine’s Long Query Governor automatically kills any query over 16,000 characters.

Claude connected the dots: when Jetpack tried to sync during checkout, the oversized query got killed, the PHP process hung waiting for a response, and after 30 seconds — timeout. cURL error 28.

But I wasn’t fully convinced yet. Could this really affect ALL payment methods?

Round 2: The Debug Log Deep Dive

I uploaded a WooCommerce debug log from a failed order. Claude parsed through the JSON (which was dense and ugly) and extracted the key details: the order was for 2 guidebooks plus a GPX file, totaling $55.89, from a customer in Green Bay, Wisconsin. Both Stripe and PayPal were available and properly configured. The cart validation had succeeded — it was the order placement step that failed.

This was the smoking gun. The payment gateways were fine. The failure happened before any gateway was even contacted.

Round 3: Timeline Correlation

Here’s where it got really interesting. I uploaded the customer’s email thread — three messages over three days documenting her failed attempts and the payment methods she’d tried.

I asked Claude to correlate her reported times with the debug logs. It converted UTC timestamps to Denver time (the site’s timezone) and matched them up:

• Customer reported trying at 4:38 PM on January 5th → Debug logs showed failed attempts at 4:30, 4:35, and 4:36 PM
• Customer reported trying at 5:37 PM on January 6th → Debug log showed a failed attempt at 5:36 PM

The times matched within minutes. We could now definitively tie a specific customer’s experience to specific server-side failures.

Round 4: Ruling Out Payment Gateways

With the correlation complete, Claude made a point that crystallized the whole investigation: “No single payment gateway issue could cause identical failures across four different payment methods on multiple devices.”

That one sentence closed the case on my original hypothesis. This customer had tried Stripe, PayPal, Apple Pay, and Google Pay on both her phone and laptop. All failed identically. The only common denominator was the server-side checkout process — and Jetpack was interfering with it.

The Resolution

Armed with this analysis, my client deactivated Jetpack. No cart failures since.

The irony? WP Engine had actually identified the Jetpack issue on January 5th and applied a fix (disabling the query governor). But that fix didn’t hold because the underlying sync queue was still causing resource contention. It took our deeper analysis to understand why the problem persisted and what the actual solution needed to be.

What I Learned About AI Collaboration

This case changed how I think about AI-assisted troubleshooting. Here’s what worked:

AI excels at pattern recognition across large datasets. I might have eventually noticed the Jetpack query issue in those logs, but Claude spotted it immediately and connected it to the customer-facing error.

AI can correlate information humans would struggle to connect. Matching customer-reported times to server logs across timezones, while also tracking which debug log files corresponded to which events — that’s tedious work for a human brain but trivial for AI.

The human still drives the investigation. I decided what files to upload, what questions to ask, and which threads to pull. When Claude’s first assessment pointed toward Jetpack but I wasn’t convinced about payment gateways, I pushed back — and that led to the debug log analysis that sealed the case.

AI can produce professional deliverables fast. Throughout this investigation, Claude generated three versions of a comprehensive analysis document, each updated as new evidence came in. These weren’t rough notes — they were polished, client-ready documents with tables, timelines, and clear recommendations.

The Takeaway for Freelancers

If you’re a freelancer doing technical troubleshooting, consider AI as a collaborative partner rather than just a search engine replacement. The key is feeding it complete context — logs, transcripts, correspondence, all of it — and then engaging in a back-and-forth dialogue.

Don’t just ask “what’s wrong?” Instead, present your hypothesis, let the AI challenge or confirm it with evidence, and iterate. In this case, my payment gateway hypothesis was wrong, but working through why it was wrong led us to the actual answer.

The future of technical consulting isn’t AI replacing humans or humans ignoring AI. It’s the two working together, each contributing what they do best: human intuition and client relationships paired with AI’s ability to process, correlate, and document at scale.

One last thing, I asked Clause to create a synopsis of our collaboration that I could give to the client. Claude produced a 1500 word chronology of our investigation and analysis complete with tables and near-term and long-term recommendations. I’m thinking the analysis with recommendations will gain me even more work with the client. Cha-ching!

Ernie St. Gelais is the founder of Sitez Incorporated, providing WordPress development, monitoring, and technical consulting to small and medium businesses. With 30 years of experience in web technologies, he’s always looking for better ways to serve his clients — even if that means teaming up with an AI.