Every month, SaaS companies silently hemorrhage revenue through checkout failures they never see. While you’re focused on churn metrics, a bigger threat lurks in your payment flows. The Thoughtworks Technology Radar just validated what we’ve been saying: traditional QA is broken for modern revenue systems, and AI-powered testing isn’t just the future—it’s the present.
Picture this: You’re the CTO of a fast-growing SaaS company with $20M ARR. Your churn metrics look healthy. Your engineering team ships features weekly. Your board is happy.
Then you discover you’ve been losing $390,000 annually to silent checkout failures.
This isn’t hypothetical. It’s the median reality for mid-market SaaS companies, calculated from our analysis of 27 customers’ pre-QA.tech revenue flows. The formula is devastatingly simple:
Annual Revenue Loss = ARR × (Checkout Failure % + Involuntary Churn %) × Detection Lag
Let’s break this down with real numbers:
Your silent revenue leak: $390,000 per year
But here’s what makes this worse: while you’re losing that revenue, your competitors using autonomous QA are capturing it.
In April 2025, Thoughtworks Technology Radar—one of the industry’s most influential technology guides—declared what we’ve known for years: “Traditional QA practices weren’t built to handle things like model drift, hallucinations or unpredictable behavior.”
They went further, specifically highlighting QA.tech as an example of the new breed of testing tools that use “multi-modal LLMs fine-tuned for UI snapshot processing, allowing test scripts written in natural language to navigate an application.”
This isn’t just validation. It’s a wake-up call for every company still relying on Selenium, Cypress and Playwright scripts and DOM selectors to protect their revenue.
Modern checkout flows are a nightmare of complexity:
As Jeff Titterton, GM of Stripe Billing, puts it: “Involuntary churn quietly wipes out more ARR than competitive loss.”
Let’s talk about what traditional testing actually costs—beyond the obvious revenue leakage.
Our analysis of 31 engineering teams revealed a shocking truth: 3.2 developer days per sprint are lost to test maintenance. That’s not writing new tests. That’s fixing broken selectors, updating deprecated APIs, and chasing flaky test failures.
Here’s the breakdown:
That’s before considering the opportunity cost of engineers not building features.
A developer on Reddit summed it up best:
“Spent 7 hours simulating Stripe SCPI-30 scenarios. Still can’t reproduce user’s error #413.”
This is the hidden tax of modern testing — burning days debugging integrations you don’t control, chasing edge cases you can’t reproduce. You’re not just testing your code — you’re testing a tangled web of third-party systems.
But what if testing didn’t drain your team? What if it scaled with your code instead of breaking with it?
Traditional testing asks: “Does this button click?” Autonomous testing asks: “Can a customer give us money?”
The difference is revolutionary.
When Thoughtworks highlighted our approach, they zeroed in on what makes us different: we don’t just look at code. We see what your users see.
Here’s how it works:
"Complete checkout as a returning customer with a saved card in France"
Here’s the breakthrough: you don’t even need to define the steps.
Our AI continuously scans your application, building an “action graph”—a living map of everything users can do. When you create a test, you just declare the goal. The AI figures out the path.
Traditional Selenium Approach (Breaks constantly):
driver.findElement(By.id("checkout-btn")).click();
wait.until(ExpectedConditions.presenceOfElementLocated(By.id("stripe-iframe")));
driver.switchTo().frame("stripe-iframe");
driver.findElement(By.css("[data-testid='card-number']")).sendKeys("4242424242424242");
// 200 more lines of brittle code...
QA.tech Approach (Adapts automatically):
test: "European customer checkout with 3D Secure"
steps:
- "Add premium plan to cart"
- "Apply discount code SAVE20"
- "Complete checkout with French billing address"
- "Handle 3D Secure authentication"
verify:
- "Order confirmation shows €47.20 including VAT"
- "Stripe webhook confirms payment captured"
Behind the scenes, our agent:
You CAN provide specific steps if you want to guide the agent:
test: "High-value European checkout"
goal: "Complete €5,000 purchase with 3D Secure"
steps: # Optional - only if you need specific behavior
- "Add enterprise plan to cart"
- "Apply VIP discount code"
# The agent figures out the rest
But for most tests? Just state what you want to achieve. No more writing 40-step scripts that break next week.
The QA.tech version isn’t just shorter—it’s intelligent. When Stripe updates their checkout modal next week, our test adapts. The Selenium version? Dead on arrival.
Lavendla, a platform connecting people with end-of-life planning professionals across Sweden, Denmark, and the UK, faced a challenge familiar to many growing companies: complex checkout flows across multiple markets with a lean team of just four developers.
The Challenge:
Charlie Lindberg, Head of Growth at Lavendla, explains their turning point:
“Our goal with QA.tech was to move beyond predefined test cases. We wanted a solution that could spot unusual user behavior and catch issues before real users encounter them.”
The Solution: Instead of writing hundreds of scripts for each market variant, Lavendla deployed QA.tech’s autonomous agents. The setup was remarkably simple:
“The platform’s design is incredibly appealing—simple, seamless, and efficient. Just input the URL, and it runs, even suggesting additional tests. It’s very straightforward and streamlined.”
The Results:
As Charlie notes: “We weren’t prioritizing testing before, but QA.tech has given us the ability to focus on it now. Previously, we’d deprioritized testing in favor of building features, but as we scale, downtime impacts us more.”
SHOPLAB, a sophisticated Swedish e-commerce management platform, represents the other end of the spectrum—high-velocity development with daily releases.
The Challenge:
Alexander Fugah, CEO at SHOPLAB, describes their situation:
“We were releasing new updates almost daily, but it became hard to keep up. Our process was tedious and we needed more coverage without hiring more staff.”
The Integration: SHOPLAB integrated QA.tech directly into their CI/CD pipeline using GitHub Actions:
“QA.tech fits seamlessly into our workflow. We use GitHub Actions, and it was simple to hook it up to our deployment process. Now, every time we push a release, QA.tech runs the tests.”
The Transformation:
The key insight from Alexander:
“With QA.tech, we’ve cut down our manual testing time by over 70%. We no longer worry about bugs slipping through—QA.tech catches them automatically.”
Let’s get specific about returns. Here’s our standardized ROI model based on 27 customer implementations:
Costs:
Returns:
ROI: 1,867%
But the real value compounds in Year 2 when you’re preventing issues before they exist.
Our pooled data from 9 scale-up teams shows:
MetricBefore QA.techAfter 60 DaysImpactDeploy frequency22/week26/week+18%Failed build rate8.5%2.4%-72%Dev team NPS6.17.3+20%Time to detect payment issues5-14 days<1 hour-99%
The last metric is crucial. When a payment issue that you could have caught pre-production goes unnoticed for two weeks, you’ve already lost customers who won’t come back.
Here’s what most companies miss: your competitors probably have the same payment vulnerabilities. But if you fix yours first, you capture their lost revenue.
Consider this competitive matrix:
Companies using autonomous QA see:
The biggest objection we hear: “This sounds too good to be true.”
Fair enough. Here’s exactly how to prove it to yourself in one coffee break:
Add this to your GitHub Actions:
name: QA.tech Revenue Guardian
on:
push:
branches: [ main, staging ]
jobs:
revenue-protection:
runs-on: ubuntu-latest
steps:
- uses: actions/checkout@v4
- name: Run Revenue Flow Tests
uses: qa-tech/agent-action@v1
with:
api_key: ${{ secrets.QATECH_API_KEY }}
environment: staging
focus: checkout,billing,renewals
alert_threshold: revenue_impacting
Within minutes, you’ll see your first results in the QA.tech dashboard:
[VISUAL ASSET 7: QA.tech Dashboard Alert]
Design Brief: Need product screenshot.
The dashboard shows you exactly what failed, when, and why—with full replay capability to see exactly what your AI agent saw.
Checkout friction and failed payments silently drain millions from SaaS companies every year. This calculator estimates how much of your annual recurring revenue (ARR) is at risk due to payment complexity — and shows how fast QA.tech can help you recover it.
Answer a few quick questions to get your risk profile, revenue leakage estimate, and your expected time to ROI.
North AmericaEuropeAsia PacificSouth AmericaOther
StripePayPalBraintreeAdyenSquareHelcimMerchant OneOther
When evaluating QA.tech against alternatives, consider this framework:
Pros:
Cons:
Best for: Companies with dedicated QA teams and static UIs
Pros:
Cons:
Best for: Simple applications with predictable flows
Pros:
Cons:
Best for: Revenue-critical applications with complex, dynamic flows
When Thoughtworks Technology Radar features a tool, it’s not just recognition—it’s a signal to the industry. Their April 2025 report didn’t just mention QA.tech; they positioned us as exemplifying the future of testing:
“One category of tools uses multi-modal LLMs fine-tuned for UI snapshot processing, allowing test scripts written in natural language to navigate an application. Examples in this space include QA.tech…”
This places us at the intersection of three major trends Thoughtworks identified:
Based on patterns from our most successful implementations, here’s your optimal rollout plan:
When you present autonomous QA to leadership, focus on three pillars:
“We’re currently exposed to $X in annual revenue leakage from undetected payment failures. QA.tech reduces this risk by 95% while requiring zero additional headcount.”
“Thoughtworks Technology Radar has validated this approach as the future of testing. Our competitors using traditional methods are 12-18 months behind.”
“Investment of $62K delivers $1.2M in year-one returns through prevented revenue loss and reclaimed developer productivity. Break-even in 6 weeks.”
No. QA.tech complements existing test suites. Start with revenue-critical flows that aren’t well-covered by current tests.
QA.tech operates entirely within your staging environment using credentials you provide. For payment providers, we support test mode APIs from Stripe, PayPal, and others.
All test execution happens in isolated environments. No production data is used. We’re SOC 2 Type II certified and GDPR compliant.
If a human can navigate it, our agent can test it. We’ve successfully tested flows with 15+ steps, multiple payment methods, and complex conditional logic.
The only “catch” is that you need to think differently about testing. Instead of maintaining scripts, you’ll be defining goals. Some teams find this transition challenging for about two weeks.
Every day you delay implementing autonomous QA, you’re accepting three costs:
The question isn’t whether to adopt autonomous testing—it’s whether you’ll lead or follow.
The path forward is clear:
Book a 30-minute session where we’ll:
Schedule Your Risk Assessment →
Start protecting revenue immediately:
For engineering teams wanting details:
In 2025, revenue protection isn’t about better testing—it’s about smarter testing. While your competitors wrestle with Selenium scripts and flaky tests, you could be using AI agents that understand your business goals and protect your revenue streams.
Thoughtworks has validated the approach. Industry leaders like Lavendla and SHOPLAB have proven the results. The only question remaining is: how much revenue are you willing to lose before making the switch?
QA.tech is the autonomous testing platform that protects revenue-critical flows for modern SaaS and e-commerce companies. Recognized by Thoughtworks Technology Radar as exemplifying the future of AI-powered testing, we help teams achieve 95%+ coverage of payment flows with zero maintenance overhead.