ExampleIllustrative worked example.We work with some of the largest companies in the world — quietly, by their preference. We don't name clients or share their work, so this example is built around a real scan of a public website. Starbucks is not a client.
Worked Example
Starbucks: a 30-day sprint, start to plan.
We ran a real automated quality scan of the Starbucks homepage, then mapped what the rest of a 30-Day AI QA Rescue Sprint would look like using those findings as the starting point. Here is the deliverable a team would receive at each of the six phases.
Why Starbucks — and why it's only an example.
• We work with some of the largest companies in the world. Most of them prefer to work privately, so we don't name our clients and we don't share their work. That's part of why companies that need quiet, serious QA reach out to us in the first place. To show what a Sprint actually looks like, we built this walkthrough around a public website nobody owes us NDA cover on.
• Starbucks is not a client. The brand is used only as a public, recognizable example. IcebergQA is not affiliated with or endorsed by Starbucks.
• The Day-8 Quality Baseline & Bug Map below is anchored to a real automated scan of the public starbucks.com homepage by the testers.ai platform — so the ten findings reflect what an honest baseline scan surfaces. Scoring and the phase deliverables shown are illustrative of how a sprint typically unfolds from a baseline like this one.
AI-first at every step
Discoveryreads every existing case · maps intent · scores AI-readiness & ROI→Baselinescans the live site · scores · surfaces real findings→Transformconverts old tests · generates new coverage→Executeruns full suite nightly, autonomously→Validateflags signals · XBOSOFT humans confirm each one
Every phase is AI-driven and AI-executed. Humans advise, validate, and stay in the loop — but they don't hand-write the tests or hand-execute the runs. The client is in control of every decision; we do all the work.
38/100
Quality score
F
Grade vs category
10
Issues (7 high)
29
Console errors
A high-quality visual brand experience — currently suffering significant technical degradation. The privacy-consent, identity and analytics layers are silently broken by CORS and header misconfiguration. The storefront looks fine; the trust signals underneath do not.
We get inside the real product: environments, the flows that matter, and an honest inventory of the test assets that already exist.
Deliverable · Access & Scope Confirmation
What we connected to, and what we'll watch.
Site map · surface area scoped
Crawled, deduped, and graphed: Home at the hub, the eight critical flows on the first ring (gold), supporting sub-pages branching outward. Every node becomes a target for AI personas, accessibility checks, and the integrity probes.
Existing test assets inventoried
420 Selenium / Cypress E2E tests across web + mobile repos — ~60% flagged flaky in the last 30 days
~1,200 manual cases in the team's test management tool, mostly happy-path
No automated checks today for consent, identity, or analytics integrity
Staging access & data-state accounts
Staging environment reached via an X-Stage-Token header — lets the AI break things safely without touching production
4 test accounts with distinct data states (empty cart, mid-order, new Rewards Green, long-time Gold) so personas hit realistic conditions, not synthetic ones
AI-readiness & ROI · 4 paths for the existing suite
Once the AI has read everything, every cluster of tests gets an AI-readiness + ROI score. You then pick one of four paths per cluster. You choose. We do the work.
1
Convert to AI-first execution
Our tools convert most manual and automated cases into AI-first versions where the AI executes and validates. Same scenarios you cover today — no brittle selectors, no flake.
Best for: stable scenarios with clear intent · ROI: highest
2
Improve via our SDK
Drop our SDK into your existing automated test runs to layer AI assertions, smart waits, and self-healing on top of what you already wrote. Keep the investment, get the AI uplift.
Best for: large suites worth preserving · ROI: high
3
Augment via browser + mobile extensions
Our browser and mobile extensions piggy-back on existing test runs to capture massive extra coverage — accessibility, visual diffs, console errors, network signals — without writing one new test.
Best for: free extra coverage on existing flows · ROI: very high
4
Auto-generate net-new coverage
AI analyzes the gaps and auto-generates new coverage in whichever format you want: more manual cases, more automated scripts, AI-first journeys, or extra permutations of what's already there.
Best for: blind-spot coverage · ROI: depends on gap depth
By end of Day 3 the AI has read every existing case, mapped intent, scored AI-readiness, recommended a path per cluster, and the baseline scan is already running against staging — its output becomes the Day-8 Quality Baseline & Bug Map below.
Phase 2Days 4–8Actual scan data
Quality Baseline & Bug Map
The Day-8 milestone. A scored, prioritized map of what's actually wrong — this section is real data from a testers.ai scan of starbucks.com.
Day-8 Deliverable · Quality Baseline & Bug Map
Starbucks homepage — 38/100, Grade F.
38/100
Quality
F
Grade
62
Jank score
10
Issues
29
Console errors
4.1
Persona /5
What's affecting the grade
Reliability
2
Accessibility
2
Visual
2
Content
2
Privacy
1
Performance
1
Prioritized bug map — 10 findings
P
Finding
Category
Sev
P10
CORS policy blocking Privacy Consent Manager (TrustArc)
JavaScript exception in keyboard accessibility audit
Accessibility
High
P7
Optimizely event logging blocked by CORS
Reliability
High
P6
Low contrast on outline buttons
Accessibility
High
P5
Excessive network requests on initial load
Performance
High
P4
Inconsistent button styling in hero sections
Visual
High
P3
Non-standard meta tags for social sharing
Content
Med
P3
Generic Open Graph image
Content
Med
P2
Vertical alignment of 'Find a store' icon
Visual
Med
The headline: three of the top four findings are third-party integrations — consent (TrustArc), identity (mParticle) and analytics (Optimizely) — all silently blocked by CORS and header misconfiguration. Pass/fail page tests stayed green. The consent and measurement layers did not.
Phase 3Days 9–16Illustrative
AI Test Transformation
We don't throw the existing suite away. We upgrade it into self-healing AI checks, and add coverage where the baseline showed there was none.
Deliverable · Coverage Transformation Summary
From 420 brittle tests to 1,200 durable AI journeys.
420
Upgraded
1,200
AI journeys
820
AI-generated new
<2%
Flake (was ~60%)
New coverage added where the baseline was blind
Consent integrity — banner renders, accepts/declines, and TrustArc actually loads
Identity & analytics — mParticle and Optimizely requests succeed, not 0-byte CORS failures
Keyboard accessibility — the audit runs without the JS exception; tab order verified
Contrast — outline buttons checked against WCAG AA automatically
Initial-load budget — request count and weight gated per release
Social/share metadata — OG tags and image validated
Example: a brittle locator becomes a self-healing AI check
- cy.get('#hero > div.cta-wrap > button.btn--primary.css-1x7a2k').click()+ aiCheck("Tap the primary 'Order now' button in the hero",+ { intent: "start an order", expect: "menu or order page loads" })
The AI check survives the next CSS-class change — exactly the kind of churn that made the original suite 60% flaky.
Phase 4Days 17–22Illustrative
AI QA Runs & Human Validation
The AI runs the journeys against the real product, nightly. Then senior XBOSOFT QA validates every finding so the team only ever sees real, prioritized bugs.
Deliverable · AI-discovered, Human-validated
412 raw signals → 187 confirmed bugs.
412
Raw AI findings
187
Confirmed real
225
Noise / dupes filtered
177
New, beyond baseline
The 10 baseline findings were reproduced and confirmed. After Samantha had the AI generate 820 new journeys and execute all 1,200 nightly, hundreds of signals surfaced — 177 of them validated as net-new real bugs that the original suite would never have caught.
Sample of newly-surfaced, human-validated issues
Finding
Flow
Sev
Consent choice not applied before analytics fires (race condition)
Store locator returns 0 results when geolocation is denied (no fallback)
Find a store
High
Customization options lost when switching size mid-order
Order ahead
Med
Gift-card balance not announced to screen readers
Gift cards
Med
Every confirmed bug ships with repro evidence, severity, and a fix prompt, filed straight into the team's tracker. The 225 noise items never reach an engineer's inbox — that filtering is XBOSOFT's senior validators, with AI assistance.
Phase 5Days 23–25Illustrative
Benchmark & Operating Model
Where Starbucks sits against its category, and the cadence that keeps quality from sliding back after the sprint ends.
Deliverable · Competitive Benchmark & Operating Model
Quality vs the QSR category.
Overall quality score — illustrative peer set
Peer A
86
Peer B
81
Peer C
74
Starbucks (today)
72
Category avg
70
Right at category average — the visual experience is best-in-class, but the broken trust layer and 177 flow bugs are what's keeping Starbucks out of the leader band. All addressable.
Recommended operating model
Hybrid. IcebergQA runs the 1,200 AI journeys nightly and a senior-validated pass monthly; the Starbucks team owns triage and fixes.
Release gate: consent, identity, analytics and the top accessibility checks must pass before ship.
The Day-30 executive readout: what changed, what it's worth, and exactly what to do over the next quarter.
Day-30 Deliverable · Executive Readout
72 → a projected 88, with the trust layer restored.
72/100
Quality, Day 8
88/100
Projected, fixes shipped
3
Trust layers restored
1,200
AI journeys gating
Closing the three Highs (TrustArc, mParticle, Optimizely) plus the top flow bugs Samantha's nightly runs surfaced lifts the homepage from C+ to A−, and re-enables consent, identity and analytics that were silently dropping in production.
The 30 / 60 / 90-day plan
First 30 days
Ship the 3 priority Highs
Turn on the nightly AI gate
Restore consent + analytics signal
By 60 days
Work down 177 validated flow bugs by ~half
Extend journeys to native apps
WCAG AA across primary flows
By 90 days
Re-benchmark vs category
Hybrid model fully handed off
Quality trend owned in-house
The ask: the homepage was the cheapest place to look. The same engine, pointed at order-ahead, rewards and checkout, is where the revenue-and-trust risk actually lives — that's the 90-day extension.
Want this run on your product?
Starbucks was a public example. The same 30-day sprint — real Day-8 Quality Baseline & Bug Map, then transformation, validation and a plan — runs on your app.