Product I built · AI Shopify audit product · 2026
Kevin Builds Brands
Built the audit funnel that turned a store diagnosis into a real $747 paid rebuild: My-Mu shipped live from the KBB pipeline.
$747
first paid rebuild
Live
audit + checkout paths
Verified
findings gate
GA4
funnel tracking
- Problem
- Shopify owners need store diagnostics they can trust, and a solo operator needs the diagnostic work to scale.
- Build
- AI audit engine, verified-findings gate, free-to-paid funnel, checkout, dashboard, GA4 tracking, and Full Store Fix path.
- Result
- The system produced the first paid $747 rebuild, then that rebuild shipped live as My-Mu Bookstore.
Visual proof
Screens that show the work.
Cropped for the case study. Open the live link for the full production surface.
Free audit intake
The public store-URL capture path that starts the diagnosis without asking for a card.
Paid audit offer
The $197 diagnostic offer, positioned as a standalone plan and a credit toward the rebuild.
Audit deliverable preview
Safe sample deliverable: score, revenue-at-risk math, and prioritized fixes without exposing private client data.
Done-for-you rebuild path
The offer that converted into the first paid client rebuild: talk first, scope together, then ship.
Operating proof
The conversion chain, not just the AI
The product is built around a buyer path: diagnose the store, verify what the system can honestly say, convert the right owner, then fulfill the rebuild.
Intake
Owner submits store URL and business context.
Diagnose
The engine scrapes the live store and scores observable issues.
Verify
Findings pass through evidence checks before they become claims.
Offer
The owner sees the audit, paid plan, or Full Store Fix path.
Scope
A real rebuild is quoted after the store is reviewed.
Ship
My-Mu moved from diagnosis to a live Shopify rebuild.
Receipts
- ✓First paid Full Store Fix client: Jack / My-Mu at $747.
- ✓Public proof uses safe funnel/sample screens only; admin, prospect, Stripe, and outreach records stay private.
- ✓My-Mu is separately featured with client consent, before/after screenshots, and live storefront proof.
“You turned what I did into something presentable. I could not be any happier at the outcome.”
Jack Churchward · My-Mu Bookstore, first KBB Full Store Fix client
What it is
My own productized service + SaaS: a live Next.js product that audits Shopify stores, moves owners from a free diagnosis into a paid audit or Full Store Fix, and then turns the diagnosis into real execution. The important proof is not just that the AI runs. It is that the funnel produced a real $747 client rebuild, and that rebuild shipped live as My-Mu Bookstore.
What I built
- 01A public audit intake that captures store URL, business email, and company context, then routes the owner into the right diagnostic path.
- 02An AI audit engine on Next.js/Vercel that scrapes the live store, detects platform signals, scores problems, and generates tiered findings.
- 03A verified-findings gate that blocks the AI from making claims it cannot connect to observable evidence from the store.
- 04A value-ladder funnel: free audit, deeper paid audit, SEO fix, and the $747+ Full Store Fix where I do the rebuild instead of handing over homework.
- 05Checkout, customer dashboard states, GA4 conversion tracking, and a zero-render-cost teardown-video pipeline using headless browser capture, ffmpeg, and AI voiceover.
Business problem
Shopify owners do not just need a list of problems. They need enough confidence in the diagnosis to pay for the fix. A solo operator also cannot manually inspect every store, write every teardown, and keep the funnel moving by hand. KBB had to diagnose stores at low cost, keep the AI honest, and turn the right owner into a scoped rebuild conversation.
My role
Founder and full-stack / AI developer. Product, audit engine, funnel, infra, and go-live — all me.
Proof
- ✓Live product at kevinbuildsbrands.com with free audit, paid audit, and Full Store Fix paths.
- ✓Verified-findings gate exists so AI output cannot freely invent what it did not observe.
- ✓First paying client was a $747 Full Store Fix: My-Mu Bookstore, shipped live and shown separately with consent.
- ✓GA4 tracks the funnel; customer/admin/order surfaces exist but are intentionally not screenshotted here.
Success criteria
The standard I held the product to
The point was not to make AI sound smart. The point was to create a trustworthy path from diagnosis to paid execution.
No private admin screenshots. No prospect data. No fake client outcomes. The only commercial result stated here is the verified first $747 paid rebuild.
Diagnose
Pull enough real store context to identify useful problems, not generic ecommerce advice.
- ✓Store URL intake is explicit and mobile-friendly.
- ✓Platform and page signals are detected before the report is generated.
- ✓The output separates free diagnosis, paid audit, and done-for-you execution.
Verify
Make the AI prove what it says before the claim reaches a buyer.
- ✓Findings are tied to observable store evidence.
- ✓Unsupported claims are blocked or softened instead of published as fact.
- ✓Public samples avoid real prospect, order, Stripe, and admin data.
Convert
Turn diagnosis into a buyer decision without making the audit a dead end.
- ✓$197 audit stands alone and credits toward the rebuild.
- ✓Full Store Fix is scoped after review, from $747, with no surprise number.
- ✓My-Mu proves the funnel can become shipped client work.
What this proves
This proves I can connect AI, funnel design, checkout, tracking, and service fulfillment into one commercial system. The AI is not the headline by itself; the headline is that the system moved from store diagnosis to paid work to a live shipped rebuild.