What your site is actually doing.
One page: what is working, what is costing you visitors, and what to fix next. A phone-and-desktop teardown of the live site: 4 phone loads + 2 desktop loads. No login, no insider access, no Harvv pixel needed. The full evidence is at the bottom.
Working
What's already working. These held up across every load.
Speed is good.
The page is about 0.1 MB across 12 requests. That keeps it quick on mobile data and cheap to load repeatedly.
No JavaScript errors on load.
Lighthouse scores SEO 100/100. The fundamentals Google looks for are present.
Nothing spilled past the edge at either 390px (phone) or 1366px (desktop), so the structure is responsive.
Costing you
Ranked by what hurts conversion most. Full evidence below.
Tiny buttons are hard to tap on mobile
4 form fields have no label
Some text is low-contrast and hard to read
AI search crawlers are blocked
No XML sitemap found
Security headers are missing or weak
One focused change, on the pages people actually land on: Tiny buttons are hard to tap on mobile. We re-measure the same samples after.
01Findings, ranked by what hurts conversion most
| Severity | Finding | How we know |
|---|---|---|
| High | Tiny buttons are hard to tap on mobileMobileAccessibility (WCAG)Conversion 10 of 20 tappable items on this page (50%) measure under 24 pixels on their shorter side, below the 24px floor WCAG 2.5.8 sets for accessibility and well under the platform minimums (Apple recommends 44pt, Android 48dp) for reliable tapping, and the same ones came up small on every test load. When visitors can't hit what they expect to, they get frustrated and many of them leave instead of trying again. The exact elements we found: paste into Claude, Cursor, or ChatGPT | |
| Low | Unused JavaScript is being downloadedBothPerformance Code that never runs on this page still costs download and parse time on every visit. Splitting or removing it speeds up load. Lighthouse measured: Est savings of 50 KiB. paste into Claude, Cursor, or ChatGPT | |
| High | 4 form fields have no labelBothAccessibility (WCAG)ConversionTracking Screen readers can't announce these fields, and a sighted user who clears the placeholder can't recover the prompt. Wrap each input in <label>…</label> or add aria-label. The exact elements we found: paste into Claude, Cursor, or ChatGPT | |
| Medium | AI search crawlers are blockedBothSEO Your robots.txt blocks 8 AI crawlers (gptbot, google-extended, claudebot, ccbot, bytespider, applebot-extended, amazonbot, meta-externalagent). Those engines (ChatGPT, Perplexity, Google AI Overviews, Claude) cannot read your site, so your content cannot be cited or shown in AI search. The exact examples we found:
paste into Claude, Cursor, or ChatGPT | |
| Low | No advertising pixel detectedBothTracking Analytics is present but no Meta/Google/TikTok ad pixel was found. If you run paid ads, conversion tracking should be installed before the next campaign so the platforms can optimize toward buyers, not clicks. paste into Claude, Cursor, or ChatGPT | |
| Medium | No XML sitemap foundBoth No sitemap.xml at the standard location and none listed in robots.txt. A sitemap tells search engines every page you want indexed; without it they rely on link discovery and may miss pages. paste into Claude, Cursor, or ChatGPT | |
| Low | No canonical tag, so duplicate URLs split the page's rankingBoth When the same content is reachable at multiple URLs (think tracking parameters or session IDs), Google can split your ranking signal across them. A single canonical tag tells Google which version counts. paste into Claude, Cursor, or ChatGPT | |
| Low | 3 form fields missing autocomplete hintBothConversionAccessibility (WCAG) Browsers can autofill name, email, phone, address from the user's saved profile only when you tell them which field is which via autocomplete="email", autocomplete="name", etc. Faster checkout, fewer typos. The exact elements we found: paste into Claude, Cursor, or ChatGPT | |
| Low | No llms.txt fileBothSEO No /llms.txt. Optional: some AI tools read this file (Anthropic, Vercel, Stripe, Cloudflare and Hugging Face publish one, and AI coding assistants use it to find docs), so adding one is a cheap courtesy to them. Google has said in writing that Google Search and its AI features do not use llms.txt, so do not expect a search-ranking or AI Overviews benefit from it. paste into Claude, Cursor, or ChatGPT | |
| High | Some text is low-contrast and hard to readBothAccessibility (WCAG) Text that does not stand out enough from its background is hard to read for many visitors, and fails accessibility guidelines Google checks. paste into Claude, Cursor, or ChatGPT | |
| Medium | Security headers are missing or weakBothSecurity The server response is missing browser-hardening headers that protect visitors and are a standard security and agency checklist item. Missing or weak here: Content-Security-Policy (the main defense against injected and cross-site scripts); clickjacking protection (X-Frame-Options or a CSP frame-ancestors rule); a stronger HSTS policy (max-age at least 180 days plus includeSubDomains); X-Content-Type-Options: nosniff (stops MIME-type sniffing attacks). These are set at the server, CDN, or host level (most platforms expose them in settings or a config file) and do not change how the site looks or performs. paste into Claude, Cursor, or ChatGPT | |
| Low | Headings skip levelsBothAccessibility (WCAG) Skipping a heading level breaks the document outline that screen readers and search engines rely on. On this page the outline jumps to an H4 without the level above it, at "SERVICES". Lighthouse flagged 1 heading out of order. paste into Claude, Cursor, or ChatGPT |
Accessibility findings are automated checks against Web Content Accessibility Guidelines (WCAG) 2.1 and 2.2. They flag potential barriers and legal risk, not a certification or a determination of compliance with the ADA, Section 508, or EN 301 549. Automated testing catches only a subset of issues; a full conformance review needs manual and assistive-technology testing by a qualified reviewer.
"How we know": unlabeled = a deterministic fact, identical on every load (e.g. element sizes). Most findings are this kind, so we only mark the exceptions: median across loads = a noisy lab metric, reported as a median. real-user field data = Google CrUX, actual Chrome visitors.
Structural and AI-search checks crawl up to 8 pages from your sitemap (a sample, not your full site). "Broken" means a link returned 404, 410, or 5xx, or did not respond; access-controlled pages (401, 403) are not counted.
02Performance: phone, desktop, and real visitors
| Metric | Mobile | Desktop | Read |
|---|---|---|---|
| TTFB (lab median) | 129 ms | 75 ms | Lab |
| FCP (lab median) | 480 ms | 182 ms | Lab |
| LCP (lab median) | 1.7s | 1.0s | Good |
| Page weight (median) | 0.1 MB | 0.1 MB | OK |
Google Lighthouse (lab): Performance 89 mobile / 93 desktop, SEO 100, Accessibility 93, Best Practices 100.
Lab numbers are from a headless mobile browser on an unthrottled connection: treat them as a floor, not a typical experience. Add a Google API key to light up real-user field data (CrUX) and Lighthouse scores.
03Tiny buttons are hard to tap on mobile
10 of 20 tappable items on this page come in below the platform minimums for reliable tapping on a phone (Apple recommends 44pt, Android 48dp; WCAG 2.5.8 sets 24px as the hard accessibility floor). The same ones came up small on every one of the 4 test loads, so this is the page itself, not a fluke.
The buttons measuring below the minimum on this scan:
- a 258x17 "book an automation audit in "
- a 231x17 "improve your local search ra"
- a 358x20 "max@mwjsautomations.com"
- a 358x20 "0479 149 841"
- a 78x17 "All Services"
- a 199x17 "Custom Website Development"
- a 164x17 "AI Chatbots & Workflows"
- a 155x17 "Local SEO Optimization"
The fix is CSS-only on most sites: add padding around the icon (don't just change the icon size) so the actual tap area is at least 44×44 pixels. No redesign, no new assets.
04Technical SEO & structured data
| Check | Result |
|---|---|
| Title | Top Web Design & AI Automations in Melbourne | MWJS Automations (63 chars) |
| Meta description | 156 chars |
| H1 | 1 on page |
| Canonical | Missing |
| Structured data (JSON-LD) | ProfessionalService |
| Open Graph | Title + image |
05The fix checklist
Everything to fix, priority first, each tagged with the screen it affects and a rough effort. Work top to bottom.
- Tiny buttons are hard to tap on mobileMobileCSS only
- Unused JavaScript is being downloadedBothVaries
- 4 form fields have no labelBothVaries
- AI search crawlers are blockedBothVaries
- No advertising pixel detectedBothDev afternoon
- No XML sitemap foundBothVaries
- No canonical tag, so duplicate URLs split the page's rankingBoth1 line
- 3 form fields missing autocomplete hintBothVaries
- No llms.txt fileBothVaries
- Some text is low-contrast and hard to readBothVaries
- Security headers are missing or weakBothVaries
- Headings skip levelsBothVaries
Effort is a rough read from the outside: "CSS only" means no new assets or backend work, "1 line" means a single tag, "Dev afternoon" means a developer needs to touch tracking or scripts.
06What this report cannot tell you
Everything above is from the outside, looking at the page on a simulated phone and desktop. The questions that actually decide revenue need real visitors. Install the Harvv pixel (one script tag, 16 KB, zero personal data, no engineering project) and within about 72 hours you'd know which buttons real customers tapped and missed, how often Google Analytics is missing visits, and exactly where mobile shoppers stalled and left. This report shows you where to look. The pixel shows you how often it happens, and to whom.
Drop the Harvv pixel on mwjsautomations.com and we turn this one-off scan into ongoing measured behavior: which taps miss, where sessions stall, and the real drop rates. Free to start, no card needed.
Add the pixel free07How we did this, and what it can't prove
- 4 mobile + 2 desktop loads of one URL from headless Chrome (iPhone viewport at 390px, desktop at 1366px), September 2, 2026. Enough loads to separate real defects from random noise, not a full-site crawl.
- Lab numbers, not real-user numbers (no field data was available for this run). Real devices on real networks run slower.
- Friction is inferred, not counted. We can prove a button is small. We can't, from the outside, count how often it causes a missed tap. That requires the pixel on a live page.
About Harvv, the source of this teardown
Harvv is a behavioral UX analytics platform (harvv.com). A lightweight JavaScript pixel captures how real visitors behave on a site (dead clicks, rage clicks, scroll depth, Core Web Vitals, JavaScript errors, and 50+ other signals) and the engine turns them into prioritized, plain-English findings. This teardown is the outside-in version of that: the same detectors run against a public page, with no pixel installed.
How to read it. Every finding here is a reproducible, automated measurement, not an opinion: element sizes, contrast ratios, load metrics, and structured-data checks that anyone can re-run against the same URL. The method is stated in full above. Automated testing catches a subset of issues, so this is a starting point, not a certification.
Full disclosure. Harvv makes the pixel that would measure the friction these findings imply, so we have a commercial interest. That is exactly why the findings are kept to things a reader can verify independently, and why nothing here is inflated: an unreproducible claim would undermine the tool it is meant to demonstrate.
Prepared by Harvv (harvv.com), a behavioral UX analytics platform. Last updated September 2, 2026.