AI Fix Blueprints
Not a report. An actual code change.
AuditJet reads your Lighthouse results, CrUX field data, and RUM traces — then writes the exact diff your developer needs to merge. No Googling. No Stack Overflow.
Problem: 780KB JPEG loaded without priority hint. Browser discovers it late in render — LCP: 4.1s.
−1.8s LCP · +2.1% checkout conversion · ~$5,800/week recovered
Generated from your live scan data — not generic templates.
How it works
From audit to merged PR in an afternoon.
AuditJet doesn't just identify the issue — it reads your stack and generates the specific code change. Your developer opens a PR, not a debugging session.
AuditJet scans your site
Lighthouse, CrUX field data, and Real User Monitoring run automatically on your schedule.
AI identifies the root cause
Not just the metric — the specific file, function, and pattern causing the slowdown.
Blueprint generated
Framework-aware code diff. Next.js, Shopify, WordPress, Webflow — the fix matches your stack.
Developer merges it
Hand the blueprint to your dev. Average implementation time: 45 minutes.
Next.js
App Router, Pages Router, next/image, next/script, next/font — blueprints reference your actual component names.
Shopify
Theme architecture, Dawn, Horizon — LCP image preloading, render-blocking liquid sections, CLS from dynamic content.
WordPress
WooCommerce, Elementor, Divi — LCP hero images, render-blocking scripts, plugin conflicts.
Webflow
Webflow-specific embed optimisation, lazy load configuration, and CMS image handling.
Framework-aware
The fix speaks your stack's language.
Generic advice is useless. AuditJet detects your framework and generates fixes that match your actual architecture — not advice that starts with “you should consider optimising your images.”
Frequently asked questions
What is an AI Fix Blueprint in AuditJet?
An AI Fix Blueprint is a code-level remediation produced by AuditJet after it identifies the root cause of a Core Web Vitals failure on your site. Unlike a general recommendation, each blueprint names the specific file responsible, shows a before-and-after diff, and includes an effort estimate so your team knows whether this is a 20-minute fix or a half-day project.
How does AuditJet determine which fix to recommend for a given LCP problem?
AuditJet cross-references Lighthouse audit results, CrUX field data, and Real User Monitoring traces to identify the bottleneck — not just the symptom. If LCP is 4.1 seconds and the trace shows a 780KB hero image loading without a priority hint, the blueprint targets that specific image tag with a fetchpriority and format fix, not a generic “optimize your images” suggestion.
Can AuditJet generate blueprints for INP and CLS issues, not just LCP?
Yes. AuditJet generates blueprints for all three Core Web Vitals: LCP, INP, and CLS. An INP blueprint might wrap a synchronous cart state update in React's startTransition to stop it blocking the main thread, while a CLS blueprint adds explicit width and height attributes to an image that has been causing layout shifts on load.
How are blueprints different from what Lighthouse already shows?
Lighthouse identifies opportunities at the diagnostic level — it will tell you an image is unoptimized but not which component file to open or what exact attribute to add. AuditJet goes further: it detects your framework, maps the issue to a specific file in your codebase, and produces a diff you can hand directly to a developer. Average implementation time across AuditJet users is 45 minutes from blueprint to merged pull request.
Are AI Fix Blueprints generated fresh each scan or cached from previous audits?
AuditJet generates blueprints from the live scan data collected during each audit run — they are not cached or recycled from prior results. If you deployed a fix that partially resolved an LCP issue, the next blueprint reflects the new baseline and targets whatever bottleneck remains, rather than repeating guidance that no longer applies.
Get your first blueprint free.
Run a free audit — no account required. Blueprint included.