Every week there's a new "AI-powered website audit" tool. Most of them are Lighthouse wrappers that return the same generic advice: "Improve your page speed" and "Fix your meta tags."
I wanted something different. Not advice. Code.
So I built a 6-dimension audit engine that ends every finding with a copy-paste prompt for Claude, Cursor, or GPT. Run a URL through it, and 90 seconds later you get:
- A scored breakdown across 6 dimensions (Visual & UX, Conversion & Flow, Technical SEO, Content Quality, Competitor Intelligence, Domain & SEO Value)
- 8+ specific issues, each ranked by estimated monthly revenue loss
- For every issue: the exact fix prompt to paste into your AI coding agent
- Competitor comparison against named local competitors
- Full-page annotated screenshots
The engine is live right now. Let me walk through the tech stack.
1. Next.js App Router + Edge-ready API Architecture
The audit engine runs inside a Next.js App Router project deployed on Vercel. The core analysis pipeline is ~2,000 lines across 6 category analyzers:
app/api/audit/route.ts → Entry point for scans
lib/deep-audit-orchestrator.ts → Orchestrates 6 analyzers
lib/audit-engine.ts → Main scoring engine (Lighthouse, signals, crawl)
lib/keyword-gap-analyzer.ts → Competitor keyword comparison
The engine processes everything server-side and returns structured JSON. The frontend just renders what the API gives it. The free scan returns a quick 3-dimension preview, while the deep audit runs as a POST to /api/audit/[id]/deep when a user starts the trial.
2. Screenshot Microservice on Railway (Headless Chromium)
Screenshots were the hardest piece. Next.js serverless functions have a 10-second timeout — not enough for headless Chromium. Solution: a standalone Node/Express service deployed on Railway with Docker that auto-discovers 5 pages and captures viewport + full-page WebP screenshots.
Key microservice features:
- Auto-discovers pages (home, about, services, contact, pricing) via link scanning
- Runs 10 structural HTML checks (meta, headings, schema, analytics, favicon, canonicals)
- Returns everything in under 90 seconds
3. The Click-to-Code Feedback Loop
The most important design decision was how to present fix prompts. Most audit tools give you a paragraph of advice. Our prompts look like this:
"Fix meta descriptions and title tags on my site. URL: {YOUR_SITE_URL}. Generate unique 155-character meta descriptions for every page. Keep title tags under 60 characters. Output a table: URL | Current Title | New Title | Current Meta | New Meta"
Copy. Paste into Cursor/Claude/GPT. Hit enter. Spend 15 minutes reviewing instead of 2 hours figuring out what to fix.
The barrier isn't "knowing your site has issues." It's knowing what to type into your AI agent. Every audit tool should end with something you can paste.
Live right now
✅ 6-dimension scoring (composite 36/F on our test run)
✅ 8+ issues with revenue impact (estimated $6,500/mo loss)
✅ 8 AI-ready fix prompts per audit
✅ Competitor comparison tables
✅ Card-required 3-day free trial (checkout works — no caveats)
✅ Free scan: no email, no account, 90 seconds
Try it
Run any URL: outboundautonomy.com/#audit
3-day trial with full deep audit: outboundautonomy.com/trial
Built with Next.js, Vercel, Railway, Stripe, and a distributed agent team. Questions? Drop them below.
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