Metadata endpoint
Title, description and Open Graph
The meta generator reads current title, description, H1, headings and text before drafting title/description alternatives, keywords, recommendations and copy-paste code.
Live motion layer · Build pattern
AI Search and Schema Sprint
Inputs, AI logic, review boundaries and public-safe outputs
Use case · Real build pattern, generalised
URL-first AI search and schema automation
Paste a page URL, inspect what is actually there, then generate metadata, FAQ blocks, JSON-LD and indexability checks without guessing sensitive facts.
Based on real implementation patterns from the ManchesterAI build archive, generalised for public explanation. This is not a named client claim, not a guaranteed metric and not regulated advice.
3 SEO AI endpoints: meta, FAQ and schema21 snapshot fields before AI writesFallbacks when AI output is missing or malformed
Problem pattern
The work this kind of system removes
Good AI-search visibility work is rarely just a title-tag rewrite. Pages need clean entities, FAQ structure, machine-readable schema, crawlability and a way to avoid making up facts.
A safe generator only uses facts present on the page or provided by the business. It should avoid invented prices, reviews, addresses and product claims.
Implementation proof
Three generators, one rule: inspect the URL first
The source pattern is a Netlify Functions tool-chain where each request starts by fetching a public page snapshot, then returns a reviewable output package rather than directly editing a site.
FAQ endpoint
4–10 FAQ items + FAQPage JSON-LD
The FAQ generator builds page-specific questions and answers from the captured content, then emits a Schema.org FAQPage block for human review.
Schema endpoint
7 structured-data types + Auto
Allowed outputs are WebPage, Article, FAQPage, LocalBusiness, Product, Service and BreadcrumbList, with Auto selecting the safest type from the evidence.
Snapshot layer
21 fields before generation
The crawler captures final URL, status, content type, title, canonical, robots, headings, word count, text sample, OG and Twitter metadata before prompting.
Guardrails
Useful SEO automation is conservative by design
The strong part is not that AI can write tags. It is that the tool refuses weak assumptions and keeps a fallback route when model output is not safe to use.
No invented price/review/NAP fields
Prompt rules explicitly block guessed prices, ratings, phone numbers, addresses and guarantees unless the data is visible on the page or supplied by the business.
Public URL only
The normaliser accepts HTTP/HTTPS URLs and blocks localhost, loopback and private-network addresses so the tool is not used as an internal network fetcher.
Fallback JSON-LD
If AI output is missing or malformed, the schema endpoint returns a basic valid JSON-LD object from the snapshot rather than a broken code block.
Review before publish
Outputs include recommendations and a Rich Results Test reminder; the automation is a drafting/review layer, not blind production publishing.
Public-safe proof: this describes a real URL-first tool-chain pattern and its safety rules. It is not a ranking guarantee, not a claim of Google endorsement and not a replacement for editorial review.
Fetch the live page
Normalise the URL, follow redirects and capture visible headings, metadata, robots, canonical and text.
Draft from evidence
Generate tags, FAQ or JSON-LD from the snapshot while blocking invented prices, reviews and contact fields.
Return reviewable code
Show the output, source/fallback state and recommendations so a person can approve changes before deploy.
Validate after publish
Check rendered schema, sitemap/indexability, mobile layout and live URL status instead of trusting the draft.
Next step
Want to scope this as a controlled build?
Send one real workflow and the data it uses. We will map the safest first version and the review point before writing code.