Public scanner concept · AI-era SEO · agent readiness

AI search and agent readiness audit.

A public website scanner concept for measuring how ready a site is for search engines, AI answers, and agent workflows using real crawl evidence, performance APIs, structured-data checks, and useful recommendations.

The right version is not a fake “AI SEO score” widget. It is a real public scanner: crawl the site, verify the technical layer, enrich where useful with official APIs, and output recommendations that a founder or operator can actually act on.

Public scan

Run a real readiness scan.

Checking whether the site is machine-readable or just visually polished.

Mapping trust gaps before they spread across more pages and shares.

Turning vague AI-search claims into URL-level implementation actions.

Score model

A score is only useful if the weighting model is visible.

20%

Crawlability and indexability

Status codes, canonicals, robots, sitemap coverage, redirect hygiene, crawl depth, and whether important pages are discoverable without guesswork.

20%

Structured data and entity clarity

JSON-LD quality, schema coverage by page type, entity consistency, breadcrumb structure, and whether the site is explicit about what the business is and what each page owns.

15%

Internal linking and page-role separation

Money pages connected to proof pages, support pages, and topic pages cleanly enough that both humans and machines can follow intent paths.

15%

Answerability and support surfaces

FAQ, comparison, cost, proof, definition, and support content that makes the site easier to quote, summarize, and retrieve from AI-style search flows.

10%

Core Web Vitals and render health

PSI and CrUX data where available, plus render checks that catch pages that technically exist but perform badly or expose weak UX to crawlers.

10%

Media, social, and reputation signals

Image alts, image sitemap hygiene, Open Graph and card metadata, proof distribution, and whether review or testimonial surfaces reinforce trust instead of fragmenting it.

10%

AI-agent readiness

Machine-readable assets like llms.txt, stable metadata, predictable page patterns, clean support surfaces, and low-contradiction content structure.

Evidence model

The score cannot be the product. The evidence is the product.

Deterministic crawl checks first. No LLM hallucination required to tell you a canonical is wrong, a page is orphaned, or schema is missing.
Recommendations generated second. The model translates findings into implementation advice, but the score is grounded in evidence rather than vibes.
Every issue tied to exact URLs, evidence, and confidence labels: direct, API-verified, or inferred.
Impact x effort ranking so the report says what to fix now, not just what is technically imperfect.

Report output

Overall readiness score with the weighting model exposed, not hidden behind a black box.
Per-page findings with crawl evidence, API evidence, and the exact HTML or metadata issue where relevant.
Top 10 fastest wins so the report is useful to a founder, not just an SEO specialist.
Template-level clustering so one issue on a repeated page type does not get reported as 80 separate mysteries.
A clear split between what fits inside Live in a Day and what belongs in a deeper SEO or automation engagement.
JSON export for internal use and a human-readable report for the site visitor.

Features worth adding

Template clustering by page pattern so recommendations scale across the site instead of repeating one issue page by page.
Competitor gap mode later, but only after the core site audit is good enough to trust.
Reputation layer: testimonial placement, review surface checks, proof-page coverage, and whether social shares reinforce the same service story.
AI-answerability layer: detect missing FAQ, comparison, cost, proof, and definition pages around important service intents.
Workflow follow-on hints: where review capture, feedback triage, internal-link suggestions, or schema generation could become partially automated.

Supporting proof

The scanner should lead somewhere stronger than a score: strategy in the article, implementation in the case study.

Article

AI Agents and the Marketing Opportunity of the Decade

The strategic angle: why this window exists now, why teams need to act before the advantage normalizes, and how the technical layer, social signals, reputation, and agent workflows turn into actual marketing leverage.

Frames the urgency: early operational adoption beats waiting for the market to catch up.
Connects technical SEO, AI retrieval, reputation, and workflow automation into one commercial story.
References the Neil Patel video and translates it into practical implementation logic.

Read the article

Case study

AI Search Readiness

The implementation angle: what gets shipped, how long it takes, what technical layers matter, and how Live in a Day fits when the work is bounded enough for a fast pass.

Covers JSON-LD, schema structure, image tags, llms.txt, location.kml, sitemap resources, and internal-link architecture.
Explains the timeline from audit through technical fixes, support-page buildout, and compounding publishing workflows.
Shows the trust layer too: social metadata, review signals, testimonials, and reputation-management follow-ons.

Read the case study

Live in a Day

Find the issue. Fix it fast.

This scanner is here to show what is weak on the site, then point straight at the one-day pass that fixes the important parts first.

01
This is not a vague audit-and-vanish offer. The scanner is meant to lead directly into a one-day implementation pass when the fix list is finite enough.
02
That one-day pass is Live in a Day: schema, metadata, internal links, answer surfaces, crawl resources, image/social cleanup, and machine-readable context shipped on the existing site.
03
The point is speed. If the report says the frame is weak, the next move is to tighten the frame now, before more pages, links, and contradictions stack on top of it.

See Live in a Day

One-day AI search readiness implementation.

What fits inside Live in a Day

The audit becomes more valuable when it points clearly at what can be shipped fast.

The public scanner should not just say what is wrong. It should also say which fixes are bounded enough to fit inside Live in a Day.

Schema and JSON-LD fixes
Metadata cleanup
Internal-link and page-role corrections
FAQ, support-surface, or proof-block additions
Image, social-card, and crawl-resource cleanup

That is the commercial logic: public scan first, visible evidence second, then a clean handoff into a one-day implementation pass when the fixes are clear and finite enough to ship quickly.

FAQ

Questions the page should answer before the tool exists.

Would this be a real tool or just a landing page with an arbitrary score?

It should be a real tool. The score only has value if it is backed by an actual crawl, API evidence, structured-data extraction, and recommendations tied to exact URLs.

Why avoid a “connected audit” in version one?

Because the public scanner is the useful entry point. It can scan any public site without OAuth, private access, or setup friction. That makes it shareable, productizable, and easier to trust as a first pass.

Can it be accurate without expensive data vendors?

Yes, for a strong v1. A crawler plus PageSpeed Insights, CrUX, local schema extraction, and optional Bing or GBP enrichments already gives enough signal to produce useful reports. Paid APIs improve breadth, not the core truth layer.

Where do automated workflows fit in?

After the audit identifies repeated patterns. If the same issues keep appearing in reviews, support tickets, page templates, or internal-link gaps, those are strong candidates for semi-automated workflows rather than one-off manual fixes.

Next step

Replies in ~24h

Need the report to point at fixes, not just problems?

That is the standard. The scanner should identify what is wrong, what matters most, and what can be shipped fast in one bounded implementation pass.

See Live in a Day Book a SEO session ->