AI SEO Tracker

AI SEO Tracker gives technical SEO teams, agencies, and product marketers one structured site-audit record with crawlability, indexation, metadata, canonical, sitemap, structured-data, and AI-search-readiness fields. It returns a source-linked dataset item that turns a public website URL into prioritized, actionable audit evidence.

Best fit and workflow routing

Choose AI SEO Tracker for a public website baseline before a release, content migration, or recurring technical review. It works well for teams that need a compact audit record and evidence URLs they can validate with the site owner.

Start with this Actor to establish page-level technical evidence, then use AI Search Visibility Tracker when the next decision is measuring whether a domain appears as a source in AI answers. Together, the two records support a practical workflow from site readiness to observed AI-search visibility.

Quick start and input

{
  "siteUrl": "https://example.com",
  "maxPages": 5,
  "checkAiReadiness": true,
  "previousSnapshot": {
    "overallScore": 72,
    "criticalIssueCount": 1,
    "warningIssueCount": 4
  }
}

siteUrl is the public homepage or page that starts the audit. maxPages bounds the same-origin HTML sample from one to 25 pages. checkAiReadiness adds named AI-crawler directives, llms.txt, and structured-data signals. previousSnapshot supplies a prior summary so the next audit can calculate an overall-score change.

Workflow story: release baseline to fix list

A technical marketer starts with the production homepage and a five-page sample. The Actor collects robots, sitemap, page metadata, canonical, heading, and structured-data evidence, then returns one report. Next, the marketer assigns critical issues from the report, validates each evidence URL with the site owner, and schedules the same bounded input after the release. The new report compares its score with the prior snapshot and becomes a source-linked change record for the team.

Output data

Each dataset item represents one completed public-site audit.

Field	Meaning
overallScore	Bounded 0–100 readiness score for the sampled site evidence
technicalScore	Crawlability, metadata, canonical, and indexation signal score
aiReadinessScore	AI-search readiness signal score when enabled
issues	Prioritized issue objects with severity, code, evidence URL, and recommendation
robotsStatus, sitemapStatus, llmsStatus	Presence status for the public discovery resources
auditedAt	Collection timestamp for comparing repeat audits

{
  "siteUrl": "https://example.com",
  "overallScore": 78,
  "technicalScore": 82,
  "aiReadinessScore": 70,
  "pagesAudited": 5,
  "criticalIssueCount": 0,
  "warningIssueCount": 2,
  "robotsStatus": "present",
  "sitemapStatus": "present",
  "llmsStatus": "missing",
  "issues": [
    {
      "severity": "warning",
      "code": "canonical-missing",
      "message": "Page needs an absolute canonical URL.",
      "evidenceUrl": "https://example.com/pricing",
      "recommendation": "Add one absolute canonical URL for the preferred indexable version."
    }
  ],
  "auditedAt": "ISO-8601 collection timestamp"
}

OUTPUT and RUN_SUMMARY are written to the default key-value store. The dataset is the durable audit record for export, API readback, or a scheduled comparison.

Pricing

AI SEO Tracker uses Pay per event pricing. One persisted site-audit-completed report is the billable event, and Apify platform usage is charged separately. A five-page audit produces one report event; maxPages keeps the public-page sample specific and bounded. Use the live Pricing tab when planning a scheduled audit's current event price and platform-usage details.

API example

curl "https://api.apify.com/v2/acts/khadinakbar~ai-seo-tracker/runs?token=$APIFY_TOKEN" \
  -H 'content-type: application/json' \
  -d '{"siteUrl":"https://example.com","maxPages":5,"checkAiReadiness":true}'

AI agent prompt

Audit https://example.com across five pages. Return the overall score, critical issues, and the three highest-priority warnings with source URLs. Read the dataset item after completion, preserve the collection timestamp, and state the Pay per event scope before scheduling a comparison.

The Actor reads public web resources and returns one bounded dataset item. An agent can retrieve that item from the default dataset and pass its summary into the next scheduled audit as previousSnapshot.

Best results and practical guidance

Provide a canonical public URL that represents the release or section you want to assess. Start with five pages for a focused baseline, validate issue evidence URLs with the site owner, and schedule the same input after a meaningful site change. Use a consistent sample size so score comparisons remain useful. Pair the audit with the visibility workflow above when source-citation measurement is the next business question.

Builder's note

I designed the Actor around one complete, validated audit record per site. That shape keeps evidence URLs, charge accounting, exports, and repeat comparisons aligned for both technical teams and AI-agent readback.

Responsible use

Use public websites you are authorized to assess and follow applicable laws, site terms, intellectual-property requirements, privacy obligations, and the site's published access guidance.