How to Audit Your Entire Website for AI Content Using the TextSight API

Run a content site of any real size and you have a blind spot. You don't actually know how much of what you've published was AI-generated, lightly edited, or never fact-checked at all. Freelancers touched it. Agencies touched it. So did two or three teams before you got here. Trying to audit your website for AI content one page at a time falls apart the moment you're staring at thousands of URLs. So don't. This guide walks through a programmatic, site-wide audit using the TextSight API: crawl the sitemap, scan every page, and turn the output into a ranked fix list instead of a nagging worry.

The point isn't to scrub every trace of AI from your domain. It's trust at scale. You want to know which pages are unverified, which carry fabricated claims, and which need a human read before they quietly cost you rankings or credibility.

Why a Site-Wide AI Content Audit Matters

Search engines now treat content quality and trustworthiness as explicit ranking factors. A site that slowly piled up mass-produced, unverified pages is carrying risk, caught or not. And the failure modes are sneaky. AI-assisted content breaks in ways nobody notices until someone actually checks:

• Fabricated facts and statistics. A model can invent a study, a date, or a percentage, and that number can sit in your evergreen content for months without anyone questioning it.
• Thin, templated patterns. Pages spun from the same prompt scaffold read alike. That sameness chips away at your topical authority.
• Citations that don't hold up. AI happily produces sources that don't exist, or that say nothing like what the page claims they say.

Manual review can't keep up with a library that grows every week. An API-driven one can. Instead of "is this article AI?" you get to ask something far more useful: across the whole domain, which pages are unverified, and which carry the most risk? That shift, from one-off spot checks to portfolio-level content trust, is the whole reason to bother.

Be honest with your team about what detection actually buys you, though. It's probabilistic guidance, not proof. A site-wide scan gives you a triage signal that says where to look first. It doesn't convict a page on its own.

Step 1: Build Your URL Inventory

You can't audit what you haven't listed. So start with a complete inventory of the URLs you want to scan. Pull from these, roughly in order of how much you can trust them:

1. Your XML sitemap(s). https://yoursite.com/sitemap.xml is the canonical list of pages you actually want indexed. Grab every <loc> entry, and chase sitemap index files down to their children.
2. A crawl of your own site. A crawler, or a small script that follows internal links, catches orphaned pages and anything the sitemap forgot.
3. Search Console and analytics exports. These show you the pages that genuinely pull traffic. Those are often your highest-priority audit targets.

Now clean it up. Deduplicate, strip tracking parameters, collapse trailing-slash variants, and drop the non-content stuff: tag archives, paginated series, login pages. Leave those out unless you want them in scope. A cleaner inventory means a cheaper, clearer audit.

For a mid-size site, this usually lands as a flat list of a few thousand URLs in a CSV or a database table. That list is the queue your audit script chews through.

Step 2: Extract Each Page's Content

Raw HTML is noise. Navigation, footers, cookie banners, ad markup, all of it dilutes the signal and burns your scan budget. Before you analyze anything, you want the main article text pulled clean from the surrounding chrome.

There are two paths that work well:

• Server-side extraction in your own pipeline. Run each fetched page through a readability or boilerplate-removal library to isolate the article body.
• Let the API extract for you. TextSight's content tools can take a live URL and hand back the meaningful text. The URL Summarizer pairs nicely here. It pulls and condenses page content, which is handy for two things: sanity-checking what your extractor grabbed, and building a human-readable index of everything under review.

Pick one and stay consistent. Normalize the extracted text the same way every single time. Same encoding. Same minimum word-count threshold so you skip near-empty pages. Same handling for multi-section articles. That discipline is what makes results comparable across the whole site, and it's the part most people skip.

Step 3: Scan at Scale with the TextSight API

Here's where the inventory meets the analysis. The pattern is a plain, sturdy loop: for each URL in the queue, send the extracted content to the API, store the response, move on. The API Docs cover authentication, request and response shapes, and the available endpoints in full. What follows is the shape of the workflow you build around them.

A production-grade audit script gets four things right:

• Authentication. Send your API key with every request, and keep it in an environment variable or a secrets manager. Never hard-code it in the script. Never commit it to a repo.
• Concurrency with a ceiling. Run requests in parallel so thousands of URLs finish in a reasonable window. But cap that concurrency and respect rate limits, or you'll hammer the API and your own origin.
• Retries and backoff. Network calls fail. Wrap each request in a retry with exponential backoff, and log the permanent failures so they get re-queued instead of vanishing.
• Idempotent storage. Key each result by URL. A re-run then updates the record instead of duplicating it, which lets you resume a half-finished audit without starting from scratch.

Here's the core loop in pseudocode:

for url in inventory:
    text = extract_main_content(url)
    if word_count(text) < MIN_WORDS:
        record(url, status="skipped")
        continue
    result = textsight_api.scan(text)          # detection
    facts  = textsight_api.check_claims(text)   # optional: hallucination/claims
    record(url, score=result.ai_score, flags=facts.unsupported_claims)

Run it against a small sample first. Fifty URLs is plenty to confirm your extraction quality and response handling before you point it at the full inventory. A broken extractor will quietly poison thousands of results, so catch it early.

Step 4: Turn Raw Scores into a Prioritized Action List

A spreadsheet of AI-likelihood scores is data, not a plan. The value of a site-wide audit lives in how you rank the results. Pair the detection signal with business context, and your team spends its limited review hours where they count.

Sort and segment by:

• Risk against reach. High AI-likelihood plus high organic traffic is your number one. Same score, low traffic? That can wait.
• Unsupported claims. Any page where the claim check flagged fabricated facts, fake statistics, or sources that don't exist needs a human pass regardless of its authorship score. A made-up stat is a credibility problem even in copy a person wrote.
• Topical clusters. When a whole category scores alike, you've probably found one freelancer or one templated batch. Fix the pattern, not the pages.
• Money pages. Anything tied to revenue, to YMYL topics like health, finance, and legal, or to your brand's authority gets extra scrutiny.

Out of that ranking, build something simple. High-risk pages drop into a review queue. An editor verifies, then rewrites or re-sources. The page gets re-scanned to confirm the fix. Track the audit as a living metric, like "percentage of indexed pages verified in the last 12 months," rather than a project you close and forget.

One guardrail to keep repeating to stakeholders: a high score is a reason to investigate, not a confession. You're auditing to improve content, not to brand it. Plenty of flagged pages just need verification, a real citation, and a human edit to turn into trustworthy assets.

Step 5: Make the Audit Continuous

A one-time audit goes stale the second your next batch publishes. Teams that get lasting value wire detection straight into the pipeline so trust holds on its own:

• Pre-publish checks. Scan new and updated articles before they go live. Catch the problem at the source, not in a cleanup six months later.
• Scheduled re-crawls. Re-run the full audit monthly or quarterly to catch drift and pick up freshly published or edited pages.
• Alerting. When a new page crosses a risk threshold, flag it to an editor automatically. The queue fills itself.

Because the whole flow runs through the API, it drops into the CI/CD or CMS setup you already use. The audit stops being an event and becomes a standing quality gate. That's the difference between cleaning up a mess once and never letting it pile up again.

Frequently Asked Questions

How do I audit my entire website for AI content without checking pages manually?

Build a URL inventory from your sitemap and a crawl, extract the main content from each page, then feed that text to the TextSight API in an automated loop. The API returns a likelihood signal for every URL, plus optional claim-level verification, so you cover the whole site without opening a single page by hand. The API Docs lay out the endpoints and request format.

How many URLs can I scan at once?

There's no hard page-count ceiling on the approach. You scan as many URLs as your inventory holds by iterating through them with controlled concurrency. In practice, throughput comes down to your plan's rate limits and your own crawl politeness. Run requests in parallel with a sensible cap, add retries with backoff, and a multi-thousand-URL audit finishes in one batch run.

Does a high AI score mean a page will be penalized by Google?

No. AI detection is probabilistic guidance, not a ranking verdict and not proof of how a page was written. A high score just tells you the page is worth a human review for originality, accuracy, and genuine usefulness. Search engines reward helpful, trustworthy content. The audit helps you find the pages that may not clear that bar yet so you can fix them.

What's the difference between detecting AI text and checking for hallucinations?

Detection estimates how likely it is that text was AI-generated. Hallucination and claim-checking ask something else entirely: are the facts in this text actually supported? A page can read as fully human-written and still contain fabricated statistics or fake citations. A thorough audit uses both signals, authorship likelihood for triage and claim verification for accuracy.

Auditing a large site for content trust only hurts when you do it page by page. Wire your sitemap to the TextSight API and it becomes a single, repeatable run you can schedule and forget. Learn how to scan thousands of URLs automatically. Start with the API Docs, validate on a sample, then audit your whole domain with confidence.