Free SEO Writing AI: A Fact-Check Workflow That Keeps You Safe

A free AI writer produces fluent text fast. It does not know your current prices, your service area, your plan limits or what changed last month. It fills those gaps with plausible guesses.

A fact-check pass sorts every claim in the draft into three buckets:

• Verified: you matched it to a source you trust.
• Wrong: a source contradicts it.
• Unverifiable: you can't find support either way.

Only the first bucket survives. That's the core difference between a draft and a page. If you want the wider picture of what these tools output, see what AI SEO writers produce and miss and the roundup of free AI SEO content generators.

The tool itself isn't the risk. Google's guidance on generative AI content says "Appropriate use of AI or automation is not against our guidelines." The risk is publishing unchecked text, and publishing a lot of it.

Use this for every page. It takes longer than one click, which is the point.

1. Write the brief yourself. Name the reader, the question the page answers, and the facts you'll supply. Paste your real facts (prices, hours, plan limits, locations) into the prompt so the model doesn't invent them.
2. Generate one draft for one page. Not a batch.
3. Pull out every checkable claim. List each number, date, price, product name, competitor comparison and "studies show" line in a separate document.
4. Verify each claim at its primary source. Your own pricing page, the vendor's documentation, the study itself. If a statistic has no source you can open, delete it.
5. Check the draft against your own site. A price on the new page that differs from your pricing page or footer creates a contradiction. Run a free check for contradicting facts on your own site.
6. Add what only you know. Your process, your constraints, the questions customers actually ask you. If you use an example you made up, label it as hypothetical.
7. State key facts in plain sentences. A one-line price or definition in the HTML gives an engine a sentence it can quote. See structured data vs plain-text facts.
8. Edit the title and meta description by hand, then check lengths with the title and meta description checker.
9. Publish, then start the next page. Keep the cadence at the pace you can verify.

Google's spam policies define scaled content abuse as "when many pages are generated for the primary purpose of manipulating search rankings and not helping users." The first example given is "Using generative AI tools or other similar tools to generate many pages without adding value for users."

OtterlyAI tested this in 2026. It launched two new domains, each loaded with roughly 1,000 AI-generated posts in one click. One site showed an author box presenting a fictional wildlife biologist. The other had no author.

• The site with the fictional expert lasted about 14 days in Google's index. The other lasted about 90.
• Otterly reports zero manual actions in Search Console on either domain. The removal was silent.
• Bing kept both sites. Of 3,934 AI citations Otterly tracked for one site, 2,718 came from Microsoft Copilot, which grounds on Bing.
• Google's AI surfaces almost never cited the sites once they were out of Google's index.

The test has limits: one site per arm, no human-written control, a low-stakes topic, and a vendor running it. It still matches Google's written policy. For the side-by-side, read scaled AI content vs people-first content.

The next move: treat Copilot citations as no substitute for losing Google.

Count your buckets after step 4.

• Mostly verified: the draft is usable. Edit for voice and add your own detail.
• Several wrong claims: the model guessed where it lacked data. Rewrite those sections from your own facts rather than patching them line by line.
• Mostly unverifiable: the draft is padding. Start again with a tighter brief.

Then apply one more test. After you cut the bad claims, does the page say anything a competitor's AI draft couldn't? If not, don't publish it. It adds a page without adding value, which is the pattern the policy describes.

Don't rewrite for a special "AI style." Google's AI optimization guide says "You don't need to write in a specific way just for generative AI search," and that you don't have to worry about capturing every long-tail variation. More on this in how to write a blog post for AI search.

Be clear about what sits outside your control.

• It can't promise rankings or citations. Google says a page must be indexed and snippet-eligible to appear in AI Overviews or AI Mode, and that indexing and serving aren't guaranteed.
• It can't change what a model already believes. Answers given without live search come from training data frozen at a cutoff date. A new page doesn't reach them.
• It can't fix access problems. If your content only appears after JavaScript runs, most AI crawlers won't see it. A firewall can also block a crawler your robots.txt allows.
• Markup isn't a shortcut. Google says structured data isn't required for generative AI search, and Google Search ignores llms.txt files. Read this before you make an llms.txt.

What the workflow does control: whether the facts you publish are right, and whether you publish at a scale Google treats as abuse.

If you'd rather not build the review list from scratch, use 99Visibility's free AI Content Checklist as the pre-publish pass for each draft. Run it page by page, alongside the fact extraction in steps 3 to 5.

Once the page is live, the free checker on this site shows whether AI engines name you for up to three buyer questions. It's a snapshot, so rerun it after meaningful changes rather than reading one result as a trend.