AIContentGapAudit
Diagnose what your page is missing for AI search, then generate publish-ready content to fill the gaps.
FAQ
FAQ&Methodology
The Shift
SEO is being re-indexed by AI assistants
58% of Google searches now end without a click. When your customer asks ChatGPT a question, your content either gets cited or it doesn't. The rules are different from classical SEO, and most content tools haven't caught up.
Reads well, doesn't get cited
AI assistants pull from passages with atomic claims, specific examples, and clean schema. Long generic paragraphs lose every time.
Generic AI output gives you away
"Leverage", "ever-evolving", "crucial", em dashes. These tells mark text as AI-generated and lower its citation rate.
Auditing is decoupled from fixing
Most tools tell you what's broken. They don't write the fix in the same workflow. You audit, then go open another tool.
Schema is an afterthought
FAQPage, BlogPosting, and ItemList schema decide whether AI can parse your content. Few writers add them automatically.
Five modes, one tool
Every step of the audit-to-publish loop
Diagnose finds the gaps. Four generation modes fix them. Each runs the same 5-pass pipeline that strips AI fingerprints and bakes in schema.
Methodology
The four properties that decide whether AI cites you
GIST is the scoring framework behind every output. Each generated passage is tested against these four properties before it ships.
Granularity
Each claim is atomic and self-contained. AI assistants pull single sentences or short passages, not paragraphs.
Inferability
Surrounding context is enough to verify the claim. The passage stands alone when extracted.
Specificity
Concrete tool names, numbers, file formats, version numbers. No generic abstractions.
Topicality
The section answers the query it sits under. No padding, no off-topic context-setting paragraphs.
Pipeline
Five passes per article, different models for different jobs
A single LLM call cannot produce publish-ready content. The pipeline routes each job to the right model: fast for analysis, capable for writing, deterministic for cleanup.
Identifies angle, voice, and which concrete examples to weave in.
Writes the article. The most capable model handles the hardest job.
Replaces abstractions with concrete tool names, numbers, examples.
LLM removes tics. Deterministic regex sweep removes the rest.
Smooths transitions. Generates meta tags, slug, schema.
FAQ
Questions worth answering
What does GIST stand for and what does it measure?+
GIST stands for Granularity, Inferability, Specificity, and Topicality. These four properties determine whether a passage gets cited by AI assistants like ChatGPT, Perplexity, and Google's AI Overviews. Pages that score high on all four get cited; pages that don't, do not.
How is this different from generic AI writing tools?+
Generic AI writers produce text. This tool produces text engineered to be cited by AI assistants. The pipeline runs five passes per article: a strategy pass to choose the angle, a draft pass on the most capable model to write it, a specificity pass to add concrete tool names and numbers, a fingerprint strip to remove AI tics, and a final coherence pass with schema and meta. Every output passes a deterministic banned-word sweep to remove phrases like "crucial", "leverage", "ever-evolving", and em dashes that mark text as AI-generated.
What modes does the tool support?+
Five modes. Diagnose audits any page against a target phrase and returns specific gaps. New Blog Article generates a full article from a diagnosed gap. Direct Generation writes an article on any topic without a URL or diagnosis. Article Rewriter rewrites an existing article for AI readability while preserving facts. On-Page Content generates landing or product page sections, optionally formatted for WordPress, Shopify, WooCommerce, Wix, or Webflow.
How does pricing work?+
The Content Gap Audit is included in the Website AI Score subscription at $49 per month for 100 credits. A diagnosis costs 1 credit. Any generation mode costs 4 credits. A full audit-to-publish workflow on a single page is 5 credits. Unused credits do not roll over.
Will the output need editing before publishing?+
Most outputs are publish-ready. The pipeline produces 1200 to 2400 word articles with H2 and H3 structure, real tool names rather than generic terms, valid schema markup, meta tags, and FAQ sections on request. Author and publisher names can be baked into the schema during generation.
What if the worker fails or generates poor output?+
Credits refund automatically on any API failure, parse error, scraper block, language drift, or output that comes back too short to be useful. The refund is binary: either the output is delivered or the credit is returned. There is no partial-quality logic.
Can I rewrite content I don't own?+
The rewriter accepts any public URL the scraper can read. Use it on your own content, content you have rights to, or competitor content for analysis. The tool doesn't enforce ownership; you do. Republishing someone else's content as your own is a copyright issue regardless of how it was processed.
Does it work for languages other than English?+
English only for now. The pipeline includes a language detection step that returns a refund if the input or output drifts away from English. Multi-language support is on the roadmap; it requires tuning the banned-phrase sweep and tone presets per language.
Stop guessing. Run a diagnosis in 30 seconds.
Free check at the top of this page. If the gaps look real, the fix is one click away inside the full tool.