The AI search toolkit · 2.0Content · Tool 08
Free Entity & Topic Coverage Checker
Compare the topics you care about across your page and a reference text. Add explicit phrases and aliases, find relevant gaps and export an editorial review list without treating keyword repetition as content quality.
Compare topic coverage
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Your evidence comes first.
Compare supplied topics and aliases across your content and a reference page. Enter your information or load the labelled example to see the workflow.
No score, scan or platform result is invented while you wait.
A practical workflow
From input to a useful next step.
Choose a fair comparison
Paste the main text from your page and a relevant reference. Remove navigation, cookie notices and unrelated sections when they would distort the comparison.
Define the topic vocabulary
Enter one topic per line. Add aliases after a vertical bar, such as “structured data | schema markup”, to make your assumptions explicit.
Decide what deserves coverage
Review reference-only topics and absent-in-both topics. Add material only when it serves your audience and you can contribute a useful explanation or evidence.
Know what you are measuring
The method behind the result.
Compare supplied topics and aliases across your content and a reference page. The scope and limitations below help you interpret the output and decide what to verify next.
Explicit phrases instead of hidden scoring
The checker uses the vocabulary you supply. It looks for complete token sequences rather than matching a short term inside an unrelated word. It does not run a named-entity or embedding model.
Aliases are visible assumptions
Each line can contain up to eight variants. Presence of any variant counts as coverage; the displayed count is the highest individual alias count, which avoids adding overlapping aliases together.
A gap is an editorial question
A competitor can discuss a topic that does not belong on your page. Review intent, factual support and user needs before expanding content. The output is a decision aid, not an instruction to imitate another page.
Clear answers
Before you act on the result.
Understand the inputs, what the output can establish and how to use it in a broader audit.
Is this a semantic entity-analysis system?
No. It compares explicit topics and phrase aliases in the text you provide. The method is transparent and local, but it does not identify knowledge-graph entities or calculate embedding similarity.
How do I enter synonyms?
Put the main topic first and separate aliases with a vertical bar. For example: “AI visibility | AI search visibility”. Each line is treated as one topic for coverage reporting.
Does a missing competitor term mean I must add it?
No. First check whether it answers the intent of your page and whether you can support the content. A reference-only phrase may be irrelevant, promotional or incidental.
How are counts calculated?
The tool counts case-insensitive token-sequence occurrences for each alias and displays the highest alias count. It does not sum overlapping aliases or turn repetition into a quality score.
Does the checker support other languages?
It recognizes Unicode letters and numbers, but token matching works best where words have clear boundaries. Languages without spaces and language-specific morphology require a more specialized review.
Can I export a content brief?
You can export the topic comparison as CSV. Use the findings as a review list, then add your own intent, evidence, outline and ownership decisions before turning it into a writing brief.
Continue your investigation
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Built for DigiMSM · Methods reviewed 14 September 2026 · Free tool; service work is scoped separately.