How it works
Paste any long prompt, research brief, customer email or product description. The finder strips stopwords, tokenises what is left into unigrams, bigrams and trigrams, counts the frequency of each and surfaces capitalised sequences as likely named entities. Everything happens locally in your browser - no prompts ever leave the page.
Why this matters for AI search
- Semantic anchors outrank keywords. AI assistants hash long prompts into a handful of embedding neighbours - the top 5-10 keywords are where the matching actually happens.
- Phrases beat unigrams. A bigram like "customer support" carries more disambiguation power than either word alone.
- Entities drive citations. Named entities (brands, products, places) are the strongest retrieval signal. If your top keywords are generic, your page will never win a citation race.
Ship it
- Run any prompt you want to rank for through this tool and note the top 5 bigrams.
- Make sure those exact bigrams appear in your page H1, first paragraph and a schema description.
- Feed the top phrases into the Query Expansion Tool to discover adjacent phrasings AI assistants may match on.
- Pair with the Query Intent Classifier to build a page shape that matches the winning intent.
- Deep reading: long-tail keywords for AI search.