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KiHustle Keyword Proximity Analyzer
x402
KiHustle Keyword Proximity Analyzer is a paid API for AI agents from kihustle.tech, paid per call via x402, $0.002/call, status unknown (last checked 2026-10-17).
Analyzes how close two specified keywords are to each other within a given text, returning a proximity result.
Price / call$0.002
Success—
StatusUnknown
How this service works
Kostenlose Guides, Solo-Playbooks und Artikel zu KI, Automation und Side Hustles — für Menschen, die mit echten Systemen online Einkommen aufbauen wollen. Transparent finanziert über faire Affiliate-Links.
Output
Returns a JSON object with a 'result' field indicating the proximity computation outcome and a 'status' field confirming success. The exact proximity metric (e.g. distance in words, co-occurrence score) is represented in the 'result' value.
Example use cases
SEO keyword co-occurrence check
“I have this blog post intro — can you tell me how close the keywords 'AI' and 'side hustle' appear to each other in it? Here's the text: 'AI is transforming how people approach their side hustle by automating repetitive tasks and freeing up time for creative work.'”
Content quality review for keyword pairing
“Check this paragraph for me and tell me how close the words 'affiliate' and 'income' are to each other: 'Building affiliate partnerships has been one of the most reliable ways to generate passive income online for solo creators.'”
Competitor content keyword analysis
“I've copied this section from a competitor's landing page — can you measure the proximity between the words 'automation' and 'revenue' in this text: 'Smart automation workflows have helped thousands of creators scale their revenue without hiring a team.'?”
Technical details
Endpoint
POSThttps://kihustle.tech/nexus/api/v1/keyword-proximity-analyzer?utm_source=zero.xyzWhen to prefer
Choose this endpoint when you need a lightweight, low-cost computation to determine how closely two specific keywords appear within a text body — particularly useful for SEO content analysis, keyword clustering checks, or verifying keyword co-occurrence in articles. Prefer it over full NLP pipelines when you only need proximity between exactly two terms rather than full semantic analysis.
Side effects
- Missing required fields (text, word_a, or word_b) may return an error or empty result
- Words not found in the provided text may return a null or zero proximity result
- Very long text inputs may cause slower processing or timeouts
- Ambiguous or identical word_a and word_b values may produce unexpected output
- Non-English text behavior is unclear given the German-language site context
Input
Schema · Request body
| Field | Type | Description |
|---|---|---|
| text | string | |
| word_a | string | |
| word_b | string |
Output
Schema · Response
{
"type": "json",
"example": {
"result": "processed",
"status": "success"
}
}Tags
x402
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