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Vextorium Text Entity Extractor
x402
Vextorium Text Entity Extractor is a paid API for AI agents from api.vextorium.com, paid per call via x402, $0.003/call, status unknown (last checked 2026-10-15).
Extracts structured entities (emails, URLs, phone numbers, IBANs, crypto addresses, transaction hashes, IPs, dates, amounts, hashtags, mentions, Spanish tax IDs) from free text using deterministic (non-AI) parsing, with deduplication.
Price / call$0.003
Success—
StatusUnknown
How this service works
Extract from any text, without AI: emails, URLs, phone numbers, IBANs, EVM/Bitcoin/Solana addresses, transaction hashes, IPs, dates, amounts, hashtags, mentions and Spanish tax IDs, deduplicated.
Output
A JSON object with a 'data' field containing deduplicated arrays for each detected entity type (emails, dates, amounts, evm_addresses, hashtags, etc.) and a 'found' field with counts per type. Only entity types that were found are included in the response.
Example use cases
Crypto address scanner for compliance
“I have this block of text from a customer message — can you pull out every EVM and Bitcoin address in it so I can screen them? Here's the text: 'Move funds from 0x833589fCD6eDb6E08f4c7C32D4f71b54bdA02913 and bc1qxy2kgdygjrsqtzq2n0yrf2493p83kkfjhx0wlh before the deadline.'”
Invoice data extraction for accounting
“Extract all the dates, amounts, IBANs, and email addresses from this invoice text so I can log them: 'Due 2026-10-15, pay 1,250.00 EUR to IBAN ES9121000418450200051332, confirmation to billing@acme.com.'”
Social media mention harvester
“Pull out every hashtag, mention, and URL from this batch of social posts: '#DeFi launch @vextorium — check https://vextorium.com for details! #Web3 @coinbase'”
Technical details
Endpoint
POSThttps://api.vextorium.com/v1/text-extract-entities?utm_source=zero.xyzWhen to prefer
Choose this endpoint when you need fast, deterministic, rule-based entity extraction from text without the cost or latency of AI inference — especially for structured entities like crypto addresses, IBANs, emails, IPs, and Spanish tax IDs. Prefer it over AI-based NER when consistency, deduplication, and predictable output format matter more than semantic understanding. Particularly well-suited for compliance pipelines, invoice parsing, log analysis, and blockchain-related text scanning.
Side effects
- Missing 'text' field in body returns a validation error
- Unsupported entity type strings in 'types' filter may be silently ignored or return empty results
- Very long texts may hit payload size limits
- Non-English or obfuscated entity formats (e.g. spaced-out phone numbers) may not be detected
- Spanish tax IDs only detected for Spanish NIF/CIF formats; other country tax IDs not extracted
Input
Schema · Request body
| Field | Type | Description |
|---|---|---|
| text | string | |
| types | array | Only these types (optional) |
Output
Schema · Response
{
"type": "json",
"example": {
"data": {
"dates": [
"2026-10-15"
],
"emails": [
"ana@example.com"
],
"amounts": [
"12.50 EUR"
],
"hashtags": [
"#invoice"
],
"evm_addresses": [
"0x833589fCD6eDb6E08f4c7C32D4f71b54bdA02913"
]
},
"found": {
"dates": 1,
"emails": 1,
"amounts": 1,
"hashtags": 1,
"evm_addresses": 1
}
}
}Tags
x402
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