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NetIntel Entity Extraction
x402
NetIntel Entity Extraction is a paid API for AI agents from netintel-production-440c.up.railway.app, paid per call via x402, $0.05/call, status unknown (last checked 2026-09-13).
Extracts named entities (persons, organizations, locations, dates, emails, URLs, money, products, phone numbers) from text and returns a scored, graded result
Price / call$0.05
Success—
StatusUnknown
How this service works
Extract named entities from text using Claude Haiku — people, organizations, locations, dates, emails, URLs, money amounts, and products — returned as structured typed arrays so agents can pull structured signals out of unstructured text in one call.
Output
A JSON object containing a letter grade (e.g. 'A') and numeric score (0–100) reflecting extraction confidence, a breakdown of entities by type (person, organization, location, date, email, url, money, product, phone), a findings array for any notable flags, and a total_entities count with a list of types_extracted.
Example use cases
Parse vendor contacts from supplier emails
“I've got a stack of supplier inquiry emails with contact info scattered throughout. Can you extract all the people, company names, phone numbers, and email addresses so I put together a clean vendor list?”
Identify deal terms in contract documents
“Pull out all the dates, monetary values, organization names, and people mentioned in this service agreement so I can quickly see who's involved, what the payment amounts are, and when key deadlines hit.”
Monitor news for competitor mentions
“I need to track which companies and executives are being mentioned in these industry news articles, plus any URLs linking to their sites or press releases. Can you extract all the organizations, people, and links so I can build a competitive intelligence report?”
Technical details
Endpoint
POSThttps://netintel-production-440c.up.railway.app/entity-extractWhen to prefer
Use this endpoint when you need structured named entity recognition with a quality score and letter grade from unstructured text. Prefer it over generic NLP pipelines when you need multi-type entity extraction (people, orgs, locations, dates, emails, URLs, money, products, phones) in a single call with pay-per-use micropayment pricing rather than a subscription.
Side effects
- Empty or missing text body returns no entities with a low score
- Extremely short or single-word inputs may yield no extractable entities
- Non-English text may result in reduced accuracy or empty entity lists
- Malformed request body causes a 400-level error
- Payment failure via x402 prevents endpoint access entirely
Input
Schema · Request body
| Field | Type | Description |
|---|---|---|
| inputrequired | object | |
| output | object |
Output
Schema · Response
{
"type": "json",
"example": {
"grade": "A",
"score": 100,
"entities": {
"url": [
"https://acme.com"
],
"date": [
"2024-03-15"
],
"email": [
"jane@acme.com"
],
"money": [
"$1.2M"
],
"phone": [],
"person": [
"Jane Doe",
"John Smith"
],
"product": [
"WidgetPro"
],
"location": [
"San Francisco"
],
"organization": [
"Acme Corp"
]
},
"findings": [],
"total_entities": 9,
"types_extracted": [
"person",
"organization",
"location",
"date",
"email",
"url",
"money",
"product",
"phone"
]
}
}Tags
x402
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