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Orthogonal Text-to-Profile-Search-Params
x402
Orthogonal Text-to-Profile-Search-Params is a paid API for AI agents from x402.orthogonal.com, paid per call via x402, $0.04/call, status unknown (last checked 2026-09-14).
Converts a free-form natural language description of a candidate profile into structured search filter parameters for profile/talent search.
Price / call$0.04
Success—
StatusUnknown
How this service works
Takes free-form text (e.g., 'Software engineers in US with 5+ years of experience') and converts it into a structured set of filters for profile search. This endpoint helps transform natural language queries into standardized search parameters such as job titles, skills, seniority, locations, past experiences, education, languages, and more.
Output
A structured object containing standardized search filter parameters derived from the natural language query, such as job titles, required skills, seniority levels, geographic locations, years of experience, past employers, education credentials, and spoken languages — ready to be passed to a profile search API.
Example use cases
Recruiter talent search automation
“I need to find mid-level data scientists in Europe who know Python and SQL and have worked at a tech startup — can you turn that into structured search filters I can use to query our talent database?”
Sourcing filter builder for niche roles
“Help me build search filters for this role description: 'Product managers with fintech experience, based in New York or London, at least 3 years in the role, ideally with an MBA.'”
Multi-criteria candidate pipeline setup
“I want to search for DevOps engineers fluent in English and Spanish, located in Latin America, with experience in Kubernetes and AWS, and at least 4 years of experience — convert that into structured search parameters.”
Technical details
Endpoint
POSThttps://x402.orthogonal.com/fiber/v1/text-to-search-params/profilesWhen to prefer
Choose this endpoint when you have a free-form natural language description of a candidate profile or talent requirement and need to convert it into structured, machine-readable search parameters. It is ideal for recruiting agents, HR automation workflows, or talent sourcing pipelines that accept human-written job criteria and need to query a structured profile database. Prefer this over building your own NLP parser when you need standardized fields like job titles, skills, seniority, and locations extracted reliably from plain English.
Side effects
- Vague or ambiguous query may produce overly broad or incomplete filters
- Highly domain-specific jargon may not map correctly to standard taxonomy fields
- Missing required context (e.g. no location or seniority mentioned) results in sparse filter output
- Malformed or empty query string returns an error or empty filter set
Input
Schema · Request body
| Field | Type | Description |
|---|---|---|
| query | string |
Tags
x402
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