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POSTName Match API

AI-powered name comparison trained on 100M+ Indian name records — returns a match score (0–1) and match category for automated KYC decisions.

POST/tools/kyc/name-match

Name Match is an AI-powered name comparison API built for India's complex naming conventions. Trained on over 100 million Indian name records, it handles initials, abbreviations, phonetic and regional spelling variants, salutation patterns (S/O, D/O), subset matching, and name ordering variations — returning a numeric score and match category that let you set rule-based pass/fail thresholds.

Request

Body parameters

initiator_idstringRequired

Registered mobile number of the API user (see Platform Credentials).

example: 9962981729

client_ref_idstringoptional

Unique reference ID per API call, generated by your system (max 20 characters).

example: 2026010100123456789

name_1stringRequired

First name string to compare (e.g. the name from a PAN card or bank record).

example: S K Mishra

name_2stringRequired

Second name string to compare against name_1 (e.g. the name from an Aadhaar or GST record).

example: Satish Kumar Mishra

Responses

  • statusnumber

    Primary success indicator (0 = success).

  • messagestring

    Human-readable response / error message.

  • response_status_idnumber

    Granular status id; see the shared error-codes table.

  • response_type_idnumber

    A unique id for every possible response shape (success or error) — useful for client logic branching and analytics.

  • dataobject

    API-specific response payload.

    • name_1string

      The first name string as submitted in the request.

    • name_2string

      The second name string as submitted in the request.

    • scorenumber

      Numeric match score between 0 and 1. Thresholds: 1.0 = Direct Match; 0.85–0.99 = Good Partial Match; 0.60–0.84 = Moderate Partial Match; 0.34–0.59 = Poor Partial Match; 0.00–0.33 = No Match.

    • reasonstring

      Human-readable explanation of the match result — effectively the match category label along with the AI model's rationale (e.g. initials expansion, phonetic match, subset match).

Next steps

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