# Saylor Innovations Fuzzy Match API

> Saylor Innovations Fuzzy Match API is a paid API for AI agents from saylorinnovations.com, paid per call via x402, $0.01/call, status unknown (last checked 2026-10-30).

Ranks candidate strings against a reference string using Jaro-Winkler, Levenshtein, token-set similarity, and Soundex scores, returning a composite score and verdict for each candidate

## Facts

- Endpoint: GET https://saylorinnovations.com/api/text/fuzzy-match?utm_source=zero.xyz
- Price: $0.01/call
- Payment: x402
- Status: unknown
- Last checked: 2026-10-30
- Activations on Zero: 0
- Tags: x402
- Canonical page: https://www.zero.xyz/c/saylor-innovations-fuzzy-match-api-266711b7
- Structured record (JSON): https://api.zero.xyz/v1/capabilities/cap_O67t38ttooNxxsk7weoLU

Status and success rate cover calls made through Zero and Zero's own probes. Third-party monitors may report differently.

## How to call it through Zero

Zero handles the 402 payment challenge and records the run. With the Zero CLI installed (`npm i -g @zeroxyz/cli`):

```sh
zero fetch --capability saylor-innovations-fuzzy-match-api-266711b7
```

Example prompt: Score these candidate names — 'Jonathan Smith|Jon Smith|J. Smith|Johnathan Smyth|Jane Smith' — against the input 'Jonathan Smith' and tell me which one is the best match along with the Jaro-Winkler, Levenshtein, and token-set scores for each.

## When to prefer this

Choose this endpoint when you need multi-algorithm fuzzy string comparison (not just one metric) and want a ready-made composite verdict — ideal for name matching, record deduplication, or entity resolution workflows where a single similarity score is insufficient. Prefer it over single-algorithm libraries when you need Soundex phonetic matching combined with edit-distance and token-set metrics in one call.

## Known failure modes

- Missing required 'a' parameter returns an error
- More than 50 candidates separated by '|' may be truncated or rejected
- Empty candidate list returns no matches
- Non-string or malformed input may cause scoring errors
- Network timeout on large candidate sets

## How this service works

Rank candidate strings against an input with Jaro-Winkler, Levenshtein, token-set and Soundex scores plus a verdict, for name matching and deduplication

## Output

Returns a ranked list of all candidate strings with per-candidate scores (Jaro-Winkler, Levenshtein distance, Levenshtein similarity, token-set similarity, Soundex first-word match, composite 0–100 score, and a text verdict), plus a 'best' object highlighting the top-scoring candidate, the original input string, a source label, and a contextual note.

## Request schema (JSON Schema)

```json
{
 "type": "object",
 "$schema": "https://json-schema.org/draft/2020-12/schema",
 "required": [
  "input"
 ],
 "properties": {
  "input": {
   "type": "object",
   "required": [
    "type",
    "method"
   ],
   "properties": {
    "type": {
     "type": "string",
     "const": "http"
    },
    "method": {
     "enum": [
      "GET"
     ],
     "type": "string"
    },
    "queryParams": {
     "type": "object",
     "required": [
      "a",
      "candidates"
     ],
     "properties": {
      "a": {
       "type": "string",
       "description": "String to match"
      },
      "candidates": {
       "type": "string",
       "description": "Up to 50 candidates separated by |"
      }
     }
    }
   },
   "additionalProperties": false
  },
  "output": {
   "type": "object",
   "required": [
    "type"
   ],
   "properties": {
    "type": {
     "type": "string",
     "const": "json"
    },
    "example": {
     "type": "object",
     "required": [
      "best",
      "matches"
     ],
     "properties": {
      "best": {
       "type": "object",
       "properties": {
        "score": {
         "type": [
          "number",
          "null"
         ],
         "description": "Score from 0 (worst) to 100 (best)"
        },
        "verdict": {
         "type": [
          "string",
          "null"
         ],
         "description": "Overall assessment derived from the listed reasons"
        },
        "candidate": {
         "type": [
          "string",
          "null"
         ],
         "description": "Best matching candidate"
        },
        "jaro_winkler": {
         "type": [
          "number",
          "null"
         ],
         "description": "Jaro winkler"
        },
        "levenshtein_distance": {
         "type": [
          "number",
          "null"
         ],
         "description": "Levenshtein distance"
        },
        "token_set_similarity": {
         "type": [
          "number",
          "null"
         ],
         "description": "Token set similarity"
        },
        "levenshtein_similarity": {
         "type": [
          "number",
          "null"
         ],
         "description": "Levenshtein similarity"
        },
        "same_soundex_first_word": {
         "type": [
          "boolean",
          "null"
         ],
         "description": "Same soundex first word"
        }
       },
       "description": "Highest-scoring candidate with its component scores and verdict"
      },
      "note": {
       
… (truncated)
```

## More

- Live health (JSON, refreshed every minute): https://www.zero.xyz/c/saylor-innovations-fuzzy-match-api-266711b7/health.json
- [Zero catalog index](https://www.zero.xyz/llms.txt)
- [Other services from saylorinnovations.com](https://www.zero.xyz/host/saylorinnovations.com/llms.txt)