{"uid":"cap_O67t38ttooNxxsk7weoLU","slug":"saylor-innovations-fuzzy-match-api-266711b7","name":"Saylor Innovations Fuzzy Match API","description":"Rank candidate strings against an input with Jaro-Winkler, Levenshtein, token-set and Soundex scores plus a verdict, for name matching and deduplication","url":"https://saylorinnovations.com/api/text/fuzzy-match?utm_source=zero.xyz","method":"GET","headers":{},"bodySchema":{"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":{"type":"string","description":"Caveat or context for reading the result"},"input":{"type":"string","description":"String that was matched"},"source":{"type":"string","description":"Data source the values come from"},"matches":{"type":"array","items":{"type":"object","properties":{"score":{"description":"Score from 0 (worst) to 100 (best)"},"verdict":{"description":"Overall assessment derived from the listed reasons"},"candidate":{"type":["string","null"],"description":"Candidate string"},"jaro_winkler":{"description":"Jaro winkler"},"levenshtein_distance":{"description":"Levenshtein distance"},"token_set_similarity":{"description":"Token set similarity"},"levenshtein_similarity":{"description":"Levenshtein similarity"},"same_soundex_first_word":{"description":"Same soundex first word"}}},"description":"Matching items"},"soundex":{"type":"string","description":"Soundex code of the first word of the input"},"normalized":{"type":"string","description":"Input after accent, case and punctuation folding"}}}}}}},"responseSchema":null,"example":null,"exampleRequest":null,"tags":["x402"],"displayCostAmount":"0.01","displayCostAsset":"USDC","priceDynamic":false,"priceHint":null,"priceStatus":"priced","priceSource":"probe","requiresHandshake":false,"reviewCount":0,"rating":{"score":"0.00","successRate":"0.00","reviews":0,"stars":null,"state":"unrated"},"availabilityStatus":"unknown","priceObserved":null,"sessionDeposit":null,"pricing":{"kind":"static","summary":"$0.01/call","primary":{"kind":"static","protocol":"x402","network":"base","amountUsd":"0.01","per":"call","confidence":"exact"},"accepted":[{"kind":"static","protocol":"x402","network":"base","amountUsd":"0.01","per":"call","confidence":"exact"}]},"paymentMethods":[{"uid":"pm_luYmPKg3sFHjQo0uqQYlE","protocol":"x402","methodType":"crypto","chain":"base","mode":"charge","costAmount":"0.01","costPer":"request","priority":0,"asset":"0x833589fCD6eDb6E08f4c7C32D4f71b54bdA02913","unit":"request","depositMicros":null,"planRef":null}],"brandName":null,"brandSlug":null,"brandBaseUrl":null,"brandDocsUrl":null,"whatItDoes":"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","exampleAgentPrompt":"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.","exampleUseCases":[{"title":"Deduplicating customer name records","prompt":"I have a canonical customer name 'Catherine O'Brien' and these possible duplicates in our database: 'Catherine OBrien|Kathryn O'Brien|C. O'Brien|Katherine Brien|Cathy O'Brian' — can you score all of them for similarity and tell me which is the most likely duplicate?"},{"title":"Entity resolution for vendor onboarding","prompt":"We're onboarding a vendor who says their name is 'Acme Global Solutions' — check how closely that matches these existing vendors: 'ACME Solutions LLC|Acme Globl Soln|Acme International|Global Acme Ltd|Acme Global Solution' and give me a verdict on whether any are likely the same company."},{"title":"Name screening against a watchlist","prompt":"Run a fuzzy match of 'Vladimir Petrov' against this list of names: 'Vladmir Petrov|Vladimir Petroff|V. Petrov|Vladamir Petrov|Vladimir Petrowski' and tell me the composite score and soundex verdict for each so I can flag potential matches."}],"resultDescription":"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.","failureModes":["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"],"whenToPreferThis":"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.","instructions":null,"reviewSummary":null,"reviewSummaryHighlights":null,"reviewSummaryConcerns":null,"reviewSummaryGeneratedAt":null,"activationCount":0,"lastUsedAt":null,"lastSuccessfullyRanAt":null,"lastHealthCheckAt":"2026-10-30T00:37:59.867Z","isFirstParty":false,"canonicalSlug":"saylor-innovations-fuzzy-match-api-266711b7"}