{"uid":"cap_oMUjmX9o3_Ue7NzIE42Kf","slug":"gateway-apiosk-com-data-deduplication-data-clean-dedupe-220d4bb1","name":"gateway.apiosk.com – Data Deduplication (data-clean/dedupe)","description":"Fuzzy-match a submitted record set and return cluster ids for likely duplicates.","url":"https://gateway.apiosk.com/data-clean/dedupe","method":"POST","headers":{},"bodySchema":null,"responseSchema":null,"example":null,"exampleRequest":null,"tags":["x402"],"displayCostAmount":"0.022","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":"$0.02/call","primary":{"kind":"static","protocol":"x402","network":"base","amountUsd":"0.02","per":"call","confidence":"exact"},"accepted":[{"kind":"static","protocol":"x402","network":"base","amountUsd":"0.02","per":"call","confidence":"exact"}]},"paymentMethods":[{"uid":"pm_xH2kgjbdWgBodUoQ_2e37","protocol":"x402","methodType":"crypto","chain":"base","mode":"charge","costAmount":"0.02","costPer":"request","priority":0,"asset":"0x833589fCD6eDb6E08f4c7C32D4f71b54bdA02913","unit":"request","depositMicros":null,"planRef":null}],"brandName":null,"brandSlug":null,"brandBaseUrl":null,"brandDocsUrl":null,"whatItDoes":"Deduplicates an array of records by clustering near-duplicate entries based on a similarity threshold, returning cluster assignments for each record.","exampleAgentPrompt":"I have a list of customer records and I want to find the duplicates — can you deduplicate them using a similarity threshold of 0.8 and tell me which ones cluster together?","exampleUseCases":[{"title":"Clean up duplicate supplier names","prompt":"I've got a list of supplier names from our procurement database and I suspect there are a bunch of near-duplicates due to typos and abbreviations — can you run deduplication on them with a similarity threshold of 0.85 and show me which ones are actually the same supplier?"},{"title":"Merge duplicate address records","prompt":"I have thousands of address entries exported from our CRM and a lot of them look like duplicates but with slight variations in spelling or formatting — can you cluster them by similarity so I can figure out which ones refer to the same location and clean up the list?"},{"title":"Identify duplicate product listings","prompt":"We scraped product listings from several sources and I think there are a lot of near-duplicate entries for the same products — can you deduplicate them using a fuzzy similarity threshold of 0.8 and group the ones that seem to be referring to the same product together?"}],"resultDescription":"Returns a JSON object with ok status, total record count, per-record cluster assignments (each record gets a cluster_id), and the total number of clusters found. Records sharing the same cluster_id are considered duplicates or near-duplicates of each other.","failureModes":["Empty or missing records array returns an error","Non-numeric threshold value causes validation failure","Threshold outside valid range (e.g. >1 or <0) may return error or unexpected clusters","Very large record arrays may time out or exceed payload limits","Malformed record objects may cause partial processing or error","Network/payment failure (x402) if USDC balance is insufficient"],"whenToPreferThis":"Choose this endpoint when you need to programmatically deduplicate or cluster an array of records by similarity — especially for entity resolution, data cleaning pipelines, or merging near-duplicate entries. Prefer it over exact-match deduplication when fuzzy/approximate matching is needed, controlled by the threshold parameter.","instructions":null,"reviewSummary":null,"reviewSummaryHighlights":null,"reviewSummaryConcerns":null,"reviewSummaryGeneratedAt":null,"activationCount":0,"lastUsedAt":null,"lastSuccessfullyRanAt":null,"lastHealthCheckAt":"2026-09-14T12:57:57.929Z","isFirstParty":false}