Fuzzy match
messy data with a
single API call
Send two arrays. Get back fuzzy matches, deduplication clusters, and similarity scores — from 10 records to 10 million. No pipeline to build. No infrastructure to maintain.
# Match your CRM leads against master database
import requests
r = requests.post("https://api.similarity-api.com/reconcile",
headers={"Authorization": f"Bearer {API_KEY}"},
json={
"data_a": ["Microsft Corp", "apple inc"],
"data_b": master_db,
"config": {
"similarity_threshold": 0.75,
"to_lowercase": True,
"use_token_sort": True
}
}
)Used by teams doing data work including
The Problem
Works fine at 1,000 rows.
Breaks at 100,000.
Matching a few hundred records? Any approach works. Once you cross 100K rows, every in-house solution starts collapsing under its own weight.
Pairwise comparison doesn't scale
Naive fuzzy matching is O(n²). At 1M records, that's trillions of comparisons — hours of CPU, gigabytes of memory, and a job that times out before it finishes.
You maintain more than you ship
Normalization preprocessing, blocking strategies, threshold tuning, algorithm selection — every project reinvents the same plumbing. It's not your core product.
Locked to one language and stack
Open-source matching libraries are Python-only. The moment your pipeline runs in Go, Java, a Salesforce flow, or an n8n automation — you're on your own.
The Alternative
Ship fuzzy matching
without the fuzzy pipeline
Everything you'd have to build and maintain yourself — replaced by a single POST request.
Build it yourself
Pipeline to build, test, and maintain
Call Similarity API
Similarity API
1 API CallPerformance at Scale
The gap isn't linear.
It's exponential.
Anyone can match small datasets. The question is what happens at a million rows.
What Happens to Your Data
Noisy input.
Clean output.
Real-world records are inconsistent — casing, punctuation, word order, missing suffixes. The API scores similarity at the character level and groups records that refer to the same entity, however they were written.
Works the same whether you're deduplicating a single list or reconciling two datasets against each other.
How It Works
Two endpoints.
Every data matching problem.
Deterministic, explainable similarity scoring — stable across runs, tunable with clear parameters.
/reconcile
POSTMatch records in Dataset A against a reference Dataset B. One call handles two strings or two million — same API, same parameters.
- →CRM lead matching against master account list
- →Vendor name reconciliation across ERP systems
- →Product catalog linkage without clean IDs
- →Fuzzy JOIN between datasets from different sources
/dedupe
POSTFind near-duplicate records within a single dataset. Returns pairs, clusters, or a clean deduplicated list — your choice of output format.
- →Clean contact lists before CRM import
- →Detect duplicate company or supplier records
- →Identify re-registrations in signup flows
- →Content deduplication in knowledge bases
Tunable parameters on every request: set a similarity_threshold, control preprocessing ( to_lowercase, remove_punctuation, use_token_sort ), strip common business entity suffixes and prefixes (Inc., Corp., Ltd., LLC, and more), and choose output format. Results are deterministic — same input always returns the same scores.
Use Cases
The same problem
across every industry
Anywhere humans type names into fields, you need fuzzy matching.
CRM Deduplication
Clean contact and account records before import. Catch "Microsoft Corp", "Microsoft Corporation", and "MSFT" as the same entity.
Data Reconciliation
Link records across two systems that share no common ID — supplier names in your ERP vs your finance system.
Product Catalog Matching
Match incoming vendor SKUs against your master catalog. Handle abbreviations, missing punctuation, and word-order differences.
KYC & Compliance
Screen entity names against watchlists and sanction databases where name variants, transliterations, and abbreviations abound.
Lead Routing & Enrichment
Match inbound leads against your CRM before creating new records. Stop sales reps from contacting the same company twice.
ETL & Data Pipelines
Add fuzzy matching as a step in your ingestion pipeline without spinning up additional infrastructure or managing Python dependencies.
Built For
Works for your team,
in your stack
For practitioners wiring systems together — whether that's code, a cloud pipeline, or a no-code automation.
Data Engineers
Drop fuzzy matching into Airflow, Databricks, or any cloud pipeline as a plain HTTP step. No Python dependency, no blocking logic, no infra.
GTM & RevOps Engineers
Call directly from HubSpot workflows or Salesforce Flow. Match inbound leads, dedup contacts, and route accounts — without leaving your CRM.
CRM & Data Consultants
Deliver cleaner migrations and reconciliation projects faster — without rebuilding matching scripts from scratch for every client engagement.
If it speaks HTTP, it works
Automation
- n8n
- Zapier
- Make
- Workato
CRM & RevOps
- Salesforce Flow
- HubSpot Workflows
- Pipedrive
- Zoho CRM
Data & Cloud
- Databricks
- AWS Lambda
- GCP Functions
- Azure Data Factory
Orchestration
- Apache Airflow
- Prefect / Dagster
- dbt (via hooks)
- Any REST client
In no-code tools: use any HTTP Request node — POST, Bearer token, JSON body. No plugin needed.
Copy-Paste Ready
Works in your language,
in your stack
One REST call. Structured JSON response. Node, Python, Go, Java — all covered in the docs.