Match, Enrich, and Merge Data fromMultiple Sources
Identify that 'Microsoft Corp', 'MSFT', and 'Microsoft' are the same company. Deduplicate records and enrich your dataset with confidence.
What is Data Reconciliation?
Data reconciliation is the process of matching records across different datasets or against authoritative sources to ensure consistency.
In business, one entity (like a customer or product) often exists in multiple systems with slightly different names. This fragmentation leads to duplicate records, missed insights, and embarrassing errors.
AstralRefine solves this by using advanced fuzzy matching to identify that "IBM" and "International Business Machines" are actually the same company.
Common Matching Scenarios
Intelligent Matching with Confidence Scores
Select & Connect
Choose the column you want to reconcile (e.g., "Company Name") and select a reference source.
Fuzzy Matching
Our AI assigns a confidence score (0-100%) to every potential match found.
Review & Merge
Auto-accept high confidence matches. Manually review the rest with a sleek UI.
Match Against Authoritative Data
Wikidata
Free access to 100M+ entities including companies, people, and locations.
Custom Reference
Upload your own "Master List" (e.g., Product Catalog) to match against.
Clearbit
Enrich company records with logos, improved names, and firmographics. (API Key required)
Google Places
Validate addresses and add geocoordinates to location data. (API Key required)
Reconciliation FAQ
Stop Wasting Time on Data Cleanup
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