Entify review
A file-focused entity resolution workspace with explainable matching and flexible exports.
Reviewed by iTechGuides Editors · Editorial team · Updated Oct 2026
Entify is an open-source workspace for finding duplicate or linked records in messy datasets. It is designed for data stewards, analysts, and engineers working with customer or organizational records. Users can upload tabular files, profile columns automatically, configure blocking and comparison logic, run entity resolution, inspect match clusters, and export a deduplicated result. The product supports web, API, and self-hosted use, with a FastAPI backend, Next.js frontend, and DuckDB included in its project description.
Its file and ecosystem coverage suit teams working across varied data sources. Entify accepts CSV, TSV, Parquet, JSON, and Excel inputs, while integrations include Splink, DuckDB, Supabase, and Clerk. Automatic column profiling and blocking-rule configuration help structure the matching workflow before resolution begins. Comparison options include exact matching, Jaro-Winkler, Jaccard, and Levenshtein methods. Semantic blocking with sentence-transformers is available as an optional extra, giving teams another approach for more varied text-based records.
The standout capability is the combination of Splink-powered probabilistic entity resolution and review-oriented explanations. Match clusters and waterfall charts make the resolution logic inspectable, while field-level survivorship supports a deduplicated export. PDF audit reports add a documented output for review or handoff. Entify is a strong fit for file-based deduplication projects that need multiple comparison methods, explainability, and self-hosted control. Teams seeking duplicate prevention at the point of record creation should choose a different tool, because Entify focuses on resolving existing datasets rather than preventing new duplicates.
Entify pros and cons
- Where it wins
- Ingests CSV, TSV, Parquet, JSON, and Excel files
- Combines exact, fuzzy, semantic, and probabilistic matching methods
- Provides explainable clusters, survivorship exports, and PDF audit reports
- Where it doesn't
- Does not prevent duplicates during record creation
- Semantic blocking with sentence-transformers is an optional extra
- Self-hosted deployment may require local technical setup
Entify fact sheet, pricing and score →
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