Unify Records Across Systems with AI-Assisted Entity Resolution
Determine which records belong together and how multiple entities are connected across your data. WinPure Entity AI™ uses Senzing's principle-based entity resolution engine to find difficult matches, reveal relationships between separate entities, and harmonise data without any infrastructure cost.
Know Who is Who and How they are Connected
Across large, dynamic datasets, establishing the true identity of each entity is a significant challenge. The same entity may appear in multiple records, with conflicting details and different histories spread across systems, making it difficult to determine who is who and who is connected to whom. These disconnected fragments can lead organisations to treat one entity as many, accidentally merge separate entities incorrectly, and overlook relationships that indicate shared ownership.
WinPure Entity AI™ weighs the combined evidence across every available record. It understands which details are unique, which are commonly shared, and which change over time, then resolves the records into trusted entity profiles and identifies their known connections. In just three simple steps, users can identify hidden relationships and harmonise data without having to build any custom programs.
Entity resolution once demanded specialist teams, enterprise budgets and months of configuration. WinPure makes entity resolution accessible, helping you resolve millions of records in days.
A Simplified, Accessible Approach to Entity Resolution
WinPure brings enterprise entity resolution into a guided module within the Clean&Match data quality platform for enterprises, enabling technical and non-technical users to resolve complex entities within a singular platform.
Resolve Entities in Three Simple Steps
Entity AI™understands what each field represents as identity evidence, regardless of what the column is called. When a user classifies a field as a name, date of birth, or passport number, the engine evaluates each attribute according to its discriminating weight in the resolution decision. For example, a passport number carries far more identity certainty than a first name, and the engine reflects that automatically without any additional rule configuration. Users can simply:
- Auto Map field types across all loaded source tables in a single click
- Manually assign or override semantic field types to sharpen the engine’s resolution accuracy
- Let the AI analyse selected files and columns to surface duplicates that traditional matching can fail to catch
Entity-Centric Record Matching
EntityAI™ compares each incoming record against the full entity profile built from every source already loaded, instead of relying on individual records in isolation (as with traditional matching). When two records share a similar attributed, such as an organisation name, a common address, or a relevant secondary identifier, the engine surfaces that relationship because it is reasoning against the entity as a whole. With this users can:
- Resolve records from multiple sources against a unified entity profile in a single session
- Isolate cross-table matches to identify entities that span source systems
- View the explanation column per matched group to see exactly what evidence linked records across sources
Create Persistent Records with Golden IDs
Once the engine has analysed your records, EntityAI™ surfaces matched results in a focused view grouped by entity. Teams review each group, determine whether records are duplicates, and select which record carries forward as the master. Every decision is human reviewed before anything is actioned. Once confirmed, each resolved entity is assigned a persistent Golden Record Identity™ that remains stable across every future resolution run.
Users can:
- Merge, update, or overwrite matched records and export the final master record output
- Match new incoming data against already resolved entities without reprocessing the full dataset
- View a field level explanation for every matched group showing exactly what evidence linked the records
Not Sure Where to Start With Entity Resolution?
The WinPure Entity Resolution Buyer’s Guide covers the key technical decisions, evaluation criteria, and the questions data teams ask us most during demos.
How WinPure Simplifies Entity Resolution Challenges for Teams
| The challenge | Why WinPure |
|---|---|
| Managing dirty source data | Profiling, standardisation, and resolution in one platform. |
| No shared identifier | Attribute based matching across any source. No common key required. |
| Choosing matching algorithms | Fuzzy, exact, numeric, and AI matching configurable per field. No code. |
| International names and scripts | Global name recognition including transliteration and script variation. |
| Threshold calibration | Configurable per entity type, adjustable mid-session. |
| Scale and compute cost | Blocking reduces candidate space before scoring. Runs on standard hardware. |
| Ongoing licensing costs | One licence. Unlimited resolution runs. No per-record pricing. |
| Survivorship logic | SmartMaster AI™ selects the golden record on completeness, consistency, and reliability. |
| Reviewing match results | Side by side match review with scores per field before anything commits. |
| Audit and governance | Full audit log per run. Attribute level match evidence exportable for compliance. |
Where Entity Resolution Changes the Quality of the Outcome
The method stays consistent across sectors, but the entity and the operational consequence change. WinPure supports the review depth, deployment control, and repeatability required across the following projects.
Single Customer View
Connect customer records held across sales, service, billing, and legacy systems. Keep account relationships visible while resolving each person or organisation to one persistent identity.
Patient Identity in Healthcare
Identify the same patient across hospitals, clinics, departmental systems, and migration waves. Golden ID supports continuity when demographics or contact details change.
Supplier and Vendor Records
Resolve suppliers entered under trading names, legal names, local branches, and inconsistent addresses. Consolidated entities improve spend analysis, onboarding controls, and vendor master maintenance.
Fraudulent Identity Resolution
Reveal relationships that exact matching misses, including shared addresses, phone numbers, accounts, and other identifiers. Analysts can review the evidence behind each connection before escalating it.
Migration and Consolidation
Resolve identities before records move into a new CRM, ERP, case management platform, or data warehouse. Persistent IDs keep later migration waves aligned with the mastered dataset.
Entity Resolution for AI
AI models are only as reliable as the data they train on. Resolving entities before training ensures your AI works from a single, accurate representation of each customer, supplier, or product so your AI doesn’t make mistakes.
Questions Data Teams Ask About Entity Resolution
Tell us about your entity resolution project.
Whether you are preparing for a system migration, consolidating records across sources, or building entity resolution into your data operations, our team will walk you through what the platform can deliver against your specific dataset and use case.