Turn Fragmented Records into Golden Identities
WinPure Clean&Match Enterprise™ (CAM) cleanses inconsistent records and, with AdaptiveMatch™, resolves duplicates across CRM, ERP and operational data, all inside your own infrastructure.
The Industry’s Go-To Platform for Data Quality Operations
For organisations handling sensitive contact data, WinPure’s Clean & Match provides a level of control that cloud based processing often cannot support. The platform runs entirely within the customer’s own infrastructure, allowing data teams to clean & prepare data while staying in full control of their environment.
With CAM, you get a new, efficient, modern way to resolving legacy data quality issues. Now, easily:
- Get data quality insights and assess the health of your data
- Match disparate and duplicate records without unique identifiers
- Consolidate and create golden identities with a few simple clicks
Matching Depth, Explainable AI, and a Workflow that Aligns with Your Infrastructure
A Complete Data Quality Platform
WinPure Clean&Match Enterprise™ brings profiling and cleansing into one platform. DataQuality Insights™ reports completeness, consistency and duplication by dataset and by column, then CleanAI™ reads the data type of each column and fills in the cleaning matrix. You can configure every operation and reuse cleaning workflows across future imports, which keeps data preparation consistent as requirements change.
- DataQuality Insights™ profiling with column level statistics and quality scores
- CleanAI™ builds the cleaning matrix from the data type of each column
- Reusable cleaning workflows, custom dictionaries and validation rules
Matching with the Control to Get It Right
Choose the matching approach your data requires, from exact, fuzzy, numeric and AI matching rules to entity resolution performed via a principles-based MatchAI™ module that identifies entities and relationships across fragmented records. You can review each recommendation and refine the configuration to balance match coverage with precision.
- Exact, fuzzy, numeric and AI matching, configurable by column
- AdaptiveMatch™ recommends methods and thresholds for each column
- Match Explanation™ shows why records were grouped
Build a Single Source of Truth in a Secure Environment
Bring fragmented and duplicate records together into a reliable dashboard view where your team can define which records become new master records, and which need to be merged or purged. SmartMaster AI™ scores matched records to select the golden record, and GoldenID™ keeps the same identifier on each entity across imports and migrations, so repeat duplication does not return. With flexible deployment options your team can build and preserve the single source of truth ready for downstream application, all locally in your environment.
- SmartMaster AI™ selects the golden record from each matched group
- GoldenID™ keeps one stable identifier per entity across runs
- Merge or purge records from a single review dashboard
One Platform. Every Step of the Data Quality Journey.
WinPure brings every stage of enterprise data quality into a single no-code platform. From connecting and profiling data through cleansing, verification, identity resolution, golden record creation and ongoing governance, organisations can transform fragmented, inconsistent data into trusted data that powers Identity Intelligence, analytics, AI and better business decisions
Connect to Virtually Any Data Source
Import data from databases, spreadsheets, CRM platforms and flat files into WinPure without modifying your original source. Support for multiple data sources allows you to consolidate information from across your organisation, creating a secure working copy for cleansing, matching and analysis.
Benchmark: A Sample Matching Run on 604,714 Records
| Measure | WinPure Clean&Match v12 |
|---|---|
| Records processed | 604,714 |
| Time to complete | 33.27 seconds |
| Match groups | 22,368 |
| Potential duplicate records | 48,278 |
| Duplicate rate | 7.98% |
| Hardware | Standard desktop, 32GB RAM |
| Matching configuration | AdaptiveMatch™ automatic rule configuration |
Why Data Teams Choose WinPure Over Competitors
| Capability | WinPure Clean and Match Enterprise | Cloud DQ Tools | In-House Solution |
|---|---|---|---|
| End-to-end data quality in one platform | ✓ Profile, clean, match, resolve, verify in one workflow | — Typically single-function tools with limited end-to-end capabilities | ✗ Difficult to implement end-to-end DQ on a large scale |
| Ease of use | ✓ No-code interface, operational in one setup. | — Steep learning curve on complex platforms | ✗ Requires SQL, scripting, or developer resource |
| Purpose-built on-premise AI | ✓ Secure, non-generative AI for clean & match | ✗ Use third-party LLMs or ML models hosted in their cloud infrastructure | ✗ Limitations in building secure, in-house AI algorithms. Reliance on LLMs |
| Entity resolution | ✓ Full cross-system entity discovery with DQM | — Limited to simple deduplication only | ✗ Requires significant amount of investment in R&D |
| Matching method selection per column | ✓ AdaptiveMatch™ recommends a method and threshold for each column from its data type | — Method and threshold settings vary by vendor | ✗ Each method has to be coded, tuned and maintained internally |
| Matching algorithm library | ✓ WinPureFuzzy™, Jaro-Winkler, Double Metaphone, Levenshtein, Token Sort and exact matching | — Algorithm coverage depends on the vendor | ✗ Limited to the algorithms the team builds |
| Explainable match decisions | ✓ Match Explanation™ shows why each record joined its entity, and every decision is traceable in an audit file | — Often returns a score without the supporting evidence | ✗ Needs custom logging to trace each decision |
| Name and address matching libraries | ✓ Built-in libraries for address parts, company suffixes, titles and contact names | — Library coverage varies by vendor | ✗ Reference lists have to be built and maintained internally |
| Performance | ✓ 1M records in 3-5 minutes | — Estimated 20 minutes for 1M records | ✗ 36+ Hours for 1M records |
| Accuracy | ✓ 90-98% match accuracy | — 85 - 95% depending on API model | ✗ Varies, depending on script capability |
| Scalability | ✓ Handles millions of records without performance degradation | ✓ Scales well but cost increases exponentially with volume | ✗ Limited scalability. Requires additional infrastructure |
| Cost at Scale | ✓ Flat licensing (no per-record fees) | ✗ Metered pricing. API calls balloon with volume | ✗ Hidden labor and infrastructure costs |
The Data Problems Teams Can Solve with Clean&Match Enterprise
CAM addresses the recurring data problems that happen before or during data migrations, mergers & acquisitions, and downstream projects like analytics, and AI initiatives. Each use case below is a problem WinPure has helped customers solve efficiently without requiring additional resources.
CRM Deduplication for Migration
CAM lets teams extract, clean, and dedupe legacy CRM data with speed, accuracy, and an efficiency that cannot be achieved using traditional methods. A process that takes months can now be done in just a few weeks.
Mergers & Acquisitions
During mergers and acquisitions, data from multiple ERPs and CRMs has to be combined fast, but record structures, codes, and naming conventions make it impossible to line up. CAM allows for easy comparisons, structure mapping, and the consolidation of datasets within days instead of months.
Data Migration Preparation
CAM is the ideal platform for pre-migration data prep. It allows for the easy profiling and cleaning of source data before it enters a target system so migrations inherit verified master records rather than legacy data with errors.
Entity Resolution for Fraud Detection
Fraud and integrity teams deal with contact data that appears under different identities across systems. CAM analyses entities across the entire dataset and scores records by how likely they are to belong to the same entity.
Data Quality for AI and Analytics
AI models and analytics dashboards are only as reliable as the data behind them. WinPure Clean & Match standardises, deduplicates, and unifies your data before it reaches your models or dashboards.
Vendor Master Data Matching
Resolve multiple vendor lists in ERP, AP, and contract systems, where the same suppliers are held under different IDs, and addresses. CAM enables the easy standardisation & merging/matching of these records to get a single view of vendor data.
The Complete Data Quality Pipeline. Built Into One Platform.
Data Profiling
Analyse your dataset before you touch it. Surface gaps, inconsistencies, and anomalies from the start.
Data Cleansing
Standardise, correct, and transform records using 30+ built-in operations with AI-assisted data cleaning.
Data Matching
Find duplicates and similar records even where names and identifiers do not match exactly.
Data Deduplication
Identify and remove duplicate records across single or multiple data sources. Keep one clean, trusted version.
Data Verification
Validate addresses and contact fields against reference datasets before data moves downstream.
Automation Cycles
Schedule cleansing and matching tasks to run unattended on a recurring cycle without intervention.
4,000+ organisations rely on WinPure to resolve data quality challenges with:
Financial records, Patient identities, Contact data, Vendor data, and more.
Rated by data quality teams
Independent review platforms and customer feedback from teams using WinPure in production.
What Customers Ask When Choosing WinPure Clean&Match
Start a conversation about your data.
Migration deadline, deduplication project, or compliance requirement – we will help you understand what WinPure can deliver and how quickly.