Clean, Match, and Resolve Your Data. Inside Your Infrastructure.
WinPure Clean & Match processes data through a structured pipeline, from profiling to cleaning and creating master records within your environment. Supported by localised AI built to reduce manual data cleaning while improving match accuracy. The result: golden records you can trust.

A Structured Data Quality Pipeline for Teams
WinPure's data quality platform is built around interconnected modules, each handling a defined state of the data quality process. Together, these modules form a pipeline that takes messy data and produces records your systems can rely on. Run one stage or the full pipeline depending on what your data needs. You remain in full control of the process.
See How the Platform Works →Data Profiling & Quality Review
Understand the condition of your data before you start operational tasks like matching, cleansing, or migration work. This module gives teams a clear view of data quality issues across the dataset so teams can prioritise effort and make better operational decisions earlier in the process.

Data Cleansing & Transformation
Cleaning messy data is a complex process made efficient with CleanMatrix, WinPure’s no-code environment for field-level data standardisation. Users can clean records at field-level, transforming inconsistent source data to cleaner, more reliable datasets that support reporting, migration, and day-to-day operations.

Fuzzy & Exact Data Matching
Duplicate and disparate identities are difficult to resolve using traditional exact-match rules alone. WinPure’s data matching module combines deterministic and fuzzy matching logic to identify records that refer to the same entity. Users can set independent match thresholds, field weights, and review confidence scores to make decisions with clear, auditable criteria.

Entity Resolution & Golden Record Creation
WinPure's entity resolution engine evaluates records holistically, accounting for cultural variations in name order, address formats, and field-level transpositions that fuzzy matching misses. It reads the combination of attributes across the entire record and surfaces relationships across datasets that are not visible through field-level comparison alone.

Address Data Verification
Reduce the risks of working with unverified address data. WinPure’s data verification module validates and standardises location data against official postal databases such as including USPS CASS-certified address validation (United States), Royal Mail (UK), Canada Post, and Australia Post. It resolves partial entries, corrects formatting inconsistencies, and flags records where verification cannot be confirmed.

Purpose Built AI for Secure Data Quality Operations.

Reduce Manual Data Cleanup Effort by 80%
Clean AI™ recommends the right data type and cleaning configuration for each field in your dataset, while still letting you maintain control. You can modify configurations so it matches your specific data requirements. Once configured, standardise, clean and transform your entire dataset in just one click. This gives you intelligent automation without losing control and reducing cleanup time by 80%. Every transformation is auditable & reversible.
Learn about CleanAI →Entity Resolution That Handles Real-World Inconsistency.
Match AI™ is built for matching at scale where traditional fuzzy and deterministic logic breaks down. At millions of records, the AI reads each record as a complete unit rather than as an isolated field, surfacing relationships across datasets that deterministic and fuzzy matching miss, including transposed names, regional address formats, and culturally diverse identifiers.
Learn about MatchAI →

Build Master Records with Confidence
SmartMaster AI™ automatically identifies the best record within each matched group and designates it as the golden record based on a scoring decision principle. It evaluates completeness, recency, and data quality across the group and selects the highest-scoring record. Users who prefer control can define custom scoring criteria. Either way, it helps reduce the manual review of thousands of duplicate groups.
Learn about SmartMaster AI™ →
How Much is Bad Data Costing You? Find Out Instantly.
Answer a few simple questions about your data volume, quality issues, and current approach. Get a custom report showing what bad data is costing you and how much you could save with WinPure.
Why Data Teams Choose WinPure Over Cloud Tools and Manual Processes
| 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 |
| 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 | — Manual infrastructure required to scale |
| Cost at Scale | ✓ Flat licensing (no per-record fees) | ✓ Metered pricing. API calls balloon with volume | ✗ Hidden labor and infrastructure costs |
“I’m not sure if I didn’t have a tool like WinPure that I could even do statistical survey jobs. Comparing files pairwise to find duplicates across 10,000 outlets, across five different sources, none of them with consistent identifiers, that is not something a survey tool can do. That is not something a spreadsheet tool can do. I would not be willing to do that by hand. WinPure has made something possible that most people doing survey work would not even attempt.”
CamBright CEO, Larry Campbell
Built for Sectors Where Data Security is a Non-Negotiable





What Customers Achieve
Outcomes from production deployments - real data, real environments, measurable results.
Legacy housing data cleaned and deduplicated. Migration completed on schedule.
Read case study →Vaccination records consolidated without cloud transmission. Compliant reporting delivered.
Read case study →Manual VLOOKUP comparisons replaced with automated fuzzy match logic. Lead generation efficiency improved by over 50%.
Read case study →Manual verification replaced with automated multi-list matching using CleanMatrix and WinPure's matching engine.
Read case study →"One of the great qualities about using WinPure has been its speed and simplicity. We can match a large dataset of 21,000 properties against a 100,000-record source in under 30 seconds. A software tool we simply cannot recommend enough."
Alan Kirk - Information Manager, Luton Borough Council
Your Data Quality Programme, Fully Managed by Our Team.
Some data challenges are simply too large and complex to handle alongside everything else you have running. This is where you can benefit from WinPure's fully managed service where we work with your team to profile, clean, transform and consolidate your data based on the outcomes you define. You can work with us in a one-off project or as an on-going programme, all within your infrastructure.
Learn more →
Questions We Hear from Data Teams Before They Buy
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.

