Profile your data. Match your records. Build your golden record.

Your customer, vendor, and product records are scattered across systems with misspelled names, inconsistent formats, and conflicting identifiers. MatchLogic profiles every field in your dataset, standardizes the values through a visual pipeline, matches records using proprietary fuzzy algorithms, and produces a deduplicated golden record your entire organization can trust. 96% accuracy. Results on day one.

Seven steps from messy data to golden record. One platform.

MatchLogic walks your data through a visual pipeline. Each step builds on the last. You can review, adjust, and re-run at any point without starting over. And once you have it right, the Workflow Scheduler automates the entire process going forward.

Import

Connect to virtually any data source: databases (SQL Server, Oracle, Teradata, MySQL), CRMs (Salesforce), cloud platforms, flat files (CSV, Excel, tab-delimited), JSON, and more through native connectors or ODBC. Pull records from multiple sources into a single project. No reformatting required. MatchLogic handles the schema differences.

Profile

Before you clean or match anything, you need to understand what you are working with. MatchLogic scans every column and generates a detailed analysis: data type detection, field length distribution, completeness and null rates, distinct value counts, character composition, entropy scores, anomaly detection, min/max/median/mode values, and semantic classification (is this column a name, an address, an identifier, a currency amount, a date?).

The profiler also runs the Wordsmith tool, which shows the most frequently occurring words in any column and their counts. This is how you spot standardization opportunities before writing a single rule: you see that 'LLC' appears 4,200 times, 'L.L.C.' appears 310 times, and 'Limited Liability Company' appears 47 times. You know exactly what to clean.
Profiling tells you which fields are reliable enough to use as match criteria and which are too sparse or inconsistent. Building match rules on a field that is only 40% populated produces bad results. The profiler prevents that mistake before you make it.

Cleanse and Standardize

MatchLogic provides a visual, flow-based cleansing pipeline where you chain transformations together and see the effect on your data in real time. No code. No SQL. You build the cleaning logic visually and watch the results update as you go.

Available transformations include: case conversion, punctuation removal, non-printable character stripping, abbreviation expansion and contraction (CA to California, Mfg. to Manufacturing), field parsing (split a full address into street, city, state, ZIP; split a full name into first, middle, last), field merging, find-and-replace with regex support, number cleansing for phone and ID fields, and cross-column operations. The platform ships with over 300,000 built-in standardization rules for name, address, and phone data.

Cleansing happens in memory. Your source data is never modified. Every transformation is saved in a reusable project configuration, so you can re-run the exact same cleansing pipeline next month when new data arrives.

Match

Configure which fields to compare, select from multiple matching algorithms (exact, phonetic, fuzzy, edit distance, token-based, ML-enhanced), assign weights to each field, and set confidence thresholds. MatchLogic supports cross-column matching for situations where data entry errors put values in the wrong field.

The matching engine processes millions of records in-memory at speeds rated faster than IBM and SAS in independent benchmark studies. Proprietary algorithms refined over 19 years catch the variations that cause the most missed matches: nickname-to-formal name conversions (Bill to William), phonetic similarities (Stephen to Steven, Kathy to Cathy), abbreviation differences (J&J to Johnson & Johnson), transposed characters, and format inconsistencies across systems.
In head-to-head comparisons across 15 independent studies with datasets ranging from 80,000 to 8 million records, MatchLogic consistently found at least 10% more true matches than competing commercial solutions, with the fewest false positives.

Merge and Build the Golden Record

Once matches are identified, you review them at each confidence level and decide which to accept. Then you design survivorship rules: when two records for the same entity have conflicting values, which value wins? MatchLogic lets you define this logic by field, by source priority, by completeness, or by recency.

For example: always take the email address from Salesforce because it is most current, but take the mailing address from the billing system because it is verified. Take whichever phone number was updated most recently. Take the most complete company name across all sources. These survivorship rules execute automatically across every matched group to produce a single golden record per entity: the most accurate, most complete version of that customer, vendor, or product record that your organization has.
The golden record is not a guess. It is assembled from the best attributes across every source, governed by rules your team defines and controls.

Export

Export the profiled, cleaned, matched, and merged data back to any destination: flat files, databases, CRMs, or downstream systems. Export at any stage of the pipeline. Send the profiling results to your data governance team. Send the cleaned data to your analytics platform. Send the golden records to your CRM or ERP. You choose the format and the destination.

Automate

Once you have configured a project (import sources, profiling rules, cleansing pipeline, match configuration, survivorship logic, and export destination), you can automate the entire workflow with MatchLogic's built-in Workflow Scheduler. Set a project to run on a fixed schedule (daily, weekly, monthly), trigger it at a specific date and time, or configure it to execute automatically whenever a connected data source updates.

This turns a one-time cleanup into a continuous data quality process. New records enter your CRM every day. New vendors get added to your ERP every week. The Scheduler ensures that every new record is profiled, cleaned, matched, and merged into your golden record without anyone clicking a button. A calendar view gives your team a summary of all upcoming and completed automation runs across every project.
Data quality degrades the moment you stop paying attention to it. The Scheduler makes sure you never stop.

Know your data before you try to fix it.

Most data quality problems start because someone built matching rules on assumptions instead of evidence. MatchLogic's profiler eliminates guesswork by showing you exactly what every column contains, how complete it is, and whether it is reliable enough to use for matching.

Completeness and Null Analysis

See the fill rate for every column instantly. If SSN is only 60% populated, you know not to build a required match rule on it. If email is 98% populated, you know it is a strong candidate for primary matching.

Semantic Classification

MatchLogic automatically classifies what each column contains: full name, address, identifier, currency, measurement, date, timestamp, duration. This saves time during match configuration because you immediately know which columns are comparable across sources.

Entropy and Anomaly Detection

Entropy scores reveal how much variation a column contains. Low entropy on a name field suggests most values are the same (a data problem). Anomaly detection flags outliers and extreme values that could cause false matches or indicate data entry errors.

Completeness and Null Analysis

The Wordsmith tool shows the most repeated words in any column with their exact counts. Run it on a Company Name column and instantly see every variation of 'LLC', 'Inc', 'Corp', 'Company', 'Holdings' across your dataset. This is how you build targeted standardization rules in minutes instead of days.

Rated faster and more accurate than IBM and SAS.

Speed and accuracy are not marketing claims. They are the results of independent benchmark studies conducted across 15 product comparisons with university, government, and private-sector datasets ranging from 80,000 to 8 million records.

96%

Average match accuracy across datasets

10%+

More true matches found vs. competing commercial tools

Fewest

False positives across all independent benchmark studies

In-memory processing at enterprise scale

MatchLogic processes millions of records in memory. You load your data, run the pipeline, review the results, adjust, and re-run without writing to disk between steps. This is how a single analyst can deduplicate an 8-million-record vendor master in an afternoon instead of a week.

Proprietary algorithms refined over 19 years

Matching finds the duplicates. Survivorship builds the golden record.

A golden record is the single, most complete and accurate version of an entity assembled from the best attributes across every source system. It is the answer to the question every data team asks: 'Which version of this customer record is correct?' The answer is usually: none of them individually. The golden record is built from pieces of all of them.

MatchLogic's survivorship engine lets you define field-level rules that control how the golden record is assembled. Prioritize values by source system trust, record completeness, recency of update, or custom logic. When two records for 'Robert Smith' exist and one has a verified mailing address while the other has a current email, the golden record takes the address from one and the email from the other. No manual intervention. No guessing. Rules you define, applied automatically across every matched group in your dataset.

The golden record is exportable to any downstream system: your CRM, ERP, data warehouse, analytics platform, or MDM solution. MatchLogic builds the golden record. Your systems consume it.

Deploy the way your organization requires.

Healthcare systems cannot send patient data to the cloud. Government agencies cannot transmit citizen records outside their network. MatchLogic meets your security and compliance requirements with three deployment options.

Desktop

Install locally. Run profiling, cleansing, and matching projects on your machine. No data leaves your laptop. Ideal for individual analysts and project-based work. Full pipeline. Full accuracy. Operational in minutes.

Server

Install on your infrastructure. Team access with multiple user licenses. Schedule recurring pipeline runs with the built-in Workflow Scheduler. Automate matching to trigger when source data updates. Calendar view for managing all scheduled tasks.

API

RESTful API exposes every platform feature: profiling, cleansing, matching, deduplication, and merge operations. Embed directly into your data pipelines and applications. Acts as a real-time data quality firewall between your databases and data entry forms.

One matching engine. Every data quality problem your team faces.

The same platform that deduplicates your CRM also matches patient records, resolves citizen identities, cleans vendor masters, and reconciles data during acquisitions.

Customer Deduplication
Merge duplicate customer records across CRMs, ERPs, and billing systems into a single golden record.
Read more
Vendor Matching
Eliminate duplicate vendor records, catch duplicate payments, and consolidate spend visibility across procurement.
Read more
Mergers & Acquisitions
Match records across acquiring and acquired companies. Quantify overlap and build clean combined datasets.
Read more
Patient Record Matching
Resolve patient identities across EMRs, billing, and lab systems. On-premises deployment for HIPAA compliance.
Read more
Compliance & Audit
Full audit trail for every match decision. Cross-reference records against watchlists. Documented, exportable, reviewable.
Read more
Systems Modernization
Clean and deduplicate legacy data before migrating to a new platform. Start the new system with accurate records.
Read more

19 years of matching. Zero black boxes.

Accurate from day one. No training period.

Most ML-based matching platforms require weeks of model training with labeled data before they produce reliable results. MatchLogic uses pre-built, tunable algorithms refined across thousands of real datasets since 2006. Load your data, configure your rules, and see accurate matches on your first run.

Every match decision is fully transparent.

When MatchLogic links two records, it shows exactly which fields contributed, which algorithms were applied, and what the field-level scores were. 'Name: 93%. Address: 87%. Tax ID: 100%.' If a match looks wrong, you adjust the threshold and re-run. No black box ML. No unexplainable outputs.

90% less expensive than Informatica, IBM, or Ataccama.

Enterprise MDM platforms charge six figures for capabilities most organizations never use: data lineage, catalog, governance workflows, AI agents. MatchLogic does one thing extremely well: data matching. You get enterprise-grade accuracy without paying for an enterprise-grade platform you do not need.

A dedicated matching partner, not a support portal.

Every customer gets a dedicated account manager and product specialist. Free training sessions whenever you need them. Direct access to the team that built the algorithms. When you hit a tricky dataset, you call someone who has solved that exact problem before. We have operated this way since 2006.

See your data matched in under 10 minutes.

Every demo starts with your data. Bring a sample file and we will walk through profiling, cleansing, matching, and golden record assembly live. You will see how the platform handles your specific data quality challenges. No slide decks. No hypothetical scenarios.

Schedule a Demo

Frequently Asked Questions

What does entity resolution reveal about my data?

Entity resolution shows exactly how your records connect to real-world entities. You'll see which fragments belong together, where identity variations hide, and how records cluster. Visual entity maps highlight relationships across all your systems before any data changes, giving you full control over identity unification.

How fast can matchlogic resolve large datasets?

matchlogic resolves 10 million records in under 8 minutes, linking fragments and clustering related entities at scale. The engine analyzes every field, calculates match confidence, groups related records, and generates visual entity maps without performance issues.

What identity variations does resolution typically find?

Most companies discover 30-40% entity fragmentation they never knew existed. Resolution catches nicknames hiding as formal names, typos creating false duplicates, and company abbreviations splitting single entities. These variations cost real money in duplicate processes.

How does entity resolution differ from deduplication?

Deduplication removes exact duplicates within one dataset. Entity resolution links related records across multiple systems to real-world entities - even when names, formats, and identifiers vary. You get unified profiles showing the complete picture of each customer, vendor, or contact.

Can I preview resolution results before committing?

Yes - see exactly how records will cluster before any data changes. Visual previews show entity groups with confidence scores highlighted. Review field-by-field evidence, adjust matching rules, and approve results. Nothing changes until you confirm the resolution output.

Can entity resolution help with compliance?

Entity resolution creates unified customer identities for GDPR right-to-access requests, KYC verification, and AML screening. Track which records belong to each entity, prove proper identity management for audits, and maintain evidence trails showing how identities were resolved.