Features of Our Tool

RecordLinker is a pluggable Data Normalization solution that uses advanced Machine Learning. We focus on solving one problem – connecting disparate records across systems – and we do it really, really with added quality-of-life value.

Multi-Level Domains (Tree Structures)

Understand dependencies between your items thanks to a clean visual representation in a hierarchy on a tree. Immediately see important parent and child relationships without having to check dozens of individual items one by one.


Input Format Neutral

You are ready to work regardless of the input system and data formats. RecordLinker recognizes a variety of input file structures from different systems, and presents the input data in a consistent UI. Data specialists can get straight to conversion work without wasting time on analyzing an unknown data file.


Augmentation with Industry‑Specific Data

Enhance your process and investigations with AM Best, NAIC, or any recognized universal industry data source of your choice. We can visually overlay your data on top of these sources. Access your trusted industry reference directly in RecordLinker.


Integration with Your Core System

Enjoy lightweight integrations with top core systems e.g. Vertafore and Applied Systems suites as well as DuckCreek or Guidewire. Pull data via API and sync with your tools. RecordLinker can also work completely standalone with as little as Excel imports.


Upload/Download via Excel or API

You can choose to have the mapped data delivered via API, or download them as an Excel file. Just plug them into your existing ETL or in-house system for migration or conversion. Get ready to say goodbye to messy, fragmented data!


Collaboration Options

You may label items, leave them for later to investigate, or assign them to others. Based on the labels, RecordLinker can notify the responsible person about problematic items.

Automatic Retraining

RecordLinker’s ML model can get retrained with a single click based on historical or newly completed conversions. More than that, whenever you decide to change something in your golden record set, RecordLinker will adjust and remap any initially matched records to their new or updated destination.


Manually Adjust Mappings as Needed

Manually review any mapped records our system flags when it isn’t sure about a input-canonical record pair. RecordLinker ML model takes these changes into account, getting retrained to constantly improve accuracy and increase the percent of auto-completed mappings.

We Don't Need Access to Your DB!

By design, RecordLinker operates only on the data sent to it (by you or your team). We don’t need access into your databases, which means you have complete control on what kinds of data RecordLinker sees. Which makes security review so much easier for your team to accomplish. No new ingress firewall rules nor opening up your databases!

Consistent UI and Use Cases

Mapping records during system migration? About to standardize a data mart? Need to check a book of business to assess data governance quality for your M&A? RecordLinker behaves exactly the same across different use cases. Any person experienced with our tool can handle various tasks, using a consistent, friendly user interface.

Ready to Handle Various Types of Data

RecordLinker can be pre-configured for different data types incl. companies, products, mortgage type names, car makes, spare parts etc.


Audit Trail

Understand how certain records changed and who made the specific change. Ensure team improvement and an easy way to investigate human choices at their source.


Data Quality Checks for Your Golden Records

RecordLinker can perform a data quality check on your golden record set. You’ll get a summary of misspellings, incorrect NAICs, outdated company names, and suggestions on how to fix them. It also allows access control for approval workflows.


Knowledge Base

Build a searchable knowledge base by attaching images (OCR) and leaving comments. This way your new team members can learn why certain connections are meant to be what they are.

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