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Description
DataMatch Enterprise (DME) is Data Ladder's entity resolution and data matching platform. It identifies records that refer to the same person, business, or entity across disconnected systems, then links and consolidates them into a single accurate record. Core functions include data profiling, standardization, matching, deduplication, and merging, supporting use cases such as Customer 360, KYC, fraud detection, and master data management.
The platform is available through a no-code visual interface for business users and a REST API for developers, allowing the same matching engine to be embedded in applications, data pipelines, or AI agent workflows. Match results are rule-based and traceable, so users can see the specific logic behind each linked record rather than a single opaque score.
Recent additions include entity graphs for visualizing connected records, live search for real-time matching, and Docker as a deployment option in addition to cloud and on-premises environments.
Independent benchmarking across 15 studies shows DME identifying 5 to 12% more matches than comparable tools, with fewer false positives and accuracy up to 99%. In a large-scale test, it processed 10 million records in 41 minutes.
Description
Companies that leverage data effectively utilize the Tilores API to establish a cohesive customer profile across all their various source systems. They develop real-time solutions that help mitigate risk, detect fraud, and provide tailored digital experiences, all while avoiding engineering complications. The challenges posed by inconsistent, incomplete, and outdated customer information hinder businesses in their efforts to align, strategize, and report accurately. For organizations to harness their customer data effectively, they must first standardize their schemas and integrate both new and historical data seamlessly. However, even after creating a centralized system with unified customer data, it often necessitates dedicated development efforts to enhance its utility. To streamline this process, it's essential for unified customer data to be synchronized back to the individual source systems of each department, turning every system into a distributed source of truth. This approach empowers businesses to manage risk more efficiently, detect fraud proactively, and enhance customer service. By transforming fragmented and isolated customer data into a comprehensive Customer 360 view, organizations can unlock new insights and drive better decision-making. Ultimately, this unification fosters a deeper understanding of customer behavior, enabling continuous improvement and innovation.
API Access
Has API
Yes
API Access
Has API
Yes
Integrations
GraphQL
No
HubSpot CRM
No
HubSpot Customer Platform
No
Salesforce
No
Snowflake
No
Integrations
GraphQL
Yes
HubSpot CRM
Yes
HubSpot Customer Platform
Yes
Salesforce
Yes
Snowflake
Yes
Pricing Details
starts at $10000/user per year
Free Trial
Yes
Free Version
No
Pricing Details
$2000/month
Pricing is based on Unified Customer Records (UCRs), which is the ultimate number of customers you have in Tilores, regardless of the number of data records.
Free Trial
No
Free Version
Yes
Deployment
Web-Based
Yes
On-Premises
No
iPhone App
No
iPad App
No
Android App
No
Windows
Yes
Mac
No
Linux
Yes
Chromebook
No
Deployment
Web-Based
Yes
On-Premises
No
iPhone App
No
iPad App
No
Android App
No
Windows
No
Mac
No
Linux
No
Chromebook
No
Customer Support
Business Hours
Yes
Live Rep (24/7)
Yes
Online Support
Yes
Customer Support
Business Hours
No
Live Rep (24/7)
Yes
Online Support
Yes
Types of Training
Training Docs
Yes
Webinars
Yes
Live Training (Online)
Yes
In Person
No
Types of Training
Training Docs
Yes
Webinars
No
Live Training (Online)
Yes
In Person
No
Vendor Details
Company Name
Data Ladder
Founded
2006
Country
United States
Website
dataladder.com
Vendor Details
Company Name
Tilores
Founded
2021
Country
Germany
Website
tilores.io
Product Features
Data Cleansing
Address/ZIP Code Cleaning
Yes
Charting
No
Data Consolidation / ETL
No
Data Mapping
Yes
Multi Data Format Support
Yes
Phone/Email Validation
Yes
Raw Data Ingestion
No
Sample Testing
No
Validation / Matching / Reconciliation
Yes
Data Quality
Address Validation
Yes
Data Deduplication
Yes
Data Discovery
No
Data Profililng
Yes
Master Data Management
No
Match & Merge
Yes
Metadata Management
No