Get to know us
About Data Ladder
- Pinpoint Matching Accuracy
- Real-Time Processing
- US & CA Address Verification
- ZIP+4 Level Geocoding
- User-Friendly Interface
- Hands-On Support
Our History
Delivering 15+ years’ worth of industry experience
Based out of Suffield, Connecticut, Data Ladder has relentlessly pursued to fulfill market needs for high-precision data quality and matching.
With over 15 years in product installations across government, financial services, education, and marketing verticals, Data Ladder’s matching prowess and rapid time-to-value has successfully delivered modern data cleansing, address verification, and entity resolution projects. This has enabled Data Ladder to serve large Fortune 500 companies such as Deloitte, GE and HP, government institutions such as USDA and Department of Transportation, and small to mid-sized startups.
Our Values
What do we care about?

Integrity
We pride ourselves on offering solutions based on expertise and industry experience.

Trust
Our teams work closely with clients to understand their unique challenges and deliver accordingly.

Scalability
We equip you with the solutions to accelerate your operations and long-term profitability

Employees
Our employees are the foundation of our success and the secret to achieving higher milestones.
There’s more
Where are we headed?
We firmly believe in the importance of keeping our ear close to the ground to learn from our experiences and continually refine and optimize our product offering to address current and upcoming data quality and matching challenges.
At Data Ladder, we aim to embed and harness the power of AI to tailor our solutions for complex matching environments without compromising simplicity in usability and time to value.
Customer Stories
Still unsure? See what others are saying…

It’s not just the software which works very well for us, but the focus and knowledge that Data Ladder brings to the table


Thanks to Data Ladder we successfully cleaned up and matched our internal sales file with new leads, greatly improving efficiency and sales.


We could not do these reports before. Now, DataMatch has become a main staple in my suite of tools that I work with!

Want to know more?
Check out DME resources

Merging Data from Multiple Sources – Challenges and Solutions
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Implementing a Vendor Matching Engine: A Practical Guide for Data Teams
Choosing the right vendor matching software is only half the challenge. The real value comes from how well you implement it. When done well, it

Buyer’s Guide: 10-Point Checklist for Evaluating Vendor Matching Software
A typical SMB[1] has an invoice duplication rate of 1.29%. That’s more than $12,000 in potential duplicate payments per month if each goes through. Now

Implementing a Vendor Matching Engine: A Practical Guide for Data Teams
Choosing the right vendor matching software is only half the challenge. The real value comes from how well you implement it. When done well, it

Buyer’s Guide: 10-Point Checklist for Evaluating Vendor Matching Software
A typical SMB[1] has an invoice duplication rate of 1.29%. That’s more than $12,000 in potential duplicate payments per month if each goes through. Now

Vendor Matching Software Explained: Solving Duplicate Vendor Records at Scale
In a poll by CFO Daily News, 86% of accounts payable (AP) professionals admitted their Master Vendor File (MVF) needed work. More than two-thirds said
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