AI Steward

Save Money, Increase Accuracy

Problem Definition: Tasks / Suspects

By investing in an MDM / EMPI / MPI platform you recognize the need to match your data accurately and completely but all of these platforms fail to match the entire population of data and leave the unresolved matches as potential duplicates (also known as tasks, suspects, worklists, reviews etc...) for humans to work manually.

MDM/EMPI Implementation's are configured to raise tasks / suspects

Tasks / Suspects are resolved by data stewards

Human Data Remediation


Time consuming

Requires Training

Can be inconsistent between stewards

Confined to workdays/hours (M-F, 8-hour days)

Large Backlogs are difficult to overcome (100k – millions)

Adding sources will spike backlogs

Meanwhile new Tasks/Suspects generation continues

Tasks/Suspects typically account for 10-15% of the population – affects match rate


AI Steward Implementation Process:
Production in Weeks

Implementation done in weeks (unlike an MDM implementation)
EntityWise does most of the heavy lifting

Train the Model

(no cost POC)

3 people: approximately 8 hour
End of POC-Functioning AI Steward

Deploy & Test in

Fast/slow as desired
Test n number of Tasks

Deploy in Production

Fast/slow as desired
Solve 1 task to n Tasks

Our Approach

There are 3 steps to resolving any potential duplicate on any platform:

Get the Potential Duplicate out of the Platform
Examine and Make Decision on the records
Put the Answer back into the Platform

This is true whether you are doing this through a team of people or through an automated process.​
AI Steward works in conjunction with your existing platform to help resolve these tasks.

How do we engage?

We can engage:

As a one-time project to eliminate a backlog and allow your data team to  catchup (a services engagement)

As a subscription where we continuously resolve an ongoing workload 7x24x365

As a file based engagement where we receive a file of potential duplicates and return them resolved.



How AI Steward Works

Why are Data Stewards able to resolve tasks when the MDM/EMPI/MPI cannot.

Data Stewards examine a pair of records differently than a match platform that employs a Deterministic or Probabilistic match approach.

A human looks at each of the attributes individually and makes assessments that weigh the balance of which attributes are more important in addition to how well the values match and strike a balance that is not possible from traditional approaches.
Machine Learning, what AI Steward uses, approaches the decision making process exactly the same way a human does.
It learns from the data steward and can see what the data steward see's when making the decision.

Our Solution

AI Steward can resolve tasks on platforms or file based projects (e.g. MPI Cleaups) at 20x the speed of a data team and at 1/3 the cost. And as a machine learning technology it will learn the match behavior from your team and mimic their accuracy while saving money.

To see an AI Steward
customer example click here

To watch a webinar about

AI Steward click here

About Us


Since we've specialized in record match technology and have created a machine learning approach to matching records that is more accurate than the traditional platforms.

Why us


407 Radam Lane, A-100
Austin, Tx 78745


©2010 by EntityWise.