Sold by: Interzoid
Deployed on AWS
AWS Free Tier
Quickly and easily elevate your data to new heights of quality, usability, and value. Leverage AI, Machine Learning, specialized algorithms, and comprehensive knowledge bases to enable the discovery, matching, and resolution of inconsistencies in company names, organization names, individual names, or addresses, including custom, self-defined data enrichment.
Overview
Is your data an inconsistent mess? Quickly and easily elevate your data to new heights of quality, usability, and value. Leverage Generative AI, Machine Learning, specialized algorithms, and comprehensive knowledge bases to normalize important data. The API platform, ready to use out of the box, enables the discovery, matching, and resolution of inconsistencies in company names, organization names, individual names, or addresses within your critical data assets. Also enrich your data to create new value from 30+ easy-to-integrate APIs, including custom, self-defined data enrichment.
Upon launching the EC2 instance, you can immediately begin to call the core API with a single data value, or the Connect API that processes entire datasets with native connections to databases such as AWS RDS, SQL Server, Snowflake, Postgres, MySQL and others - or text files such as CSVs, TSVs, Parquet, or Excel. Effortlessly generate data quality, data consistency, and data matching reports, and create new datasets with standardized, matched, merged, and far more normalized and useful data overall.
The instance can also be easily configured to be a server to other instances on your VPN or across the Cloud, enabling high levels of data quality in applications, business processes, ETL/ELT, or anywhere else usable data is critical to success.
Highlights
- Improve the value of your data assets through data matching and data normalization of alphanumeric data, including organization/company names, individual names, and addresses - uses Generative AI & Machine Learning behind the scenes.
- Many AI-powered data enrichment capabilities for B2B data, including the ability to generate custom, self-defined data enrichment APIs capturing the specific, specialized data you require.
- Connects via the Cloud to data tables on AWS RDS, Postgres, MySQL, Snowflake, SQL Server, and many more, as well as text file datasets including CSV, TSV, Excel, and Parquet.
Details
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Delivery method
Delivery option
64-bit (x86) Amazon Machine Image (AMI)
Latest version
Operating system
AmazonLinux 2023
Deployed on AWS
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Features and programs
Financing for AWS Marketplace purchases
AWS Marketplace now accepts line of credit payments through the PNC Vendor Finance program. This program is available to select AWS customers in the US, excluding NV, NC, ND, TN, & VT.
Pricing
Pricing is based on actual usage, with charges varying according to how much you consume. Subscriptions have no end date and may be canceled any time.
Additional AWS infrastructure costs may apply. Use the AWS Pricing Calculator to estimate your infrastructure costs.
If you are an AWS Free Tier customer with a free plan, you are eligible to subscribe to this offer. You can use free credits to cover the cost of eligible AWS infrastructure. See AWS Free Tier for more details. If you created an AWS account before July 15th, 2025, and qualify for the Legacy AWS Free Tier, Amazon EC2 charges for Micro instances are free for up to 750 hours per month. See Legacy AWS Free Tier for more details.
Dimension | Cost/hour |
|---|---|
t3.small Recommended | $0.17 |
t2.micro | $0.17 |
t3.micro | $0.17 |
i4i.12xlarge | $0.27 |
i4i.24xlarge | $0.27 |
i4i.metal | $0.27 |
t2.small | $0.17 |
i4i.32xlarge | $0.27 |
i4i.xlarge | $0.27 |
t2.xlarge | $0.17 |
Dimensions summary
You pay by the hour for the EC2 instance type that runs this platform. Pricing scales with instance size and family, not with software features. The t2, t3, and t3a families offer general-purpose sizes from micro up through 2xlarge. The i4i family covers storage-optimized sizes from large up through 32xlarge and metal. Larger instances carry higher hourly rates, so you choose based on the compute and memory your data workloads need. All sizes run the same platform; only the underlying hardware capacity and hourly cost differ.
Top-of-mind questions for buyers
You pay the hourly software rate only while the instance runs. Stopped instances do not accrue software charges. Fully powered-off instances stop the hourly meter. Note that underlying AWS storage tied to a stopped instance may still incur separate AWS fees, but the software rate meters running time only.
Each rate maps to one running instance of that specific size. The t2, t3, and t3a families provide general-purpose compute and memory. The i4i family is storage-optimized for data-heavy workloads. You pick the size that fits your throughput, and cost rises with capacity.
The platform runs data matching, standardization, enrichment, and verification through one engine. All these functions run on the same instance you pay for hourly. Your bill reflects the instance size and running time, not which functions you use. Choose the size that matches your workload volume.
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Delivery details
64-bit (x86) Amazon Machine Image (AMI)
Amazon Machine Image (AMI)
An AMI is a virtual image that provides the information required to launch an instance. Amazon EC2 (Elastic Compute Cloud) instances are virtual servers on which you can run your applications and workloads, offering varying combinations of CPU, memory, storage, and networking resources. You can launch as many instances from as many different AMIs as you need.
Version release notes
This AWS instance enables you to quickly and easily identify matches of inconsistent, duplicate/redundant, and otherwise non-normalized data of organization/company names, individual names, and addresses using an AI model built into the application platform.
There are two ways to do this, an API for a single data entity such as an organization name or an individual name, and also the command-line driven CONNECT application for analyzing a text file or database table, generating "match reports", as well as enabling match index tables to be created for more advanced purposes, such as SQL JOINS that overcome data inconsistency issues or matching/appending data between two datasets to get signficantly higher match rates, and also prevent redundant, non-normalized data to enter a database at the point of data collection.
Using the API, a similarity key is generated for the entity in the parameter of the call. Data that is "similar" will generate the same similarity key.
Examples:
GE -> wAR3laPfUVvB784_iH0cw7aQbKhr26sophlZ4z7iqtM General Electric -> wAR3laPfUVvB784_iH0cw7aQbKhr26sophlZ4z7iqtM
AMAZON.COM -> EP88bx0VFDaIh-cOt86c8pOJ6lNkb_TWiKFpmMKXakY Amazon Inc. -> EP88bx0VFDaIh-cOt86c8pOJ6lNkb_TWiKFpmMKXakY
This can be achieved by calling the API that is running on this instance. For example, you can use cURL to make the API call:
$ curl 'http://localhost:8950/getcompanymatchadvanced?company=ibm&algorithm=wide '
Use the generated similarity key, rather than the actual data itself, to match, normalize, and/or sort company name data by similarity. This avoids the problems of data inconsistency, misspellings, and name variations when matching within a single dataset, and can also help matching across datasets or for more advanced searching.
Calling this API enables the capability to be built into an infinite number of applications, processes, pipelines, and more.
For Connect, which supports analysis, processing, and reporting of full datasets, the following data sources are supported.
File Types (requires pathname in the "connection" parameter when calling the API, either locally with full path or at a URL address)
CSV TSV Excel Parquet
Databases (local, on the network, or in the Cloud - requires connection string in the "connection" parameter)
AWS RDS (Postgres & MySQL) AWS Aurora (Postgres & MySQL) Postgres MySQL Azure SQL SQL Server Snowflake Databricks Google Cloud SQL (Postgres & MySQL) SkySQL (MySQL) CockroachDB
All database connections are native connectivity. The source can either be a table within the database or a logical view.
For more documentation, usage, and getting starting info, see the following starting point directory upon launch of the instance: $ /home/ec2-user/interzoid
Additional details
Usage instructions
This AWS instance enables you to quickly and easily identify matches of inconsistent, duplicate/redundant, and otherwise non-normalized data of organization/company names, individual names, and addresses using an AI model built into the application platform.
There are two ways to do this, an API for a single data entity such as an organization name or an individual name, and also the command-line driven CONNECT application for analyzing a text file or database table, generating "match reports", as well as enabling match index tables to be created for more advanced purposes, such as SQL JOINS that overcome data inconsistency issues or matching/appending data between two datasets to get signficantly higher match rates, and also prevent redundant, non-normalized data to enter a database at the point of data collection.
Using the API, a similarity key is generated for the entity in the parameter of the call. Data that is "similar" will generate the same similarity key.
Examples:
GE -> wAR3laPfUVvB784_iH0cw7aQbKhr26sophlZ4z7iqtM General Electric -> wAR3laPfUVvB784_iH0cw7aQbKhr26sophlZ4z7iqtM
AMAZON.COM -> EP88bx0VFDaIh-cOt86c8pOJ6lNkb_TWiKFpmMKXakY Amazon Inc. -> EP88bx0VFDaIh-cOt86c8pOJ6lNkb_TWiKFpmMKXakY
This can be achieved by calling the API that is running on this instance. For example, you can use cURL to make the API call:
$ curl 'http://localhost:8950/getcompanymatchadvanced?company=ibm&algorithm=wide '
Use the generated similarity key, rather than the actual data itself, to match, normalize, and/or sort company name data by similarity. This avoids the problems of data inconsistency, misspellings, and name variations when matching within a single dataset, and can also help matching across datasets or for more advanced searching.
Calling this API enables the capability to be built into an infinite number of applications, processes, pipelines, and more.
For Connect, which supports analysis, processing, and reporting of full datasets, the following data sources are supported.
File Types (requires pathname in the "connection" parameter when calling the API, either locally with full path or at a URL address)
CSV TSV Excel Parquet
Databases (local, on the network, or in the Cloud - requires connection string in the "connection" parameter)
AWS RDS (Postgres & MySQL) AWS Aurora (Postgres & MySQL) Postgres MySQL Azure SQL SQL Server Snowflake Databricks Google Cloud SQL (Postgres & MySQL) SkySQL (MySQL) CockroachDB
All database connections are native connectivity. The source can either be a table within the database or a logical view.
For more documentation, usage, and getting starting info, see the following starting point directory upon launch of the instance: $ /home/ec2-user/interzoid
Resources
Vendor resources
Support
Vendor support
Online support: for assistance, questions, or feed back contact support@interzoid.com
AWS infrastructure support
AWS Support is a one-on-one, fast-response support channel that is staffed 24x7x365 with experienced and technical support engineers. The service helps customers of all sizes and technical abilities to successfully utilize the products and features provided by Amazon Web Services.
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This API uses AI models, Machine Learning, specialized algorithms, and extensive knowledge bases to generate a similarity key for a company name used to match with other similar company name data to identify data inconsistency. The API can be called to generate a key for single records for matching with other records, or can be used to process, match, and normalize entire datasets via either API or the Web GUI wizard.
This API uses AI models, Machine Learning, specialized algorithms, and extensive knowledge bases to generate a similarity key for an individual name to be used to match with other similar individual name data to identify data inconsistency, misspellings, name variations, nicknames, and more. The API can be called to generate a key for single records for matching with other records, or can be used to process, match, and normalize entire datasets via either API or the Web GUI wizard.
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