Using Generative API for Better Data Quality in Databases and Files

Deploy Interzoid's AI-Powered Data Matching as an AWS EC2 Instance

Analyze, process, and generate data matching and anomaly reports for databases and datasets on EC2. Access local, network, or Cloud databases and files directly from the instance. Launch and deploy in a single-click on your choice of EC2 instance type, and to any AWS region in the world.

Improve Data Quality and Usability

Inconsistent, redundant, and otherwise poor data quality can exist anywhere, including Cloud data platforms, databases, text files, and Excel files, both on the Cloud or locally. Our Data Connectivity and API products deployed to a single AWS EC2 instance can help identify and resolve these issues quickly and easily, regardless of how and where the data resides. Get up and running in minutes and achieve better quality and usability immediately.

Deploy as an AWS EC2 instance

Interzoid's entire Generative AI-powered matching system can be deployed to an instance of AWS EC2, enabling full-control, unlimited access, and maximum flexibility - all within your own Virtual Private Cloud (VPC). Simply select the Interzoid Matching Amazon Machine Image (AMI)from the AWS Marketplace or click the link below to launch to your AWS account. No commitment: pay-as-you-go.

Any Data Anywhere

Our native connectors enable the processing of entire data tables and datasets within the most popular databases and file types, including generating data match reports, appending Similarity Keys for matching purposes to existing files and columns, creating new tables within databases to aid in data matching, and much more. All of this can be executed via API and/or the Linux command line. Data can reside locally, within your VPC, or across the Cloud.

Visit the AWS Marketplace product page: learn more & launch
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There are two components to the platform once deployed. One is the set of Similarity Key HTTP APIs, the algorithmic core of the matching platform. These APIs can be called directly one record at a time for custom use as well as in a server capacity to other applications. The APIs can be integrated with a single line of code.

The platform also includes the Cloud Data Connect application with the Similarity Key APIs pre-integrated, enabling batch-like analysis, matching, data inconsistency identification, and Similarity Key appending of entire datasets and database tables.

- Get up and running immediately with an EC2 instance launch from an Amazon Machine Image (AMI) on the AWS Marketplace.

- Our Generative AI-powered specialized language model, SmartMatchAI, is built into the product. This is the core of our analytic capabilities.

- Deploy to multiple instance types, from T-series general purpose instances of all sizes, to I-series instances of various CPU and storage configurations for maximum performance and I/O throughput.

- Deploy to any region supported by AWS across the globe.

- All data analysis contained within the instance, no external Cloud access required unless desired to analyze external data tables.

- Unlimited usage within the deployed instance (no API Keys required).

- Can deploy as a server within a VPC by opening up port access to the instance for http/https calls, both for single-record and entire/dataset batch access, for application integration, data pipelines, automated analysis, and more.

- No commitment required: Pay-as-you-go through the AWS Marketplace on your existing AWS account.

Scroll down for answers to FAQs.

Maximum Performance, Total Flexibility, and Unlimited Access on your own AWS EC2 Instance:

AI-Powered Data Quality and Matching on AWS EC2

Examples of Inconsistent Data

AWS Linux Cloud Instance of Match Reports
* For SQL databases use the appropriate connection string (see docs).

Frequently asked Questions (FAQs)

How does Generative AI enhance data matching and cleansing in this product?
We use Generative AI to dramatically enhance our data matching and "inconsistently represented data" identification capabilities. We've built a specialized language model into the product, transparent to the user, that enables these advanced capabilities. It is used by our matching algorithms during the analysis and processing of data.

What are the requirements to deploy the Interzoid Matching Amazon Machine Image (AMI) on an AWS EC2 instance?
You only need an existing Amazon AWS account. Within your account, from the AWS Marketplace you simply launch the Machine Image to an EC2 instance, and you're off and running.

Can I deploy Interzoid's AI-Powered Data Matching Machine Image for EC2 to any AWS region?
Yes. There are no geographic restrictions.

Is there a commitment or long-term contract required to use this product?
No. It is billed inexpensively by the hour, so you can use it for only one hour to run data quality anomaly and match reports against datasets if desired, and/or to test drive it with existing data or datasets. Usage cost only occurs while the deployed EC2 instance is running.

How is billing handled for the use of this AWS Marketplace product?
The hours of usage are simply part of your normal, monthly AWS bill. There is no separate billing.

What types of data sources can Interzoid's AI-Powered Data Matching process?
You can quickly connect to SQL-based database tables using our built-in native drivers to begin analysis and matching, including AWS RDS, AWS Aurora, AWS Redshift, Snowflake, Databricks, Azure SQL, Google SQL, Postgres, MySQL, and others. You can also analyze and process data from flat files, including CSVs, TSVs, Parquet, and Microsoft Excel.

Can I analyze data that resides outside my Virtual Private Cloud (VPC)?
Yes. While you can choose to access and process data entirely behind your own firewalls, you can also connect to databases and files that are either out in the Cloud, on-premise, or on your own workstation or laptop for analysis, processing, and reporting.

Can I integrate this product into existing data pipelines or applications?
Yes, both the single-record and full-dataset APIs are JSON-based for compatibility. To access them externally from pipelines, processes, applications, Web forms, or anything else, simply open the port using AWS that the two APIs are running on, and they can be accessed from anywhere. You can change/configure the ports of access as well if you prefer.

Are there any limitations on the volume of data that can be processed?
Other than theoretical limits of physics and infinity, there are none. Have at it. You can use more powerful EC2 instances for massive data requirements if necessary as well.

What kind of performance can be expected?
Other than reading data from your databases/files and writing out results, all processing occurs in-memory. In other words, it is lightning-fast. Using more advanced AWS hardware infrastructure and more powerful EC2 machines can be leveraged to increase performance as well if desired.

Is technical support available for deploying and using the product?
Yes. We want you to be successful and will help you if you need it.

Additional questions? Email us at

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