Data Matching Example
See our Postgres batch processing in action - generating similarity keys to match company/organization name data from directly within Postgres tables.
Match organization names, individual names, addresses, and general text across entire Postgres tables
Lightning-fast parallel processing with AI-powered similarity matching, directly inside your database
Leverage our high-performance, parallel-processing cloud architecture to run AI-driven matching jobs against entire Postgres tables or views, finding the duplicate and near-duplicate records that exact matching cannot.
Interzoid connects using a standard PostgreSQL connection string, so it reads from managed cloud services, PostgreSQL-compatible databases, and self-managed servers alike.
No connector to install and nothing to deploy inside your environment. If your platform accepts a standard PostgreSQL connection string, Interzoid can read from it, using either individual connection fields or the connection string your provider already gives you.
Seamlessly process Postgres tables or views with direct, read-only database connectivity, including dedicated read-only roles, and optimized performance for cloud databases.
Learn MoreAdvanced algorithms and AI models identify similar data elements, generate similarity keys for clustering matches and detecting inconsistencies within and across your data tables.
Learn MoreMulti-threaded, high-performance architecture processes high volumes of data in seconds using our distributed cloud infrastructure.
Learn MoreGet started in minutes with our streamlined three-step process
Connect directly to your Postgres database tables using connection fields or a standard connection string, with a read-only role recommended.
Choose the algorithm that fits your data: organization names, individual names, addresses, general text, or a combination of two of them at once.
Receive a match report with records grouped into clusters by similarity key, ready to review, share, or import back into your Postgres tables.
See our Postgres batch processing in action - generating similarity keys to match company/organization name data from directly within Postgres tables.
Four data types, plus combinations that raise precision when one field alone is too broad
Resolve Acme Corp, ACME Corporation, and Acme Inc. to a single organization, across the legal suffixes, punctuation, abbreviations, and spelling variations that make company names so inconsistent.
Match James Johnston, Jim Johnston, and J. Johnston as the same person, handling nicknames, initials, middle names, and name ordering.
Reconcile 400 E Broadway St with 400 East Broadway Street, resolving directional abbreviations, street type variations, unit designations, and spacing.
Generate similarity keys for other text values, so product names, descriptions, and free-form fields cluster the same way that names and addresses do.
Require two fields to agree at once: organization with address, organization with individual name, or address with individual name. This separates the Dallas branch from the Phoenix branch while still recognizing that both spell the parent company four different ways.
Every value receives a key representing the entity behind the text rather than the characters themselves. Records sharing a key are grouped into clusters, which makes each match auditable instead of something to take on faith.
Any PostgreSQL server reachable over the network. That includes self-managed instances and managed services such as Amazon RDS for PostgreSQL, Amazon Aurora PostgreSQL, Azure Database for PostgreSQL, Google Cloud SQL, Google AlloyDB, Supabase, Neon, Heroku Postgres, and DigitalOcean Managed Databases, along with PostgreSQL-compatible platforms including TimescaleDB, CockroachDB, and YugabyteDB. If the platform accepts a standard PostgreSQL connection string, Interzoid can read from it.
No. Processing issues SELECT statements against the table and columns you choose and nothing else. Nothing is written, updated, or deleted, and no schema objects are created. Results are returned to your browser, and you decide what to do with them.
No. Interzoid connects directly to your PostgreSQL database and reads the columns you select. There is no CSV export, no staging area, and no copy of your data to secure, track, or delete afterward.
Credentials are transmitted over HTTPS with each request and are not retained after the session ends. Connections use TLS by default. Because processing is read-only, the recommended practice is to connect with a dedicated read-only role that can be revoked at any time.
Each record processed consumes one Interzoid API credit. Trial credits are included with a new API account, so a first table can be processed at no cost, and high volume tables are supported for production workloads.
Yes. The same processing is available as a REST API that can be called from scripts, applications, and data pipelines, which allows PostgreSQL matching jobs to be automated without using the browser wizard.
Documentation, background reading, and the rest of the Interzoid platform
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