Interzoid Platform GitHub Repository: Technical Reference for Data Matching and Enrichment
The Interzoid Platform GitHub repository is the primary technical reference for engineers integrating Interzoid’s data matching and enrichment APIs. It contains minimal, focused examples in Go, Python, Kotlin, Node.js, and TypeScript, along with file-processing utilities, match-report generators, and OpenAPI specifications organized by API category.
If you want to see exactly how to:
- Issue HTTP requests to Interzoid APIs
- Process CSV/TSV files and attach similarity keys
- Generate cluster-based match reports
- Use OpenAPI documents as a contract for your services this repository is the best place to start.
- 📦 Repository: github.com/interzoid/interzoid-platform
- 🧪 Interactive API testing: try.interzoid.com
- 🔑 API key registration: www.interzoid.com/register-api-account
license query parameter or via the x-api-key header.
API Categories in the Repository
Interzoid’s APIs represented in the repository are grouped conceptually into several categories. The examples and OpenAPI specs map cleanly to these categories:
1. Matching & Similarity / Similarity Key Generation
- Company / Organization Matching — similarity keys and match scores for organization names
- Individual Name Matching — similarity and matching for full names of people
- Street Address Matching — advanced address similarity, normalization, and keys
2. Standardization APIs
- Organization Name Standardization — convert arbitrary variations into a single official standard name
- Other standardization endpoints — APIs that normalize and canonicalize input prior to downstream processing
3. Enrichment & Business Information APIs
- Business / Company Information — details such as URLs, locations, and profile attributes
- Parent Company Information — map a company to its parent organization and location
- Custom Data Enrichment (AI-driven) — query topic-specific information (e.g., HQ, CEO, revenue) in a structured format
4. Data Quality & Verification APIs
- Email Trust Score — assess email quality/validity and associated risk indicators
- Other quality/verification endpoints — additional checks that improve downstream reliability
The GitHub repository contains HTTP examples, file processors, and OpenAPI definitions that align with these categories, so you can quickly locate the assets that correspond to the APIs you plan to use.
Multi-Language HTTP Examples
Each language example in the repository is intentionally minimal and focuses on:
- Constructing the URL with required query parameters
- Attaching authentication via
x-api-keyheader orlicenseparameter - Executing an HTTP
GETrequest - Decoding JSON into strongly typed structures (where applicable)
- Inspecting the
CodeandCreditsfields for status and usage
For example, a typical Go flow looks like:
client := &http.Client{}
req, _ := http.NewRequest("GET",
"https://api.interzoid.com/getcompanymatch?license=YOUR_API_KEY&company=IBM",
nil)
resp, err := client.Do(req)
if err != nil {
// handle transport error
}
defer resp.Body.Close()
// check resp.StatusCode, then decode JSON
Python, Kotlin, Node.js, and TypeScript examples follow the same pattern: issue an HTTP request to the endpoint, decode JSON, and handle response codes and error conditions explicitly.
File Processing and Match Report Generation
A core part of the repository is dedicated to batch-oriented workflows. These examples show how to:
- Read input from CSV or TSV files using standard libraries
- Call similarity-key or match APIs for each record
- Attach keys or standardized values as new columns
- Group records by similarity key and generate match reports
A typical pattern for a match report generator in Go looks like:
// 1. Read each record from an input file
// 2. Call a similarity-key API (e.g., company, individual, address)
// 3. Store the original record along with its simkey in memory
// 4. Sort or group records by simkey
// 5. Output clusters where multiple records share the same key
The repository includes variants of this pattern across multiple languages so you can pick the one that aligns with your existing data tooling or processing environment.
OpenAPI Specifications by Category
For each major API category, the repository contains OpenAPI documents (in both YAML and JSON) that act as a precise contract for:
- Request paths and HTTP methods
- Query parameters and authentication options
- Response schemas, including success and error payloads
- Common error codes and semantics
Typical structure:
- Matching & Similarity APIs — tagged accordingly with endpoints for company, individual, and address matching
- Standardization APIs — endpoints like organization standardization grouped under standardization tags
- Enrichment APIs — business info, parent company info, and custom data enrichment grouped together
- Quality / Verification APIs — email trust and related quality checks
These OpenAPI files are useful wherever you need a machine-readable description of the API surface area, especially when validating requests/responses or driving documentation in your own environment.
Batch Tools and High-Volume Usage
While the GitHub repository focuses on code-level examples, many of the file-processing patterns map directly to Interzoid’s hosted batch environment at batch.interzoid.com.
Common workflow:
- Prototype locally using the file-processing examples in Go/Python/Node/etc.
- Validate that similarity keys and match clusters align with expectations
- Scale up volume either using your own infrastructure or Interzoid’s batch platform
Getting an API Key and Running the Examples
Every code sample assumes you have a valid API key:
- Register at www.interzoid.com/register-api-account
- Obtain your key from the account portal
- Replace placeholders such as
YOUR_API_KEYin the examples - Run from the command line (or within your environment) to confirm connectivity and JSON parsing
Many examples also log or print the Code and Credits fields so you can
confirm both functional behavior and credit consumption.
For engineers who want concrete, working examples rather than abstract documentation, the Interzoid Platform GitHub repository provides direct, reproducible patterns for calling Interzoid APIs, processing files at scale, and generating meaningful match reports. The organization by language and API category, combined with OpenAPI specifications, makes it straightforward to integrate these capabilities into existing data pipelines and services.
Clone the repo, wire in your API key, and adapt the patterns to your environment to bring standardized, enriched, and matched data into your own systems.