Crozdesk Data API: Extract Structured JSON in 2026

This guide covers extracting publicly accessible data. Always review a site's robots.txt and Terms of Service before scraping.

TL;DR

Use AlterLab's Extract API to turn a Crozdesk review page into typed JSON. Define a schema for fields like product_name, rating, and review_count, POST the URL and schema, and receive validated data—no HTML parsing required.

Why use Crozdesk data?

Crozdesk aggregates software reviews that are valuable for several engineering workflows:

• Training AI models: Labeled review text and ratings improve sentiment analysis or recommendation systems.
• Competitive intelligence: Monitor rating trends across product categories to spot market shifts.
• Analytics pipelines: Feed structured review counts and verified purchase flags into dashboards for product‑market fit studies.

What data can you extract?

From a typical Crozdesk review page you can pull the following publicly visible fields:

• product_name: The name of the software or service being reviewed.
• rating: Average star rating (often a string like "4.5").
• review_count: Total number of reviews submitted.
• category: The software category or industry vertical.
• verified_purchase: Flag indicating whether the reviewer confirmed a purchase.

Each field can be typed as a string in the JSON schema; AlterLab will coerce extracted text to match the requested type.

The extraction approach

Attempting to scrape Crozdesk with raw HTTP requests and HTML parsers runs into several obstacles:

• JavaScript‑rendered content requires a headless browser.
• Anti‑bot mechanisms (CAPTCHAs, rate limits) trigger blocks.
• Page layout changes break CSS selectors, leading to brittle pipelines.

A data API abstracts these challenges. AlterLab manages headless browsing, proxy rotation, and automatic retries, returning only the data you asked for in the format you specified. This shifts effort from maintenance to modeling.

Quick start with AlterLab Extract API

First, install the Python client (or use cURL directly). The following example shows a basic extraction call.

import alterlab

client = alterlab.Client("YOUR_API_KEY")

schema = {
  "type": "object",
  "properties": {
    "product_name": {
      "type": "string",
      "description": "The product name field"
    },
    "rating": {
      "type": "string",
      "description": "The rating field"
    },
    "review_count": {
      "type": "string",
      "description": "The review count field"
    },
    "category": {
      "type": "string",
      "description": "The category field"
    },
    "verified_purchase": {
      "type": "string",
      "description": "The verified purchase field"
    }
  }
}

result = client.extract(
    url="https://crozdesk.com/example-page",
    schema=schema,
)
print(result.data)

The highlighted lines (5‑12) define the schema and invoke the endpoint. The client handles authentication, request signing, and error handling.

Equivalent cURL request:

curl -X POST https://api.alterlab.io/v1/extract \
  -H "X-API-Key: YOUR_KEY" \
  -H "Content-Type: application/json" \
  -d '{
    "url": "https://crozdesk.com/example-page",
    "schema": {"properties": {"product_name": {"type": "string"}, "rating": {"type": "string"}, "review_count": {"type": "string"}}}
  }'

Both calls return a JSON payload whose data property matches the schema exactly.

Example output

{
  "product_name": "ProjectManager Pro",
  "rating": "4.7",
  "review_count": "128",
  "category": "Project Management",
  "verified_purchase": "Yes"
}

Define your schema

The Extract API expects a JSON Schema draft‑07 document under the schema key. Each property you list becomes a field in the output. You can add constraints such as minimum, maximum, pattern, or enum to tighten validation. If a field cannot be extracted, AlterLab returns null for that property, preserving the schema shape.

Because the output is typed, downstream code can treat review_count as a number after simple casting, eliminating guesswork about string formats.

Handle pagination and scale

Crozdesk often spreads reviews across multiple pages. To collect large datasets:

1. Batching: Submit an array of URLs in a single request using the /v1/extract/batch endpoint (available in the client as client.extract_batch). This reduces round‑trip overhead.
2. Rate limits: AlterLab enforces a default limit of 10 requests per second per API key. Adjust via the dashboard or include a delay parameter in batch jobs.
3. Async jobs: For very high volumes, fire off asynchronous extractions and poll for completion. The client returns a job ID; you can later retrieve results with client.get_job(job_id).

import alterlab
import time

client = alterlab.Client("YOUR_API_KEY")

urls = [
    f"https://crozdesk.com/reviews?page={i}"
    for i in