Use Case

Content Monitoring & Change Detection

Track price changes, content updates, and new pages across any website with scheduled scraping and webhook alerts.

The Problem

Businesses need to know when competitors change prices, when regulations update, or when news breaks. Manual checking is unreliable and doesn't scale:

Competitor price changes that affect your market position
Regulatory or policy page updates that require compliance action
Content changes on partner or supplier sites
New job listings, product launches, or press releases

Solution Architecture

Combine scheduled scraping with diff comparison and webhook notifications:

1. Schedule

POST /schedules with a cron expression to run scrapes on your desired frequency — hourly, daily, or weekly.

2. Compare

Disable caching with cache: false so each scheduled run returns fresh data. Compare with your stored baseline.

3. Alert

Use webhook_url to receive results as they complete. Your webhook handler compares and triggers notifications.

Quick Example

Scrape a page with caching disabled and compare against a stored snapshot to detect changes:

Python

import requests
import hashlib

def check_for_changes(api_key, url, previous_hash):
    """Scrape a page and detect content changes."""
    resp = requests.post(
        "https://api.alterlab.io/api/v1/scrape",
        headers={"X-API-Key": api_key},
        json={
            "url": url,
            "cache": False,
            "formats": ["text"]
        }
    )

    text = resp.json().get("text", "")
    current_hash = hashlib.sha256(text.encode()).hexdigest()

    if current_hash != previous_hash:
        return {"changed": True, "hash": current_hash, "content": text}
    return {"changed": False, "hash": current_hash}

# First run: establish baseline
result = check_for_changes("YOUR_API_KEY", "https://example.com/pricing", "")
stored_hash = result["hash"]

# Subsequent runs: detect changes
result = check_for_changes("YOUR_API_KEY", "https://example.com/pricing", stored_hash)
if result["changed"]:
    print("Content has changed — sending alert")

Advanced Patterns

Scheduled Monitoring

Use the scheduler endpoint to automate recurring checks. Combine with a webhook to process results as they arrive:

Python

import requests

# Create a schedule to monitor a page every hour
response = requests.post(
    "https://api.alterlab.io/api/v1/schedules",
    headers={"X-API-Key": "YOUR_API_KEY"},
    json={
        "name": "Competitor pricing check",
        "url": "https://competitor.com/pricing",
        "cron": "0 * * * *",  # Every hour
        "cache": False,
        "extraction_schema": {
            "type": "object",
            "properties": {
                "price": {"type": "number"},
                "plan_name": {"type": "string"}
            }
        },
        "webhook_url": "https://your-app.com/webhooks/monitor"
    }
)

schedule = response.json()
print(f"Schedule created: {schedule.get('id')}")

Diff Detection

For granular change detection, use extraction schemas to pull specific fields and compare structured data instead of raw text:

Python

import requests
import json

def monitor_structured(api_key, url, schema, previous_data):
    """Monitor structured fields for changes."""
    resp = requests.post(
        "https://api.alterlab.io/api/v1/scrape",
        headers={"X-API-Key": api_key},
        json={
            "url": url,
            "cache": False,
            "extraction_schema": schema
        }
    )

    current = resp.json().get("filtered_content", {})
    changes = {}

    for key, value in current.items():
        if previous_data.get(key) != value:
            changes[key] = {
                "old": previous_data.get(key),
                "new": value
            }

    return {"data": current, "changes": changes}

# Monitor specific fields
schema = {
    "type": "object",
    "properties": {
        "price": {"type": "number"},
        "in_stock": {"type": "boolean"},
        "shipping_estimate": {"type": "string"}
    }
}

previous = {"price": 29.99, "in_stock": True, "shipping_estimate": "2-3 days"}
result = monitor_structured("YOUR_API_KEY", "https://store.com/product/1", schema, previous)

if result["changes"]:
    for field, change in result["changes"].items():
        print(f"{field}: {change['old']} -> {change['new']}")

Cost Tip

For monitoring, use extraction_schema to extract only the fields you care about. This is more efficient than comparing full page content and gives you structured diffs.

Scheduler Guide

Automate recurring scrapes with cron expressions and balance-based limits.

Webhooks Guide

Get notified when async scrapes complete with webhook callbacks.

News Monitoring Tutorial

Build news aggregation and brand monitoring systems.

Caching Guide

Learn when to cache and when to disable caching for fresh data.

← Previous: RAG & AI Next: Market Research →

Last updated: March 2026

Use Case

Content Monitoring & Change Detection

Track price changes, content updates, and new pages across any website with scheduled scraping and webhook alerts.

The Problem

Businesses need to know when competitors change prices, when regulations update, or when news breaks. Manual checking is unreliable and doesn't scale:

Competitor price changes that affect your market position
Regulatory or policy page updates that require compliance action
Content changes on partner or supplier sites
New job listings, product launches, or press releases

Solution Architecture

Combine scheduled scraping with diff comparison and webhook notifications:

1. Schedule

POST /schedules with a cron expression to run scrapes on your desired frequency — hourly, daily, or weekly.

2. Compare

Disable caching with cache: false so each scheduled run returns fresh data. Compare with your stored baseline.

3. Alert

Use webhook_url to receive results as they complete. Your webhook handler compares and triggers notifications.

Quick Example

Scrape a page with caching disabled and compare against a stored snapshot to detect changes:

Python

import requests
import hashlib

def check_for_changes(api_key, url, previous_hash):
    """Scrape a page and detect content changes."""
    resp = requests.post(
        "https://api.alterlab.io/api/v1/scrape",
        headers={"X-API-Key": api_key},
        json={
            "url": url,
            "cache": False,
            "formats": ["text"]
        }
    )

    text = resp.json().get("text", "")
    current_hash = hashlib.sha256(text.encode()).hexdigest()

    if current_hash != previous_hash:
        return {"changed": True, "hash": current_hash, "content": text}
    return {"changed": False, "hash": current_hash}

# First run: establish baseline
result = check_for_changes("YOUR_API_KEY", "https://example.com/pricing", "")
stored_hash = result["hash"]

# Subsequent runs: detect changes
result = check_for_changes("YOUR_API_KEY", "https://example.com/pricing", stored_hash)
if result["changed"]:
    print("Content has changed — sending alert")

Advanced Patterns

Scheduled Monitoring

Use the scheduler endpoint to automate recurring checks. Combine with a webhook to process results as they arrive:

Python

import requests

# Create a schedule to monitor a page every hour
response = requests.post(
    "https://api.alterlab.io/api/v1/schedules",
    headers={"X-API-Key": "YOUR_API_KEY"},
    json={
        "name": "Competitor pricing check",
        "url": "https://competitor.com/pricing",
        "cron": "0 * * * *",  # Every hour
        "cache": False,
        "extraction_schema": {
            "type": "object",
            "properties": {
                "price": {"type": "number"},
                "plan_name": {"type": "string"}
            }
        },
        "webhook_url": "https://your-app.com/webhooks/monitor"
    }
)

schedule = response.json()
print(f"Schedule created: {schedule.get('id')}")

Diff Detection

For granular change detection, use extraction schemas to pull specific fields and compare structured data instead of raw text:

Python

import requests
import json

def monitor_structured(api_key, url, schema, previous_data):
    """Monitor structured fields for changes."""
    resp = requests.post(
        "https://api.alterlab.io/api/v1/scrape",
        headers={"X-API-Key": api_key},
        json={
            "url": url,
            "cache": False,
            "extraction_schema": schema
        }
    )

    current = resp.json().get("filtered_content", {})
    changes = {}

    for key, value in current.items():
        if previous_data.get(key) != value:
            changes[key] = {
                "old": previous_data.get(key),
                "new": value
            }

    return {"data": current, "changes": changes}

# Monitor specific fields
schema = {
    "type": "object",
    "properties": {
        "price": {"type": "number"},
        "in_stock": {"type": "boolean"},
        "shipping_estimate": {"type": "string"}
    }
}

previous = {"price": 29.99, "in_stock": True, "shipping_estimate": "2-3 days"}
result = monitor_structured("YOUR_API_KEY", "https://store.com/product/1", schema, previous)

if result["changes"]:
    for field, change in result["changes"].items():
        print(f"{field}: {change['old']} -> {change['new']}")

Cost Tip

For monitoring, use extraction_schema to extract only the fields you care about. This is more efficient than comparing full page content and gives you structured diffs.

Last updated: March 2026

Content Monitoring & Change Detection

The Problem

Solution Architecture

1. Schedule

2. Compare

3. Alert

Quick Example

Advanced Patterns

Scheduled Monitoring

Diff Detection

Related Guides

Scheduler Guide

Webhooks Guide

News Monitoring Tutorial

Caching Guide

Content Monitoring & Change Detection

The Problem

Solution Architecture

1. Schedule

2. Compare

3. Alert

Quick Example

Advanced Patterns

Scheduled Monitoring

Diff Detection

Related Guides

Scheduler Guide

Webhooks Guide

News Monitoring Tutorial

Caching Guide