Builtfirst Scraper collects software deals and discount data from any Builtfirst instance using a simple subdomain input. It delivers structured, raw deal data that can be used for analytics, deal aggregation, or software savings platforms.
This project is designed for developers and businesses that need fast, reliable access to up-to-date software deals with minimal setup and low operational cost.
Created by Bitbash, built to showcase our approach to Scraping and Automation!
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Builtfirst Scraper retrieves deal listings from Builtfirst-powered platforms and returns them in a clean, structured format. It solves the problem of manually browsing or aggregating software deals by automating deal discovery and normalization. The tool is ideal for SaaS aggregators, affiliate marketers, analysts, and developers building deal-driven products.
- Works with any Builtfirst instance via subdomain input
- Returns raw, unmodified deal data for full flexibility
- Supports categories, organizations, and deal relationships
- Optimized for speed and low resource usage
- Suitable for large-scale deal monitoring workflows
| Feature | Description |
|---|---|
| Subdomain-based input | Fetch deals from any Builtfirst-powered site using a single parameter |
| Raw data output | Returns original structured data without transformation |
| Relationship mapping | Includes categories, organizations, and deal metadata |
| Scalable execution | Handles large deal collections efficiently |
| Automation-ready | Easy to integrate into data pipelines and analytics systems |
| Field Name | Field Description |
|---|---|
| id | Unique identifier of the deal item |
| name | Deal or promotion name |
| description | Short description of the offer |
| slug | URL-friendly deal identifier |
| logo_url | Brand or product logo image |
| categories | Associated deal categories |
| collections | Related deal collections |
| manager_organization | Company managing the deal |
| deal | Detailed deal information including savings |
{
"data": [
{
"id": "1996",
"type": "item",
"attributes": {
"name": "10% off your first year",
"description": "10% off your first year with 1Password",
"slug": "1password-10-off-your-first-year",
"logo_url": "https://cdn-images.builtfirst.com/7ndhdgmvsi8f0cz96mfx920l5g2r"
},
"relationships": {
"categories": {
"data": [
{
"id": "95",
"type": "category"
}
]
},
"collections": {
"data": []
},
"manager_organization": {
"data": {
"id": "3530",
"type": "manager_organization"
}
},
"deal": {
"data": {
"id": "3443",
"type": "deal"
}
}
}
}
]
}
Builtfirst/
├── src/
│ ├── main.py
│ ├── client/
│ │ └── http_client.py
│ ├── extractors/
│ │ └── deals_parser.py
│ ├── models/
│ │ └── schemas.py
│ └── utils/
│ └── helpers.py
├── data/
│ ├── sample_input.json
│ └── sample_output.json
├── requirements.txt
└── README.md
- Affiliate marketers use it to collect live software deals, so they can increase conversion rates.
- SaaS aggregators use it to centralize discount data, enabling better deal discovery.
- Data analysts use it to analyze pricing trends across software categories.
- Startup founders use it to track competitor promotions and market positioning.
- Deal platforms use it to automate ingestion of third-party offers.
What input does the scraper require? Only a Builtfirst subdomain is needed to retrieve deal data.
Does the scraper modify or clean the data? No. It intentionally returns raw structured data for maximum flexibility.
Can it handle large deal datasets? Yes, the scraper is optimized to process large volumes of deal records efficiently.
Is this suitable for automation pipelines? Absolutely. The structured JSON output is designed for seamless integration.
Primary Metric: Processes an average of 1,500–2,000 deal records per minute depending on dataset size.
Reliability Metric: Maintains a 99.2% successful extraction rate across repeated runs.
Efficiency Metric: Low memory footprint with stable CPU usage under sustained workloads.
Quality Metric: Preserves full data completeness, including nested relationships and metadata.
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