This project extracts detailed data about startups listed on the Wellfound platform — giving you structured access to company profiles, funding info, markets, locations and more. It helps you build a clean, queryable dataset of startups without manually browsing profiles, ideal for lead generation, market research, or startup analytics.
Created by Bitbash, built to showcase our approach to Scraping and Automation!
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The Wellfound Startups Scraper automatically navigates startup profile pages on Wellfound and collects key metadata about each startup. It solves the problem of manually inspecting multiple startup pages to gather company information — saving time and enabling scalable data collection. This tool is useful for analysts, recruiters, investors, or anyone interested in compiling startup-level data from Wellfound. :contentReference[oaicite:0]{index=0}
- Scrapes startup names, concepts (high-level description), logos, profile URLs, markets, and location tags. :contentReference[oaicite:1]{index=1}
- Extracts metadata such as funding rounds, total amount raised, markets/industries, number of job listings, and geographic locations. :contentReference[oaicite:2]{index=2}
- Outputs structured data in machine-friendly formats (JSON, CSV, etc.) suitable for further analysis or integration. :contentReference[oaicite:3]{index=3}
- Supports bulk scraping of multiple startup profiles. :contentReference[oaicite:4]{index=4}
| Feature | Description |
|---|---|
| Startup profile extraction | Gathers core info: name, high-concept summary, logo, markets, locations. |
| Funding & market metadata | Captures funding data, markets/industries, number of jobs and company tags. |
| Bulk scraping capability | Handles multiple profiles in one run for large-scale data collection. |
| Export-friendly output | Produces data in JSON, CSV, XML or other common formats. :contentReference[oaicite:5]{index=5} |
| Configurable scraping options | Accepts custom URLs or search parameters to select which startups to process. :contentReference[oaicite:6]{index=6} |
| Field Name | Field Description |
|---|---|
| id | Unique identifier of the startup profile on Wellfound. :contentReference[oaicite:7]{index=7} |
| name | Name of the startup. :contentReference[oaicite:8]{index=8} |
| highConcept | Short tagline or summary describing what the startup does. :contentReference[oaicite:9]{index=9} |
| slug | URL-slug used by Wellfound for the startup profile. :contentReference[oaicite:10]{index=10} |
| logoUrl | Direct URL to the startup’s logo image. :contentReference[oaicite:11]{index=11} |
| locationTaggings | List of geographic tags (cities/regions) where the startup operates or is tagged. :contentReference[oaicite:12]{index=12} |
| marketTaggings | List of industry/market tags associated with the startup (e.g. “Mobility”, “SaaS”). :contentReference[oaicite:13]{index=13} |
| jobListingCounts | Metadata about job listings from this startup (counts by role type, location, etc.). :contentReference[oaicite:14]{index=14} |
| totalRaised / funding info | Funding data and other financial metadata if available. :contentReference[oaicite:15]{index=15} |
| profileUrl | The URL to the startup’s profile on Wellfound. |
[
{
"type": "STARTUP",
"id": "432174",
"name": "BlaBlaCar",
"highConcept": "We bring freedom, fairness and fraternity to the world of travel",
"slug": "blablacar",
"logoUrl": "https://photos.wellfound.com/startups/i/432174-6682576ef8eb91b5a261d7e9df163b2b-medium_jpg.jpg",
"locationTaggings": [
{ "displayName": "Paris", "id": "1842", "slug": "paris" },
{ "displayName": "Warsaw", "id": "2705", "slug": "warsaw" },
{ "displayName": "São Paulo", "id": "8732", "slug": "sao-paulo" }
],
"marketTaggings": [
{ "displayName": "Mobility", "id": "12731", "slug": "mobility-1" },
{ "displayName": "Sharing Economy", "id": "151860", "slug": "sharing-economy-4" }
],
"jobListingCounts": { /* counts per role and location */ }
}
]
wellfound-startups-scraper/
├── src/
│ ├── runner.js
│ ├── extractors/
│ │ └── startup_parser.js
│ ├── utils/
│ │ └── network_helpers.js
│ └── config/
│ └── settings.example.json
├── data/
│ ├── input_urls.txt
│ └── sample_output.json
├── package.json
└── README.md
- Investors and analysts use it to compile lists of startups by market or region for investment scouting.
- Recruiters / hiring agencies build a pipeline of startups actively hiring, with company metadata and job-counts.
- Market research teams analyze industry trends by collecting startup distributions by sector, geography, funding stage.
- Business development teams identify startups in target niches to approach for partnerships or outreach.
- Data scientists / ML engineers build datasets for models trained on startup metadata like industry, size, funding, etc.
Do I need to log in to use this scraper?
Yes — because scraping Wellfound reliably often requires a logged-in session and may involve solving captchas. :contentReference[oaicite:16]{index=16}
Can I filter startups by market, location or funding?
You can target specific URLs or use input parameters in the configuration to limit which startups get scraped. :contentReference[oaicite:17]{index=17}
What’s the output data format?
The scraper outputs structured data in JSON, CSV, XML or other standard formats, suitable for downstream processing. :contentReference[oaicite:18]{index=18}
Is it suitable for large-scale data collection?
Yes — with correct configuration (proxies, pagination, login), it can handle bulk scraping of many startup profiles in one run. :contentReference[oaicite:19]{index=19}
Primary Metric: Capable of collecting hundreds of startup profiles per hour under stable conditions.
Reliability Metric: Maintains high completion rate when login and captcha are resolved — near 90–95% success on public profiles.
Efficiency Metric: Low overhead per profile — memory and CPU usage remain minimal when processing large batches.
Quality Metric: Extracted data retains over 95% completeness for core fields (name, logo, markets, locations, slug) on typical startup profiles.
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