How to scrape agriculture and food production data from Built In using the Minexa.ai extension

Built In is a tech-focused jobs and company directory that covers a wide range of industries, including agriculture and food production. Its company listings page at builtin.com/companies/type/agriculture-companies surfaces company names, employee counts, office locations, benefits counts, industry tags, and hiring status all in one place. That makes it a useful starting point for agri-food market research, competitive benchmarking, or building a targeted outreach list.

The challenge is that the data sits inside a paginated listing with structured but non-copyable HTML. Manually collecting even a few dozen records takes time and produces inconsistent results. This walkthrough shows how to extract it cleanly using the Minexa.ai Chrome extension, no code required.

Watch the tutorial first

The video below walks through the full extraction process from start to export. It covers every step shown in the screenshots further down.

https://www.youtube.com/watch?v=bXOuZOmmSmU

Scenario: you need a list of hiring agri-food companies with size and location data

Whether you are mapping the competitive landscape, identifying potential partners, or building a prospect list, the Built In agriculture listing gives you a filtered view of companies actively hiring. Each card includes employee count, office locations, and industry tags. Extracting this at scale means you can filter, sort, and enrich the data in a spreadsheet rather than reading through pages manually.

Start by navigating to the page in Chrome. Once it loads, open the Minexa.ai extension.

Minexa.ai detects the page automatically. You will see a prompt asking you to confirm you are on the right page before it proceeds.

Scenario: the listing spans multiple pages and you need all of them

Built In paginates its company listings. If you only scrape the first page, you miss most of the dataset. Minexa.ai detects the pagination method automatically and handles it without any configuration on your end.

After confirming the page, the extension shows you what pagination logic it found and asks you to confirm before continuing.

Once confirmed, you choose whether to scrape just the list or also follow each company link to extract detail page data. For most agri-food research use cases, the list itself contains enough structured information to work with.

Scenario: you want clean structured output without configuring fields manually

One of the more useful aspects of Minexa.ai is that you do not need to specify which fields to extract in advance. The extension identifies repeating patterns on the page and surfaces all available data points automatically. You select simple scraping mode for a standard extraction, or advanced if you need custom interaction logic.

After selecting your mode, Minexa.ai highlights the full data container on the page automatically. You confirm the selection and click to create the scraper.

Once the scraper is created, you can browse through all extracted data points using the navigation controls inside the extension.

What the extracted data looks like

Below are two example records from the extraction. Column name prefixes have been removed for readability.

[
  {
    "company_name": "McCain Foods",
    "company_description": "At McCain Foods we know the importance that food plays in people's lives.",
    "company_href": "/company/mccain-foods",
    "employee_count": "20,000 Employees",
    "location": "8 Offices",
    "industry_focus": "Food - Retail - Agriculture - Manufacturing",
    "benefits_count": "36 Benefits",
    "logo_image_source": "https://builtin.com/sites/www.builtin.com/files/2024-04/McCain Careers Logo.png"
  },
  {
    "company_name": "Carbon Robotics",
    "company_description": "Mission Driven AgTech Company in Seattle building the first LaserWeeder.",
    "company_href": "/company/carbon-robotics",
    "employee_count": "300 Employees",
    "location": "8 Offices",
    "industry_focus": "Artificial Intelligence - Computer Vision - Hardware - Machine Learning - Robotics - Agriculture",
    "benefits_count": "41 Benefits",
    "logo_image_source": "https://builtin.com/sites/www.builtin.com/files/2022-09/10_56.jpg"
  }
]

The company_details field deserves particular attention. It returns a structured array per card that encodes office count, individual office location names via a data-bs-title tooltip attribute, employee count, benefits link path, benefits count, hiring status label, and where available, a teams page link with its tracking identifier and icon class string. All of this comes from a single traversable field rather than separate columns, which means you can parse it downstream to extract whichever sub-values you need.

The job_alert_function field captures the JavaScript function call string attached to each company card, for example createJobAlert(110494, 'McCain Foods'). This encodes both the numeric company ID and the display name in a single text value. Parsing this string gives you a stable internal identifier for each company that you can use to deduplicate records or construct direct links to company-specific pages.

The job_section_id and job_section_id_md fields provide two DOM anchor identifiers per listing, corresponding to desktop and mobile job section containers respectively. These follow a consistent pattern of job-sections-[id] and job-sections-md-[id], and the numeric suffix matches the company ID found in the job alert function, giving you a reliable cross-reference point across fields.

The logo_image_source field surfaces the full absolute CDN URL for each company logo. Depending on how the logo was uploaded, this may point to a path under builtin.com/sites/www.builtin.com/files/ or under static.builtin.com/companies-assets/. A small number of listings fall back to a generic placeholder image at static.builtin.com/dist/images/company-logo-fallback.png, which you can filter out programmatically after export.

Running the job and exporting your data

After the scraper is configured, the job appears in your scraping jobs list. You can run it immediately from there.

Once the run completes, the results appear in a table view. From there you can export to Excel, Google Sheets, or JSON depending on where you plan to use the data.

You can also schedule the job to run on a recurring basis if you want to track changes over time, such as new companies appearing in the listing or hiring status updates.

If you want to get started, install the Minexa.ai Chrome extension and navigate to the Built In agriculture companies page. The setup takes a few minutes and the scraper can be reused on any subsequent run without repeating the configuration.

For more on extracting company and jobs data from similar sources, see how to scrape jobs data from Apna using the Minexa.ai API.