Email Finder API
Pricing
from $14.00 / 1,000 work e-mail founds
Find work e-mail addresses from a person's name and their company or domain: the address, its layout, a confidence level, the evidence behind it and the mail provider. Export scraped data, run the scraper via API, schedule and monitor runs, or integrate with other tools or AI workflows.
One person per line, written as Full Name, company. The company can be a domain (stripe.com), a website (https://stripe.com/about) or just the company's name (Clifford Chance) โ the Actor works out the domain either way. Jane Doe @ acme.com works too.
How many address candidates to return. The first is the answer; the rest are the next most likely layouts, listed in alternatives for when the first one bounces.
Look up the company's mail servers, so you know whether the domain accepts mail at all and who runs it (Google Workspace, Microsoft 365, and so on). Free and fast โ leave it on.
Write an extra row for each company with the address layout found, the addresses it was read from, and every public address on the site. These rows have different columns from the people rows, so the table shows blanks where a column does not apply โ leave it off unless you want that company sheet.
How deep to read each company's website looking for published addresses. Contact, about, team and legal pages are read first, so more than six rarely finds anything new.
A ceiling on how many people this run may look up, whatever the input contains. Leave the default unless you are running a big list on purpose.
An array of objects: {"firstName": "Jane", "lastName": "Doe", "company": "acme.com"}. fullName works in place of the two name fields, and domain, website or url in place of company.
ID of an Apify dataset to read the people from โ for example the output of US Business Leads or any lead list you already have on the platform.
Which column of that dataset holds the person's full name.
Which column holds the first name, when the name is split into two.
Which column holds the company name, domain or website.
How many companies to read at once. Lower it if a target website starts refusing requests.
How long to wait for one page of a company website.
