UpLead alternative: U.S. emails and mobiles as one free file, not credits by the month

“One credit equals one contact”, with its email and mobile direct dial. Each plan is one user. A year of Plus, $1,788, unlocks 4,800 people at most. The free file holds 5,182,067 people with a mobile or an email.

UpLead's guide to buying email lists sends you on to its lists by job title: CFOs, CTOs, VPs of sales, HR managers, business owners. Each is one pattern on the title as written on LinkedIn. Unzip the file into us_10m/, pip install duckdb, then

import duckdb

duckdb.sql("create view people as select * from 'us_10m/us_smb_mid_market_persons.parquet'")

# A website as its domain: lowercase, without http(s)://, www. or a path, so "https://www.Acme.com/about" is acme.com
duckdb.sql(r"create macro domain(url) as regexp_extract(lower(trim(url)), '^(?:https?://)?(?:www[.])?([^/:?#]+)', 1)")
duckdb.sql("create view companies as select *, domain(URL) as DOMAIN from 'us_10m/us_smb_mid_market_companies.parquet'")

# UpLead's email lists by job title, counted: the people with a mobile phone or an email in each
duckdb.sql(r"""
  with lists(list, pattern, skip) as (values
    ('Business owners', '\b(owner|co-owner)\b', '\b(product|process|system|data|service|application|platform) owner\b|\bowner.?s rep'),
    ('CEOs', '\b(ceo|chief executive officer)\b', null),
    ('CFOs', '\b(cfo|chief financial officer)\b', null),
    ('COOs', '\b(coo|chief operating officer)\b', null),
    ('CTOs', '\b(cto|chief technology officer)\b', null),
    ('CIOs', '\b(cio|chief information officer)\b', null),
    ('CMOs', '\b(cmo|chief marketing officer)\b', null),
    ('CHROs', '\b(chro|chief (human resources|people) officer)\b', null),
    ('VPs of sales', '\b(vp|vice president)\b.*\bsales\b', null),
    ('Sales directors', '\bdirector\b.*\bsales\b|\bsales director\b', null),
    ('Marketing directors', '\bdirector\b.*\bmarketing\b|\bmarketing director\b', null),
    ('HR managers', '\b(hr|human resources) manager\b', null)),
  titled as (  -- the titles any list can match, so each list's pattern reads far fewer rows
    select CURRENT_JOB_TITLE, MOBILE_PHONE, EMAIL from people
    where (MOBILE_PHONE is not null or EMAIL is not null)
      and regexp_matches(CURRENT_JOB_TITLE, '\b(owner|ceo|cfo|coo|cto|cio|cmo|chro|chief|vp|vice president|director|hr|human resources)\b', 'i'))
  select l.list, count(*) as people, count(p.MOBILE_PHONE) as mobile, count(p.EMAIL) as email
  from lists l
  join titled p on regexp_matches(p.CURRENT_JOB_TITLE, l.pattern, 'i')
  where (l.skip is null or not regexp_matches(p.CURRENT_JOB_TITLE, l.skip, 'i'))
  group by l.list order by people desc
""").show()

A title can be in two lists (“Owner & CEO”). To keep a list, swap .show() for a copy (...) to 'cfos.csv' with the columns you want, as below, or add c.EMPLOYEE_COUNT_RANGE, p.STATE_CODE or c.LINKEDIN_INDUSTRY to the where.

On Plus, UpLead takes “a list of emails, phone numbers or company URL's to exclude from your results”: the people you already have. From the file, the same exclusion is three not exists, here on the CFOs:

# have.csv: the contacts you already have, with an email, a phone and a website column (any cell can be empty).
# The CFOs, without anyone you have, nor anyone at a company you have
duckdb.sql("create view have as select * from read_csv('have.csv', header = true, all_varchar = true)")
duckdb.sql(r"""
  copy (
    select p.FULL_NAME, p.CURRENT_JOB_TITLE, c.NAME as COMPANY, c.URL as WEBSITE, p.EMAIL, p.MOBILE_PHONE, p.LINKEDIN_URL
    from people p
    join companies c on c.LINKEDIN_ID = p.CURRENT_JOB_COMPANY_LINKEDIN_ID
    where regexp_matches(p.CURRENT_JOB_TITLE, '\b(cfo|chief financial officer)\b', 'i')
      and (p.MOBILE_PHONE is not null or p.EMAIL is not null)
      and not exists (from have h where lower(trim(h.email)) = p.EMAIL)
      and not exists (from have h where right(regexp_replace(h.phone, '[^0-9]', '', 'g'), 10) = right(regexp_replace(p.MOBILE_PHONE, '[^0-9]', '', 'g'), 10))
      and not exists (from have h where domain(h.website) = c.DOMAIN)
  ) to 'cfos_new.csv'
""")

Emails are compared in lowercase, phones by their last 10 digits, websites by their domain, so “(214) 538-1300” is “+1 214-538-1300” and “HTTPS://www.Acme.com/” is acme.com. We tested it on a have.csv of 200 CFOs' emails, 200 of their mobiles and 60 of their companies' websites from the file, each written one of the ways people paste them (capitals, spaces, brackets, dots, a leading 1, www., a path), and 20 made-up rows. It took 0.8 s.

None of the emails, mobiles or companies of have.csv is left in cfos_new.csv, and it holds exactly the CFOs a separate count of ours expected. A website takes everyone at that company out, whatever their email.

UpLead verifies each email again as you unlock it, and gives it a status: Valid, Invalid or Accept All. The file is a snapshot: each of its 2,790,286 emails carries EMAIL_VALIDATED_AT, the date it was last verified.

# When each email was last verified, by month (UTC)
duckdb.sql("set TimeZone = 'UTC'")
duckdb.sql("""
  select strftime(EMAIL_VALIDATED_AT, '%Y-%m') as month, count(*) as emails
  from people where EMAIL is not null
  group by month order by month
""").show()

In the October 2026 file, 2,778,350 of them (99.6%) were last verified in August, September or October 2026, 2,055,276 (73.7%) since September 1; the oldest on September 24, 2024, the newest on October 1, 2026. Sending to them weeks later, check the older ones again with your email verifier, or in the CFOs' query above, write case when p.EMAIL_VALIDATED_AT >= '2026-09-01' then p.EMAIL end as EMAIL for p.EMAIL to keep only the latest emails, the mobiles all.

• Verification at the unlock, and a refund: UpLead verifies each email as you take it and refunds the credit of a verified bounce.
• The world: 200 million contacts and 19 million companies, where the free file is 9,521,154 people at U.S. companies of 11 to 500 employees.
• Technographics (27,000 technologies), buyer intent, Email Pattern Intel and competitor intelligence.
• The tool: a search app, a Chrome extension, CRM integrations from Salesforce to Pipedrive, and a done-for-you cold email service.

A person as they are today rather than in October, their title and company, is the LinkedIn profile behind their URL: the LinkedIn profile API, at $0.00125 a profile found.

• Your list of accounts matched to the people at the top of each: RocketReach alternative. A list of LinkedIn URLs matched to their mobiles and emails: ContactOut alternative. The same file next to other tools' contacts: ZoomInfo alternative, Apollo alternative, Lusha alternative.
• How the file compares with LinkedIn, person by person: B2B data accuracy. What each tool's free plan gives: free B2B database.

The free file is on your dashboard once you sign up with your work email, with the $5 credit and your API key.