Find Employees at a Company

What this skill does

Searches for people at a specific company. The search is scoped by company - a free-text company name (company) or, more precisely, an exact company_domain. You can also narrow within the company using seniority, department/functional_area, and country request filters. Optionally enriches the results with business emails (though search results often already include business_email). Starts with a company lookup to confirm the right entity before searching employees.

Chain

company name/domain
  to search_companies   (resolve name to domain + firmographics)
  to search_people  (find people, scoped by company_domain or company name)
  to linkedin_to_business_email     (optional - per-URL parallel calls to fill missing emails)

Inputs

• Company - name or domain (required - hard stop without it). company_domain is the most precise scope
• Optional role slice - seniority, department, or country to narrow within the company
• Acceptable risk scores - which A-F deliverability grades to keep (recommended: A, B, C and F; D excluded)

When it activates

• "who works at Stripe"
• "show me people at HubSpot"
• "list employees at [Company]"
• "find contacts at [Company]"

Output

Closes with the risk-score spread across the page. Default 10 results per page (max 25). Paginate with offset to cover more of a large employee base. Offers to fetch more or enrich with contact data.

Optional enrichment

If you want emails: results frequently already carry business_email, each with its A-F risk score. The skill shows a credit estimate first, then runs linkedin_to_business_email once per URL (single input each, fired in parallel) only for the rows that are missing one. Phone uses linkedin_to_mobile_phone with a linkedin_urls array.

Search filters

search_people filters on query, company, company_domain, country, state, city, seniority, department or functional_area, industry, linkedin_industry, naics_code, employee_range, and revenue_range.

• Filters are free precision - execution cost comes from the free-text query, so a filters-only search runs ~40x cheaper and lands more precisely. A bare query is never the right shape; at minimum it gets country attached (~99% fill rate).
• Size and revenue apply to the person's current employer - "VPs at 51-200 employee software companies" is one call, not a company search followed by a people search.
• Three industry vocabularies - broad industry buckets, LinkedIn's ~150 niche labels, or a NAICS code at any depth. Shortening the code widens the net.
• Sparse filters are the usual cause of a thin result set - seniority/industry/department are ~60% filled and naics_code/linkedin_industry ~50%, and every filter is exact-match, silently dropping records where the field is empty.
• Sizing is free - count_only: true returns a match count without fetching data or charging tokens, and every response carries results.total to read before paginating.

Company-side filters

search_companies filters on query (company name only), domain, industry, linkedin_industry, naics_code, employee_range, revenue_range, country, and state.

• domain beats free text every time - exact match, and full URLs are normalised automatically, so https://www.acme.com/about needs no cleanup first.
• Filters are free precision - a filters-only search runs ~40x cheaper than one carrying free text, and most firmographic work needs no query at all.
• Industry vocabularies overlap - the same company can sit under "Information Technology" or "Computer Software", so a thin result is often a neighbouring-value problem rather than a coverage one.
• Company location is derived from where the team is based, and large multinationals resolve to a single one of their offices - pair location with another filter on enterprise or international targeting.
• Range values are exact and skewed - "1-10" dominates employee counts, "$500k - $1M" dominates revenue, and the legacy "$1 - $1M" band holds far fewer records than its numeric equivalent.

Tips

• Use exact company_domain when you have it - it scopes results to the company far more reliably than a free-text name in company/query
• Company name in company/query is a soft signal - results may include people who mention the company in their headline, not just direct employees
• Seniority, department/functional_area, and country are accepted request filters - use them to grab a role slice (e.g. VPs in Sales). Title also returns for ranking
• Many rows already include business_email, so enrichment is often unnecessary
• Max 25 per page - use offset pagination to cover more employees
• Search only: ~10–15 seconds; with enrichment: ~20–40 seconds
• Every returned email carries an A-F deliverability risk score; you're asked up front which grades to keep, and out-of-range grades are shown and flagged rather than silently dropped