Deanonymize Website Traffic

What this skill does

Takes a batch of IP addresses from web traffic logs and resolves them to the companies behind them, then surfaces likely contacts at each of those companies - firmographics plus ranked person records and optional contact enrichment.

This identifies the company behind an IP and likely people at that company - it does not identify the specific individual who visited. Expect strong company match rates; contact discovery varies with company size and data coverage. Not-found results at each step are free.

Chain

IP addresses
  └── ip_to_company                  (per-IP single call to company name + firmographics)
        └── search_people (company domain to ranked contacts at that company)
              └── optional enrich    (per-URL linkedin_to_best_email / linkedin_to_mobile_phone array up to 100)

Inputs

• IP addresses - line-separated, CSV column, or server log format
• Private IPs (10.x, 192.168.x, 172.16–31.x, 127.x) are automatically excluded
• Acceptable risk scores - which A-F deliverability grades to keep (recommended: A, B, C and F; D excluded)

Output

IPs processed:       200
Company matches:     143  (72%)
Companies w/ contacts: 121  (85% of matched)
Contacts surfaced:   ~480
Credits used:        ~XXX

Email risk scores:
  A: X   B: X   C: X   D: X   F: X
  kept (accepted grades):  X
  filtered (excluded):     X

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.

Tips

• A mandatory credit check runs before the chain starts - company match is the reliable number; contact volume varies by company
• Contacts are surfaced by querying search_people with the company's domain (company_domain) - you can also narrow by seniority, department, country, or industry, and results come back ranked with title and seniority on each record
• These are likely contacts at the company, not the confirmed visitor - frame output accordingly
• ISP/residential IPs are flagged as "Residential/ISP - not a business" in output and skipped for contact lookup
• If credits run out mid-batch, completed rows are output with the exact resume point
• Processing 60–120+ seconds for any meaningful batch - set expectations accordingly
• 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