Reverse Email Lookup API Explained
By Kooperativa Engineering
Most enrichment workflows start from an identifier you already have: a LinkedIn URL, a username, a company domain. Reverse email lookup covers the case where all you have is an email address, pulled from a form fill, a support ticket, a CSV export from an event, or a CRM record that never got enriched, and you need to know who that person is: their name, current title, employer, and work history.
This is a genuinely different problem from profile enrichment. A LinkedIn URL uniquely identifies a person by construction. An email address does not identify anyone unless something has already linked it to a professional identity, so the whole category depends on cross-referencing an email against whatever signals exist that make that connection.
What a request and response actually look like
The interface is intentionally the same shape as looking someone up by a LinkedIn identifier, so it slots into a codebase that already calls a profile enrichment endpoint without a second data model to handle.
curl -X POST https://kooperativa.io/api/v1/resolve-email \
-H "Authorization: Bearer ik_live_..." \
-H "Content-Type: application/json" \
-d '{"email": "jane.doe@acme.com"}'A match returns the same record shape as a direct profile lookup, full work history, education, current title and company, not a stripped-down preview. A miss returns a plain 404. Nothing about the interface distinguishes "this email has no professional footprint anywhere" from "this email is tied to a real profile we have not indexed yet", because from the caller's side those two cases warrant the same next action: move on, and check again later if the identifier matters enough to revisit.
Hit rates are not one number
Any reverse email lookup provider that quotes a single match rate is describing one test list, not your list. The strength of the email-to-identity association varies enormously by context, and the gap between the best and worst case is wide enough that a single headline number is close to meaningless on its own.
- Corporate email at a large, well-established company: the strongest case, often 30-40% for individually-sampled addresses, since large companies tend to have more indexed professional activity per employee.
- Corporate email at a smaller or less digitally visible company: meaningfully lower, commonly in the 10-20% range.
- Personal email addresses (Gmail, Outlook, and similar): the weakest signal by far, typically single digits, because a personal inbox has no inherent tie to a professional identity unless the person has explicitly connected the two somewhere.
What this is not useful for
Reverse email lookup answers "who is this person professionally", not "give me contact details for this company's employees." It will not turn a list of guessed addresses into a list of confirmed people, and a provider promising a high hit rate on cold, unverified addresses is describing a different, much narrower slice of email than the one on your list.
It is at its best on emails you already have some reason to believe are real and active: form submissions, support tickets, event registrations, existing CRM contacts you are trying to backfill context for, not addresses generated by pattern-guessing a company's domain.
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