Playbooks · Recipe
Resolve each email to a LinkedIn profile URL with find_linkedin_url_by_email, then enrich that URL with enrich_profile. Two calls, two charges. Neither one is a waterfall endpoint, so both bill on any 2xx — including a lookup that resolves nothing.
Last updated September 22, 2026
Pattern: find_linkedin_url_by_email → enrich_profile
Credit math: Worked example at stated counts: 500 emails. find_linkedin_url_by_email 500 calls at 4 = 2,000 credits, charged on all 500 even though only 300 resolve. enrich_profile on those 300 at 1 = 300 credits. Total 2,300 credits for 300 enriched profiles. 4 credits per find_linkedin_url_by_email call, 1 credit per enrich_profile call
ROI math: About 7.7 credits per enriched profile at a 60% resolve rate, and the resolve rate is what moves that number — at 40% the same 500 emails cost 2,200 credits for 200 profiles, about 11 each. Clean the list before you spend. Convert credits at your tier rate on /pricing.
Owned internally by: Whoever owns the CRM backfill — usually RevOps, sometimes a data engineer with a deadline
Best for: Backfilling signup lists, webinar registrations and old CRM records that have an email and nothing else
# 1. email → LinkedIn URL. Bills on any 2xx, resolved or not.
curl -X POST https://api.richapi.ai/api/v1/find_linkedin_url_by_email \
-H "x-api-key: $RICHAPI_KEY" \
-H "Content-Type: application/json" \
-d '{"email": "dana.okafor@acme.com"}'
# 2. enrich the URL you got back
curl -X POST https://api.richapi.ai/api/v1/enrich_profile \
-H "x-api-key: $RICHAPI_KEY" \
-H "Content-Type: application/json" \
-d '{"url": "https://www.linkedin.com/in/dana-okafor"}'
Take these 40 signup emails, resolve each one to a LinkedIn profile,
and enrich the ones that resolve. Tell me the current title and company
for each, and list the emails that did not resolve so I know what I
paid for and got nothing back.
Sign up at app.richapi.ai and copy your key.
25 free credits, no card.
Strip the obvious dead weight from the list first — role addresses, test accounts, free-mail domains you do not care about. Every one you leave in is a full-price call.
Call find_linkedin_url_by_email per email and read the result at the key "LinkedIn Profile URL", with the spaces and capitals.
Call enrich_profile with that URL.
It takes a LinkedIn URL, never a domain and never an email.
Write both raw responses to storage before you transform them, so a parsing bug does not cost you the whole run again.
`find_linkedin_url_by_email` returns the URL under the literal key `"LinkedIn Profile URL"`. Spaces. Capital letters. Inside `data`. Every client library, every ORM habit, every autocomplete in your editor will push you toward `linkedin_profile_url`, and that lookup returns `undefined` in JavaScript and raises nothing at all if you used `.get()` in Python. The call succeeded. You were billed. Your row is empty. Then you spend an hour looking for the bug in your retry logic. ```python # wrong — no error, no data, full charge url = resp["data"].get("linkedin_profile_url") # None, every time # right url = resp["data"]["LinkedIn Profile URL"] ``` ```javascript // wrong const url = resp.data.linkedin_profile_url; // undefined // right const url = resp.data["LinkedIn Profile URL"]; ``` Normalise it once, at the edge of your code, and never touch the raw key again. If you are writing an assertion anywhere in this pipeline, make it this one. **[Get 25 free credits — no card](https://app.richapi.ai)**
There is no free miss in this chain. `find_linkedin_url_by_email` and `enrich_profile` are flat per-call endpoints and they bill on any 2xx, including a response that resolves nothing. The free-miss behaviour belongs to the three multi-provider waterfall endpoints — `email_finder`, `email_verifier` and `phone_finder` — and none of them is in this recipe. On a 500-row run that difference is 800 credits spent on emails that resolved to nobody. It is not a defect, it is the pricing model, and the honest way to work with it is to shrink the input list rather than hope the resolve rate carries you.
500 emails in, 300 resolving to a profile. | Step | Count | Rate | Bills on an empty result? | Credits | |---|---|---|---|---| | `find_linkedin_url_by_email` | 500 calls, 300 resolve | 4 / call | **Yes** | 2,000 | | `enrich_profile` | 300 calls | 1 / call | **Yes** | 300 | Rates used above: 4 credits per `find_linkedin_url_by_email` call, 1 credit per `enrich_profile` call. 2,300 credits, 300 profiles, roughly 7.7 credits each. Drop the resolve rate to 40% and the same list costs 2,200 for 200 profiles. The lookup step is four times the price of the enrichment, so every junk row you delete before the run saves four credits and every one you leave in costs four. Per-credit rates vary by tier, so convert on [the pricing page](/pricing).
**Role addresses.** `info@`, `sales@`, `support@`, `careers@`. Nobody's LinkedIn profile is attached to a shared inbox, and each one is a full-price call that resolves to nothing. Filter them with a regex before the run, not after. **Personal free-mail addresses.** A gmail address resolves far less often than a corporate one. Worth a separate, deliberate batch so you can measure its resolve rate and decide whether it earns its credits, rather than burying it in an average. **Feeding a domain to `enrich_profile`.** It takes a LinkedIn profile URL. A domain, a company page URL or an email will not do, and the failure is not always loud. **Re-running the whole list on retry.** A bad deploy that dropped the results does not entitle you to a refund on the second run. Persist the raw response for every call as it lands.
Going the other direction — you have a profile and need the address — is [waterfall email then verify](/use-cases/waterfall-email-then-verify), where the misses are free. If your input is a company rather than a person, [bulk enrich without seats](/use-cases/bulk-enrich-without-seats) covers the volume shape. To wire this into a research agent instead of a batch job, see [agent-driven account research](/use-cases/agent-driven-account-research). The endpoint pages for [email to LinkedIn](/api/email-to-linkedin) and [person enrichment](/api/person-enrichment) carry field-level detail, and [the Proxycurl comparison](/alternatives/proxycurl) covers the migration if that is where your list currently lives. The rest sit on [the use-case index](/use-cases).
**Why is the key formatted like that?** It comes back from the provider that way and we pass it through rather than silently reshaping a payload. Pin it in one adapter function and move on. **What happens when the email does not resolve?** You get a 2xx with no URL and you are charged for the call. Only `email_finder`, `email_verifier` and `phone_finder` return `billed: false` on a soft miss. Non-2xx responses never bill, on any endpoint. **Can I batch this?** `enrich_profiles_bulk` takes up to 50 LinkedIn URNs per call and bills per result. The lookup step has no bulk form today, so it stays one call per email. **How fresh is the enriched profile?** It is fetched live at call time rather than read from a stored index. A profile edited this morning reflects that; a profile nobody has touched since 2021 is 2021 data, and that is the profile's problem rather than ours. **Do I need a credit card?** No. 25 free credits on signup.
Run twenty emails through both calls and look at your resolve rate before you commit a list of five thousand. **[Get 25 free credits](https://app.richapi.ai)**