swan-gtm Use this when one hire worked and you want more like them. It produces a ranked shortlist where every score decomposes into title, skills, seniority, and location, so a hiring manager can argue with it.
The play
Take one exemplar, not a job description. A JD describes what someone wrote down. A person who is actually good in the role describes what worked. Start from the exemplar's real current title, skills, and location.
Push filters into the search, not into post-processing. Role family, country, city, and current-role-only belong in the query. Filtering a large result set client-side wastes lookups and hides how narrow you actually were.
Start broad, then tighten once. Role family plus two skills plus a country is a good opening net. Stacking an exact title, an all-skills-match, and a city returns zero and looks like "nobody exists" when it is really a bad query.
Decide whether to enrich. Without enrichment you get a cheap keyword-ordered list. With it you get skills, education, and geography per candidate, at one extra lookup each. Enrich 20, not 200, until the shortlist direction is confirmed.
Score transparently out of 100:
- Title match, 40 — exact title 40, same role family at any seniority 25, different family 0.
- Skills overlap, 30 — shared skills over the exemplar's skill count, times 30, compared on normalized names.
- Seniority, 15 — same level 15, one level off 7, further 0. Infer from title prefix: Senior, Staff, Principal, Head of, VP.
- Location, 15 — same city 15, same metro 10, same country 5.
Drop the exemplar before ranking. Search returns them, they score 100, and it makes the list look broken.
Return a table, not JSON. Name, current title, company, location, score, and the one line explaining the score.
What good looks like
- The strongest sourcers treat a zero-result search as information about the query, not the market, and can say which filter they loosened and why.
- The mediocre version returns fifty unranked names and calls it a pipeline, leaving the hiring manager to do the actual sourcing work.
- A score is only useful if it decomposes. "77" means nothing; "77: exact title, 6 of 9 skills, one level junior, same metro" is a conversation.
- You know it is good when the hiring manager disagrees with a specific score for a specific reason. That means they can see the reasoning.
Rules
- MUST remove the exemplar from their own results.
- MUST show the score breakdown, never a bare number.
- MUST start broad and tighten deliberately, one filter at a time.
- NEVER infer salary, notice period, or willingness to move. That data is not in a public profile.
- NEVER present a keyword-ordered list as a ranked one when enrichment was skipped. Say the scores were not computed.