The useful comparison is not Cerna vs. LinkedIn Recruiter or Cerna vs. other AI tools. It's Cerna vs. the research your team is doing today — the work that takes days, varies with who has bandwidth, and rarely leaves a documented sourcing trail.
| in-house manual research | cerna | |
|---|---|---|
| Time per search | 3–5 days of researcher time | Same day; not gated on researcher availability |
| Cost per search | $1,200–$4,000 of loaded labor | $1,000 per search |
| Source documentation | Inconsistent; sourcing often implicit | Structural — named source per confirmed criterion |
| Quality consistency | Varies with experience, workload, function | Same sourcing + QA bar every run |
| Concurrent searches | Bottleneck — 3–4 before quality degrades | No bottleneck; parallel execution |
| QA layer | Depends on partner review before client calls | Separate adversarial pass every search |
| Section B (laterals) | Depends on remaining time and sector knowledge | Required in every delivery; recall pass runs every time |
| Who decides who to hire | The recruiter | The recruiter |
Honest comparison requires saying this clearly.
The senior partner's contacts and the researcher's accumulated sector knowledge aren't replicable from surface-web research. Cerna doesn't have access to the partner's private contacts or the researcher's memory of last year's near-misses.
A researcher who has run five searches in a function or geography carries information that doesn't exist in public sources. Cerna has no continuity between searches — each one is a fresh brief.
An experienced researcher knows, before the rubric is checked, that certain executives have reputational issues or aren't callable in a given context. Cerna doesn't know that.
These are real advantages. Cerna isn't a replacement for the researcher's judgment or network. It handles the upstream surface work — finding names, confirming credentials, sourcing every claim — so the researcher can apply expertise to the shortlist rather than spend it on the hunting.
In-house research often produces output where sourcing is implicit — the researcher knows why they said it, but the record doesn't say. Cerna's sourcing discipline is structural: every confirmed criterion cites a specific, locatable source.
A separate pass — clean context, explicitly looking for what the generation pipeline got wrong — reviews every claim before the recruiter sees it. Mis-attributed evidence, unsupported assertions, and vague sources are caught.
Section A (on-spec) and Section B (laterals with reasoning) are required in every delivery. A thin Section A with two strong laterals is a better product than a padded Section A with weak sourcing.
Researcher time is a fixed cost that doesn't scale down when search volume is light. Cerna is per-search — $1,000 when you need a search, nothing when you don't.
Cerna and the in-house researcher are not mutually exclusive. The researcher applies judgment to the shortlist Cerna produces; Cerna handles the upstream hunting. The researcher becomes the judgment layer, not the sourcing layer. The sourcing layer isn't where expertise lives.
LinkedIn Recruiter and similar tools give you access to search an executive pool. They don't evaluate executives against your spec, source the evidence behind each claim, run a QA pass, or return a formatted shortlist with documented rubric scores. Someone still has to do the research.
Cerna isn't the alternative to LinkedIn Recruiter. It's what you do with LinkedIn Recruiter — the research and qualification layer that turns pool access into a deliverable.
Other AI recruiting tools (SeekOut, Findem, Beamery, and similar) are built to surface executives and return result sets. They are products the recruiter operates; the researcher still does the qualification work downstream. They aren't built around per-claim source documentation or adversarial QA. Different product.
The first search is on us. Same pipeline. Same sourcing discipline. Same QA bar as a paid one.
No credit card. $1,000 per search after that.
