A score across four dimensions, each with the text that supports it. A reading of the career path, strengths, concerns and gaps, plus the probability that the person accepts an offer.
The tool returns "87% match" and you have no idea where it came from. If the number is right, you cannot defend it; if it is wrong, you cannot correct it. A score without evidence is a guess with a decimal point.
Each dimension comes with the paragraph that justifies it, citing what in the profile supports it. You disagree on the basis of something concrete, and the disagreement becomes learning.
The separation matters. Someone can have the right skills and insufficient seniority; or an excellent trajectory and no evidence of impact. A single number hides exactly the information that decides.
The evidence dimension is the most revealing: it measures how much of what the person claims is backed by what they describe having done.
Most claims are backed up, but the discovery impact is described without numbers, so it loses points here.
Progression with growing scope, not just growing titles.
Discovery mentioned without an impact metric, so the result cannot be confirmed.
EVO estimates what the person probably earns today and crosses it with what the role offers. An excellent candidate earning twenty percent above the ceiling is a different conversation.
Signals add up, such as time in the current role, which indicates a natural window for a move, and compatibility of size and culture between the companies.
monthly · estimated, CLT contract
range published in the JD
A market estimate, not something the candidate declared. It crosses your own organization’s history with intelligence by title, company and seniority. It is never exposed in outreach.
Model-generated text is convincing even when it is wrong. These are the restraints.
A range estimated by company, title and seniority, with a stated confidence level. Feeds the acceptance prediction.
See inside →OutreachOutreach with approvalInvitation, message or InMail in your voice, citing something real from the career, sent from your account, after your approval.
See inside →LearningDashboard and calibrationRejection and shortlist reasons, distribution by dimension and the real outcome at 90 days and 12 months feeding back into the analyses.
See inside →It takes a real role to see EVO work, not a canned example.
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