The salary asked against the market estimate, funnel reach requirement by requirement, and the risks you decided to take, with the justification recorded and versioned at every round of negotiation.
The conversation about feasibility is a matter of opinion: "I think that salary is low", "I think we can find someone". With no numbers, whoever speaks loudest wins. The cost of being wrong shows up a month later.
Each requirement shows how much it narrows the funnel, and the salary appears next to the local market estimate. The discussion stops being about opinion and becomes about what you are willing to give up.
Each demand appears with the reach left after it. It is common to discover that a single requirement, almost always a niche domain, accounts for nearly all of the narrowing.
The salary provided appears against the local market estimate for that title, seniority and city, with the delta highlighted.
Fillable as it stands, with a caveat. The below-market range is sustainable because the package includes remote work and autonomy, but if it does not close in 45 days, salary is the first variable to revisit.
When the real funnel starts running, the report compares what was predicted with what happened. It is the only way for the estimate to improve over time.
And there is an important guard here: with an insufficient sample, the number is not displayed. It is not a warning in fine print, the field simply does not exist.
"Of your shortlists based on trajectory, 71% became hires that lasted past 90 days: the pattern that looked like bias is delivering. Shortlists based on evidence converted at only 45%."
Base: 17 closed processes with a recorded outcome · N is still small, read it as a trend.The product’s strongest pattern, and the hardest to copy: the guard lives in the type system, not in an instruction to the model.
It is the difference between asking the model not to make things up and making invention impossible.
Interviews whoever opened the role, challenges what doesn't add up and only then writes, in 13 sections, with 14 problem detectors.
See inside →SalariesSalary intelligenceA range estimated by company, title and seniority, with a stated confidence level. Feeds the acceptance prediction.
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.
Start using it