FeaturesDashboard and calibration
Learn and govern

The system improves with each decision you make, and shows how.

Rejection and shortlist reasons aggregated, distribution by dimension, and the real outcome of hires at 90 days and 12 months feeding back into the next analyses.

The problem

Why this exists

How it works today

You reject ten candidates for the same reason and, on the next role, the system suggests exactly the same profile. The learning stays in your head, and leaves when you change tools or jobs.

How it works with EVO

Each decision records the reason in structured vocabulary, and that aggregate enters the context of the next analysis. What you learned becomes an asset of the operation.

How it works

Dashboard and calibration from the inside

1The short loop

Your rejections teach the next screening.

When you reject or approve someone, you tag one to three reasons from a fixed taxonomy, plus a free-text note. It is quick to fill in and comparable across people and roles.

The aggregate enters the analysis prompt at four levels, from the most specific to the most general: that role, that client, that job family, and the global one, with a minimum sample for each level to count.

Each assessment shows a chip saying how many decisions calibrated it, and keeps the exact block of context used. You can audit why it thought what it thought.
How EVO learned from you
Aggregate base: 247 screening decisions + 38 flags in the last 90 days.
Top rejection reasons
evidence-weak34%
seniority-below22%
skills-mismatch18%
salary-likely-above13%
Top shortlist reasons
trajectory-strong28%
evidence-strong19%
skills-strong17%
culture-fit-strong12%
Real outcome × your decisions

"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.
2The long loop

And what happened to the people who were hired.

The outcome of the process comes back into the system, and then the 90-day and 12-month check-ins on the person hired. That allows the only question that really matters: was the recommendation actually good?

It is the slowest data to accumulate and the hardest to copy: it depends on years of recorded decisions, not on technology.

How you compare with EVO

"You shortlist on trajectory about 3× more than on evidence: EVO tends to balance those two axes. It may be strategy (hunting for talent on a fast climb) or a pattern worth watching."

YouEVO
Trajectory
Evidence
2.8×
Hard skills
aligned
Seniority

ⓘ A reading, with nothing to click. EVO carries this context into calibrating the next assessment, and sets each of your patterns against the real outcome of the hires.

Under the hood

Learning without becoming a black box

A system that learns and does not explain is a system that goes wrong without anyone noticing. These are the restraints.

minEach calibration level requires a minimum sample; below that, it falls back to the more general level
snapThe exact context used is stored alongside the analysis, visible on the screen
clampThe semantic adjustment by similarity is limited to a conservative band
rawThe score before the adjustment is preserved, for you to compare
orgThe learning belongs to your organization: nothing you teach leaks to another client
What comes next

How it fits into the rest of the journey

Bring a role that is hard to fill

It takes a real role to see EVO work, not a canned example.

Start using it