Your whole review season, read and ready to act on.
An organization-wide read of every performance review — who's coaching well, where scores are inflated, and what your people are actually saying. The whole picture in about a minute.
Summary: The organization demonstrates strong alignment with its four core values, with a culture of servant leadership across campuses — though feedback quality is uneven and some topics consistently lack written comments.
Every year, your church runs hundreds of staff reviews. Then what?
Usually they’re signed and filed. Here, every one gets read.
How your whole staff scored.
One number for the entire organization.
What’s done — and what’s overdue.
The whole season’s progress in one number.
Where everyone landed.
High, low, or right in the middle.
The headline, at a glance.
Average, completion, and spread — then we go deeper.
Every stage, in one bar.
From upcoming to signed-off.
First, the expected.
What a balanced review season should look like.
Then your data, on top.
See the gap? Your scores land about +10 above it — that’s the inflation.
Not just stored — understood.
Every score checked, every manager coached, every comment read — all your reviews, turned into a plan you can act on.
Behind? Nudge them all.
One click emails every manager with a review still outstanding.
How your scores are distributed.
Your real spread, read against a healthy baseline.
Who's scoring high.
The consistently generous scorers — surfaced so you can coach, not catch.
And what to do next.
The AI turns the pattern into one concrete step — with the numbers behind it.
Are managers writing it down?
Comment rate, coverage, and sentiment — with a one-line read on the whole season.
What they wrote about.
The recurring themes across thousands of comments, surfaced for you.
Where it's strong — and where to look.
What's landing well, and which topics need a second pass.
Same data. Narrower lens.
From the whole organization, down to one campus, and one department inside it.
Start with the whole organization.
Spots the pattern across every review — and who's driving it.
A fix, not just a flag.
From AI analysis to a concrete next step — not just another number to interpret.
Where to look first, org-wide.
AI reads every review to rank where attention is needed most.
Not every pattern matters.
The ones that do surface here — scored, explained, fix attached.
Score inflation, suggested fix attached.
Coach, not catch. Calibration training schedules itself when you accept.
Every campus, scored and ranked.
One consistent yardstick across campuses — comparable at a glance.
A narrower view to walk through.
Same screen, just the ones that need attention.
Which are pulling the average up — or down.
Every department ranked against the org — the outliers, both ways, stand out.
Cultural profile vs the org.
One department's values and competencies, measured against the org.
From “we should” to “do this, here.”
Each concern becomes a specific, assignable next step.
Backed by the cells that triggered it.
Hover any recommendation to light up the exact cells behind it.
Same data, four altitudes.
Organization up front — Campus, Department, and Action are each one click deeper.
Evaluate the evaluators.
Your reviews are only as good as the managers writing them — so the AI grades the managers too.
Every manager, on one scale.
Feedback quality across the whole org — comparable at a glance.
Ranked on the reviews they write.
Feedback quality, scoring accuracy, engagement — scored for every manager.
Who needs help — surfaced, not searched.
The tool flags the managers to support first, so nothing slips.
Five signals, one benchmark.
How this manager reviews — scored against the whole org.
High marks, light on proof.
The scores are generous; the accuracy and engagement behind them aren’t — your coaching targets.
From patterns to a coaching plan.
AI reads every comment, names the patterns, and drafts the next steps.
Every review, traceable.
Drill from the manager down to each rating they wrote.
What the writing supports.
The estimate weighs each score against its written reasoning — a high mark with thin justification drifts back toward the middle.
Measured, coached, traced to every review.
One manager’s whole leadership report — the org-wide view drills straight into it.
The manager demonstrates inconsistent feedback quality across responses — some comments are clear and supportive, others vague or light on detail. There’s an opportunity to improve comment specificity, especially for lower-scored areas like teamwork, to give more actionable guidance.
More than a score.
Open any review and see the AI’s full read — every value, every comment.
Every review, in one place.
Every completed review across the org — the AI’s estimate beside each score given.
A second read on every score.
The AI’s estimate sits beside each grade — the gaps surface on their own.
Open one — see what the AI sees.
Its read of this exact review: feedback quality, scoring accuracy, engagement.
Every value, every competency.
The full rubric for this review — each competency rated against expectations.
Not just the score — the justification.
The AI grades each comment on quality and how well it backs the rating.
One review, fully read.
Every value, every competency, every comment — the AI’s complete read.
See it on your own reviews.
Every walkthrough starts with your data — no slideware.