Exit interviews are conducted by the organisation being left, in the final week, by someone the leaver may need a reference from. What they produce is "a better opportunity" and "for personal growth" — polite, unfalsifiable and useless. Every HR function knows this and most still build retention strategy on it, because it is the only thing that looks like data.
What is informative is structural and already recorded. When people leave in their tenure: a cluster at eight months is a hiring or onboarding problem, a cluster at three years is a progression one, and they need completely different responses. Whether leaving concentrates around a team, a change, or the period after a specific event. Whether leavers had been passed over, had no internal move available, or sat at the bottom of a pay band while new hires came in above them.
That last one is the finding organisations most reliably miss: paying new people more than existing ones for the same work is invisible until someone finds out, and then it costs several people at once.
This agent reports drivers with the evidence and what each would cost to address. It analyses patterns and never individuals: predicting who will leave leads to managers treating people differently on a model's say-so, which is a worse outcome than the attrition.
Rather than a word chosen to keep a reference intact.
Onboarding and progression are different problems with different fixes.
Replacement priced against the fix.
A cluster at eight months is onboarding. A cluster at three years is progression. The stated reason distinguishes neither.
Paying new people more for the same work is invisible until someone finds out — and then it costs several people at once.
Whether people who left had anywhere to go. Often the whole story, and never in an exit interview.
Around a team, a change, or the weeks after a specific event.
Recruitment, notice, ramp and lost output, so retention competes for budget properly.
A leaving-risk score leads to managers treating people differently on a model's say-so. That is worse than the attrition.
Why HR & People in particular. Exit interviews produce 'a better opportunity' — polite and unfalsifiable. The real driver is usually structural: new hires entering above existing people, or an internal move that was never available.
Runs unattended
Started by you or by an event, and it finishes on its own. Nothing waits for someone to be at a desk.
The same standard every time
The two-hundredth item is held to the bar the first one was. Consistency is the part people cannot sustain.
It cannot act on its own
Attrition Driver Analyst has no path to sending, spending or committing. That limit is why its output is safe to act on.
This agent runs server-side through the PROMIVO runtime. Each run is logged step by step and every tool call is permission-checked before it executes.
Read-only by design. This agent has no path to sending, spending, publishing or committing anything. Where that limit is the product, removing it would remove the reason to trust the output.
Demo dataIllustrative sample output, abridged.
{
"period": "12 months to Aug 2026",
"leavers": [],
"currency": "USD",
"suppressionThreshold": 5
}{
"escalate": true,
"disclaimer": "An analysis of your own records. No individual has been scored, predicted, named or identified; no manager has been assessed; groups below five are suppressed; protected characteristics and personal circumstances were not used; and a stated exit reason is reported beside the evidence rather than contradicted.",
"byTenureBand": [
{
"band": "0–12 months",
"leavers": 31,
"ratePercent": 24.1,
"interpretation": "Nearly a quarter of new joiners leave inside a year. That is a hiring, onboarding or expectation problem, and no progression fix touches it."
},
{
"band": "24–48 months",
"leavers": 22,
"ratePercent": 18.4,
"interpretation": "A second cluster with a different cause. These people stayed, then found no way up."
}
],
"concentration": [
"Two teams account for 41% of leavers while holding 19% of headcount. Reported as a group pattern, with no assessment of any manager."
],
"costOfAttrition": {
"total": 1840000,
"componentsMissing": [
"Lost output during ramp was not supplied, so the true cost is higher than this figure."
]
},
"escalationReason": "New hires are paid materially above existing people in the same roles, and first-year attrition is 24.1%, above twice the organisation rate.",
"groupsSuppressed": 6,
"payPositionFinding": "Leavers in the 24–48 month band sat at a median 34th percentile of their band. New hires into the same roles came in at the 61st. People discovered they were being paid less than the person hired beside them, and the exit interviews say 'better opportunity'.",
"statedVersusEvidence": "78% of exit interviews cite 'a better opportunity'. The structural evidence points at pay position and blocked internal movement. Neither is something a leaver says to the person writing their reference.",
"internalMovementFinding": "Of the 22 leavers in that band, 14 had applied for an internal move in their last year. Nine of those roles were filled externally.",
"suppressionThresholdUsed": 5
}No integrations required.
Where it concentrates and what it has in common.
Where existing people sit against new hires in the same role.
Whether leaving follows the change.
Retention with drivers and a cost, not a percentage.
$349/month
Billed monthly through your PROMIVO subscription. Cancel at any time.
Runs consume your plan allowance for agent executions and tokens. See plan limits.
No, and it refuses to. An individual leaving-risk score changes how managers treat people, on a model's say-so, usually to that person's detriment. It reports structural patterns.
It reads it and reports it beside the structural evidence, so you can see how far apart they are. That gap is usually the most instructive thing in the report.
It suppresses groups below the size you set and reports no individual. Where a team is too small to report on without identifying someone, it says so instead.
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