Spot flight risk, measure engagement, and check pay equity with AI reading your HR data.
Most HR teams already drown in numbers — headcount by department, turnover by quarter, time-to-fill, survey scores rounded to a decimal. The reports get built, circulated, and then sit. This is about closing that gap.
Most people-analytics work stalls after the first burst of effort. You run a flight-risk score in March, present it, get nods — and the rhythm never forms. A playbook fixes the cadence, not just the analysis.
The resignation that surprises you is rarely the one that should. The signals sit there for months: a top performer who stops volunteering for stretch projects, whose one-on-ones get shorter, whose pay slips a notch below market while recruiters start circling.
Engagement carries real weight in your scoring, yet for most teams the only data on hand is a single annual survey number — a 3.9 out of 5 that lands in a slide deck, gets a nod, and changes nothing. A score that can't tell you what to do next isn't a measurement.
A finding is not a result. You can hand leadership "senior engineers on the platform team are 3x more likely to leave," "compensation transparency scores dropped eleven points," and "four analysts sit more than $7,500 below their pay band midpoint with no documented reason" — and still nothing changes.
Your flight-risk model returns its first ranked list. Twelve names sit at the top, sorted by probability of leaving within ninety days.
Rate each data source before you trust it. Score every criterion from 1 (poor) to 5 (strong) and average them; anything below 3 needs cleanup before AI touches it. Completeness asks how many records have the field filled in — blank salary fields sink a pay audit.
Take your messiest source and run it through the full sequence — budget an hour, since the first file always takes longest. Export to CSV, run the structural pass, and list every problem without fixing yet. Then run the values pass on categorical columns, standardizing labels, IDs, dates, and blanks.
Now you tell the model how to read the file. A good data-interpretation prompt does four things: it states what the file is, defines each column literally, names the blanks-and-format rules, and forbids guessing. Write it once for a source and reuse it every time you analyze that source.
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