— Talent & Leadership
Learns from who has already left and who stayed to score retention risk across the workforce, showing what actually differs between the two groups and the retention plan that follows.
1–2 weeks → ~15 minutes
For a full population, not a sample
No coding required
— USE CASES
Provides a prioritised watchlist of employees with elevated estimated attrition risk, with explanations of the factors contributing to each score. Supports informed review rather than assumptions about individual intentions.
Compares employees who leave with those who stay, highlighting differences in available measures such as tenure, pay, engagement and time since promotion. Distinguishes observed patterns from assumptions about the causes of attrition.
Brings risk estimates and workforce patterns into a retention plan grounded in the statistical findings. Gives decision-makers a shared basis for discussing where support and further investigation matter most.
Examines differences across departments and managers where those details are available. Helps leadership identify concentrations of risk that warrant closer attention.
— HOW IT BEHAVES
The mechanics behind this specific template — what it reads, what it calculates, and where a human stays in the loop.
Each row of your export is processed on the same basis, so no risk is skipped however long the table is.
Findings on the risk universe in scope are written up as a document that reads like professional output, with each claim tied back to a risk.
Detail rows are summarised into the grouped view of the risk universe in scope without losing the underlying risks.
Retention decisions often rely on anecdote, isolated engagement results or assumptions about why people leave. Targeting retention attention is the typical trigger — provides a prioritised watchlist of employees with elevated estimated attrition risk, with explanations of the factors contributing to each score. Supports informed review rather than assumptions about individual intentions. Get it right and the conclusion holds up in the room; get it rushed and it gets picked apart. Either way it costs roughly 1–2 weeks of experienced attention.
As a Skill, the work is already sequenced. You bring the evidence, and the run produces prioritised employee retention watchlist plus statistically calculated attrition risk scores on a 0–100 scale. What sits between input and output is the codified method: thresholds, sequencing and the points where a human confirms a call — all of it visible and editable in the Skill. The practical effect: 1–2 weeks of manual work becomes a ~15 minutes run, held to an identical standard on the tenth engagement as on the first.
Attrition risk radar exports as a structured SKILL.md file and is MCP-ready, so the same method runs in ChatGPT, Claude, Copilot or your own AI products. Adapt it to your methodology, and the intelligence stays yours — not locked to one vendor.
Builds evidence-backed employee skills profiles within HR systems, mapping documented experience to the organisation’s skills catalogue and assigning proficiency levels using consistent rules.
Assesses how clearly people explain their technical work against a consistent four-level rubric, producing a human-approved report with quoted evidence behind every score.
Reveals how organisational culture is lived through anonymous employee accounts, producing an evidence-backed diagnostic of shared themes, differences between functions and gaps between values and practice.
Turns a high-potential's assessment evidence into an acceleration plan weighted to real experience and exposure, naming the stretch assignments, mentors and programmes that will compound fastest.
Builds a calibrated, reusable benchmark from source material so every later screen, shortlist or review is graded against the same bar.
Screens candidate evidence against a saved Blueprint, applying the same calibrated bar and traceable reasoning to every applicant.
