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Solutions · Employee Retention

Keep the people you can't afford to lose

Identify attrition risk early with AI-driven predictive analytics, understand exactly what's driving people out, and retain top talent with data-backed actions that land before the resignation letter does.

  • AI attrition-risk prediction per team
  • Driver analysis behind every risk flag
  • Retention playbooks for managers
  • Regrettable-loss tracking over time
The Vadal.ai mobile app home screen
Product pillars:Workforce Intelligence · Predictive Analytics · Action Planning

See it coming

Attrition warns you, if you're listening

Resignations look sudden but rarely are. Vadal.ai reads engagement trends, sentiment shifts, lifecycle signals and workload patterns to flag at-risk teams and segments weeks before the exit interview, with the contributing factors spelled out.

  • Risk flags with explainable contributing factors
  • Team and segment heatmaps, updated continuously
  • Early-warning alerts routed to the right manager
app.vadal.ai · Analytics
The Analytics screen in the Vadal.ai product

Act on it

From risk flag to retention play

A prediction without a plan is just anxiety. Every risk flag arrives with recommended interventions, from workload rebalancing to growth conversations, assigned, tracked and re-measured so you know what actually kept people.

  • AI-recommended retention actions per driver
  • Stay-conversation guides for managers
  • Impact tracking against regretted attrition
The Vadal.ai mobile app home screen

The impact

What good looks like with Employee Retention

6 wks

average early warning before regretted exits

28%

lower first-year attrition

22%

reduction in regretted attrition overall

Illustrative sample data, to be replaced with Vadal's verified proof before launch.

We used to find out why people left in exit interviews. Now we find out six weeks earlier, while there's still time to do something. Regretted attrition is down by a fifth.
PRPriya RamanHead of People Analytics, Cobalt Finance

FAQs

Questions, answered

AI models read patterns across engagement scores, sentiment, lifecycle feedback and behavioral signals that historically precede exits in your organization. Flags come with contributing factors and confidence levels, never a black-box score, and anonymity thresholds still apply to everything underneath.

Risk is surfaced at team and segment level to protect privacy while still being actionable. Managers see where risk is concentrated and why, enough to have the right conversations without surveillance.