AIHR

The challenge

Before

A 500-employee company's HR team was making decisions about hiring, performance, and retention based on intuition and anecdote — not data. Attrition was high but the causes were unknown. Employee engagement surveys were run annually and results were never acted on. HR reporting to leadership was a manually compiled slide deck produced once a quarter. There was no early warning system for disengagement or flight risk.

• HR decisions based on intuition — no analytics or data-driven insight
• Annual engagement surveys with no action tracking or trend analysis
• Quarterly HR report manually compiled by the HR team — always out of date
• No attrition prediction or early warning for flight risk employees
• No visibility on which departments, managers, or roles drove the most turnover
• Performance data siloed in an HRIS with no analytics layer
• No benchmark comparison — leadership had no context for whether metrics were good or bad

The solution

What we built

We built an HR analytics platform that pulls data from the company's HRIS, engagement survey tool, and performance management system into a unified analytics layer. Leadership sees attrition rate by department, tenure band, and manager. An ML-based flight risk model scores employees based on engagement signals, performance trends, and tenure patterns. Engagement survey results are tracked over time with department-level drill-downs. HR reports to leadership are generated automatically on a monthly cadence.

How the system flows

1. HRIS Data + Engagement Surveys + Performance Records→Unified HR Data Layer
2. Attrition Analytics→Department Breakdown→Manager View→Tenure Analysis
3. Flight Risk Model→Employee Risk Score→HR Alert→Intervention Tracking
4. Engagement Survey→Response Collection→Trend Analysis→Department Drill-down→Action Items
5. Monthly HR Report→Auto-generation→Leadership Distribution→Year-over-year Comparison