Case Study - Workforce Intelligence — From HR Data to Decisions

Overview

Most organisations run their people on two systems — an HRIS for the formal record (employees, leave, performance, promotions) and Microsoft 365 for the actual day's work (calendars, meetings, email, collaboration). HR teams know what's recorded about their people; operators know what's happening with their people; the two pictures rarely meet, and when they do it's after the moment for action has passed. PeopleTrak treats both as inputs to a single workforce signal model, normalised into one view of the individual, the team, and the organisation.

PeopleTrak ships as four connected surfaces calibrated to who is using it and what decision they need to make: an executive dashboard surfacing attrition risk, talent density, promotion readiness and burnout patterns with AI-generated narrative briefs; an employee performance view that explains the signals about each person and proposes prioritised growth actions; a hiring workflow that creates structured role profiles, sources LinkedIn candidates and inbound applicants, and ranks each one against weighted JD / JS / KPI criteria with a transparent score breakdown and an 80%+ match threshold; and a chatbot that lets any audience query the same intelligence layer in natural language.

Data trustworthiness is treated as a first-class deliverable. Before any insight ships, the platform builds a validated ingestion foundation — every record traceable back to its source system, every sync logged, every transformation auditable. Descriptive analytics layer on top, then advanced insight engines, then predictive models, then the conversational layer. The intelligence is intentionally hybrid: deterministic rule-based scoring for the numbers that have to be auditable line by line, an AI layer on top to interpret, explain, and converse — with hard guardrails that every flag cites the evidence that drove it.

What we did