AI Workforce Intelligence & Retention System

Core Logic

Coordinates 14 specialized agents across 5 execution phases. Initializes the workflow engine, validates mission parameters, allocates compute resources, and manages agent handoffs. Uses GPT-4 Turbo with 99.8% success rate for task scheduling and error recovery. Generates final intelligence reports by synthesizing outputs from all agents.

Core Logic

Connects to 9 enterprise data sources including Workday HCM (2,847 records), Radford Compensation (15,420 benchmarks), Culture Amp surveys, Lattice performance data, Slack analytics (124,500 messages), and LinkedIn insights. Performs incremental sync with schema mapping and achieves 98.5% data quality scores.

Core Logic

Processes 52+ employee signals using transformer-based NLP with 97.8% accuracy. Performs feature engineering to create engagement scores, compensation ratios, and tenure metrics. Normalizes data across sources, applies anomaly detection to flag outliers, and generates data quality scores for each employee profile.

Core Logic

Analyzes workforce patterns using XGBoost + LSTM ensemble models. Performs cohort analysis comparing tenure bands and departments, benchmarks against industry data, detects emerging trends like compensation gaps or manager relationship issues. Identifies patterns such as 'Engineering compensation 12% below market' driving 42% of flight risk.

Core Logic

Generates flight risk predictions using Gradient Boosting + Neural Net ensemble with 94.3% accuracy. Computes individual risk scores (0-100) with confidence intervals, classifies employees into LOW/MEDIUM/HIGH/CRITICAL risk tiers, and provides estimated departure windows. Model trained on 1.8TB of historical data.

Core Logic

Uses SHAP (SHapley Additive exPlanations) analysis to identify primary turnover drivers. Quantifies feature attribution showing compensation (42%), manager relationship (28%), and career stagnation (18%) as key factors. Discovers correlations like 'Employees with <2 manager 1-on-1s/month have 2.3x higher flight risk.'

Core Logic

Matches personalized interventions to each employee's specific risk drivers using collaborative filtering + reinforcement learning. Evaluates each intervention type (compensation adjustment, manager coaching, promotion acceleration), predicts success probability, and ranks recommendations by expected impact.

Core Logic

Ensures bias-free predictions using Fairness ML + Rules Engine with 99.9% success rate. Runs demographic parity checks, validates disparate impact ratios (0.92 above 0.80 threshold), enforces GDPR data minimization with PII encryption, maintains audit trails, and generates 'Right to Explanation' SHAP-based documentation.

Core Logic

Uses GPT-4 + custom templates to generate natural language executive summaries. Synthesizes findings from all 14 agents into executive briefs with key highlights, critical alerts, and success stories. Creates visualization-ready data, executive action items with deadlines, and generates confidence scores (94.3%) for all recommendations.

Core Logic

Analyzes wellbeing metrics using BERT + sentiment analysis. Detects burnout risk patterns by monitoring work hours (52hr/week), meeting load (28hr/week), weekend work patterns, and vacation utilization rates. Identifies employees with consecutive high-work weeks and generates wellness program recommendations.

Core Logic

Uses Knowledge Graph + NLP to map current skill inventory against market demand. Identifies critical skill gaps (AI/ML 67% shortfall), calculates future-readiness scores, and generates personalized learning paths. Integrates with LinkedIn Learning and Coursera to recommend upskilling programs aligned to 2025 roadmap requirements.

Core Logic

Monitors diversity metrics with Fairness ML + statistical analysis. Tracks gender/ethnicity distribution, calculates pay equity scores (94/100), measures psychological safety and belonging scores, and identifies risk areas like 'Engineering team 72% male, below industry best practice.' Generates DEI improvement recommendations with cost and timeline estimates.

Core Logic

Uses Graph Neural Networks to map leadership pipeline and assess successor readiness. Identifies critical succession gaps ('VP Engineering has no ready-now successor'), evaluates high-potential candidates across leadership competencies, creates development plans with milestones, and calculates risk scores when incumbents have high flight risk.

Core Logic

Analyzes labor market trends using time series + web scraping from Glassdoor and Levels.fyi (245K data points). Monitors competitor compensation ('Company A offering 15% higher for senior engineers'), tracks talent availability by skill area (ML: scarce, 65 days to hire), and forecasts salary trends with YoY change projections.