Arushi Srivastava@Arushi

TalentIQ transforms fragmented workplace signals into proactive intelligence that helps managers support their teams before problems escalate. The system addresses three critical gaps: burnout detected too late, performance degradation missed, and high-potential talent overlooked. The platform currently uses synthetic demonstration data representing 12 employees across 3 teams, each with 14 days of activity across 24 signals spanning Delivery (task completion, deadlines, velocity), Engagement (meeting load, focus time, after-hours work), Collaboration (cross-team interactions, feedback, support), and Growth (learning, stretch assignments, initiative). The architecture supports real-time integration with Slack for live signal ingestion. The system employs a hybrid intelligence approach that balances deterministic scoring with AI sophistication. Days 1-13 use rule-based weighted formulas ensuring auditability, every score shows exactly which signals contributed and by how much. For the latest day (day 14), when OpenAI is configured, the system sends the full 14-day time series to the AI, which analyzes patterns, trajectories, and cross-signal correlations that fixed rules cannot detect. This produces three composite scores: Burnout Risk (0-100), Performance Health (0-100), and Growth Potential (0-100), with AI-generated rationale explaining key drivers and trajectory. If AI is unavailable, the system gracefully falls back to rule-based scoring, ensuring the platform always functions. OpenAI powers six additional capabilities: insight generation explaining why alerts fired, deep scoring analysis finding hidden risk patterns, time-series analysis with anomaly detection and two-week forecasts, personalized coaching guides that transform data into empathetic conversation starters, dynamic performance reviews generated from actual behavior rather than manager memory, and predictive alerts identifying burnout trajectories before crisis.

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