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Global Codex Hackathon: [London]
In large organizations, staffing often relies on incomplete profiles, keyword searches, and personal networks. That creates slow staffing cycles, missed opportunities, and unfair visibility for talent outside existing circles. In Common solves this by turning static workforce data into explainable, AI-driven talent intelligence that surfaces stronger matches, identifies skill gaps, and helps build teams based on capability, availability, and collaboration signals rather than proximity or guesswork./…

In Common is an AI-powered workforce intelligence prototype that helps large organizations staff teams faster, more fairly, and with better evidence. It sits on top of existing workday employee and role data, enriches static profiles through conversational AI, and uses a layered matching pipeline to connect people to opportunities. Key features: Conversational profile enrichment to capture hidden skills, motivations, working style, and team dynamics Role-to-candidate and candidate-to-role matching Hard filters plus semantic matching plus LLM-powered reasoning Anonymized shortlists by default to reduce bias and fairer Staffability scoring and training recommendations to improve deployability Decision traces and audit logs for explainability and trust

Team

sana-riazmaxinemsvase

Placement

2nd Place

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