Engineering an agentic Learning Mesh to identify live performance gaps and autonomously curate microlearning paths, reducing time-to-competency by 25% across 12,000+ employees.
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High-velocity workforce requiring rapid upskilling in distributed regional hubs.
AI Architect + 2 Full-Stack Engineers + Data Scientist embedded within People Ops.
Eliminating 'One-Size-Fits-All' training in favor of real-time, role-specific learning injections.
Vector-based Skill Ontology, Graph Neural Networks, and Event-Driven content triggers.
The client’s legacy LMS delivered static, multi-hour courses that failed to align with daily operational realities. This 'Knowledge Gap' resulted in a 40% lower course completion rate than industry benchmarks, as employees viewed training as a barrier to productivity rather than an enabler.
The risk was structural: skill decay was impacting delivery KPIs in the field. The enterprise required a transition from 'Reactive Training' to 'Proactive Micro-Interventions' without compromising strict SOC2 data privacy requirements for employee performance metadata.
Generic 60-minute modules delivered on a fixed quarterly schedule.
5-minute 'just-in-time' learning bursts triggered by live performance data.
Broad buckets (e.g., 'All Managers') received identical training regardless of skill.
Unique paths generated for every individual based on their specific proficiency graph.
Success measured by 'clicks' rather than actual competency improvement.
Success measured by the direct correlation between training and KPI recovery.
Uses Graph Neural Networks to predict future skill requirements based on emerging project metadata and industry shifts.
Anonymized performance scoring ensures compliance with EU/GDPR requirements while providing actionable L&D insights.
K8s-orchestrated delivery ensures sub-2s path generation even during global morning login spikes.
Pre-built ontology logic for mapping 2,000+ technical and soft-skill nodes.
Production-ready templates for multi-modal content chunking and path sequencing.
Real-time monitoring for knowledge retention and skill application in the workplace.
Automated resource scaling to maintain sub-2s latency while optimizing cloud spend for 12k+ users.
Transitioning to microlearning paths eliminated the 'time-barrier' and increased voluntary participation.
Targeted interventions reduced the duration between skill identification and proficiency verification.
Agentic architecture provides near-instant personalization regardless of user concurrency.
Client Testimonial
Coretus didn't just give us a learning tool—they engineered an adaptive competency engine. We closed our critical skill gaps 25% faster, and our engagement rates have reached levels that were previously unthinkable for a workforce of our size.
Chief Learning Officer