AI L&D Systems Design
CoSignal
Capability Risk Detection System for spotting workforce performance gaps before they become operational failures.
3Agents in Pipeline
5Live API Calls
4Risk Rules Triggered
7AI Interventions
Input Signals
📊
Performance Data
- Inventory accuracy
- Processing time
- Error rates
🎫
Support Tickets
- Ticket volume
- Resolution backlog
- Repeated issues
🧠
LMS Analytics
- Quiz scores
- Retakes
- Completion %
📋
Survey Sentiment
- Confidence
- Process clarity
- Change readiness
👥
SME Feedback
- Office hours notes
- Supervisor concerns
- Trainer observations
⚙️
Operational Metrics
- Throughput
- Variance
- Delays
↓
Agentic Pipeline
Agent 1
Signal Aggregator
Normalizes tickets, LMS records, pulse survey data, and operational indicators into unified workflow signals.
Pure PythonAgent 2
Capability Risk Detection
Applies auditable threshold rules to detect knowledge decay, process confusion, confidence gaps, and SME bottlenecks.
Deterministic LogicAgent 3
Prescription Engine
Generates grounded interventions using actual signal data, then formats recommendations for leaders and managers.
AI Generated↓
Risk Classification
High Risk
Inventory Adjustments4 rules triggered · Priority 1Moderate Risk
ReceivingConfidence gap · Priority 2Watch
Scanner OperationsNear-threshold signals · Priority 3↓
AI-Generated Interventions
📘
Microlearning📝
Job Aids🎯
Manager Coaching🔁
Reinforcement👥
SME Redistribution📈
Change ManagementPerformance signals drive intervention.
Not completion rates.