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AI Quality & Cost of Quality Optimization
Reduce defects, scrap, rework and cost of poor quality with AI-powered quality optimization.
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Use your real quality, defect and cost-of-quality data to prove measurable improvement in days.
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A super fast way to see how AI can improve quality and reduce cost of poor quality.
The AI Improvement Cycle
Key AI Applications
1
AGGREGATE
your data
- ERP
- QMS / Data Warehouse
- Excel / CSV
- MES / Test Systems
- Nonconformance / CAPA
- Supplier / Customer Quality
2
ANALYZE
key analyses
- Defects & Pareto
- Root cause patterns
- Scrap, rework & COPQ
- Capability & drift
3
ADVANCE
best suggestions
Prioritize highest COPQ driversHigh impact
Identify recurring defect causesHigh impact
Recommend prevention actionsMedium impact
4
ACT
in key applications
Quality & COPQ Analytics
Quality Manager
AI-Powered 8D Manager
Issue Tracker
NCR Manager
DMAIC Manager
5
AUTOMATE
manual workflows
- Issue Routing
- CAPA Approvals & Escalations
- Reminders & Notifications
- SPC Alerts & Monitoring
Quality Management Is Complex - AI Can Help
Stop chasing quality data across spreadsheets, ERP, MES, QMS, inspection systems, supplier portals, emails, images, forms, and tribal knowledge. Praxie’s AI-powered quality management workspace connects quality events, process data, documentation, inspections, suppliers, CAPA, audits, and customer requirements so manufacturers can detect issues earlier, reduce rework, and improve compliance.
20–40% fewer quality escapes
30–60% faster root cause analysis
15–30% lower cost of poor quality
2
Specs, Standards & Compliance
3
Process Parameters & Limits
4
Operator Skills & Training
5
Supplier & Material Quality
6
Gauges, Fixtures & Tooling
7
Equipment Condition & Maintenance
AI Quality
Engine
Approved
Checked
Review
Released
Quality Approved
traceable + controlled
Customer Ready
reviewed + signoff ready
AIRisk
Signals RCARoot
Cause CAPAAction
Tracking
Signals RCARoot
Cause CAPAAction
Tracking
8
Process Flow & Work Centers
9
WIP, Lot & Batch Traceability
10
Inspection Plans & Sampling
11
Supplier Certs & Incoming Inspection
12
Defects, Scrap, Yield & Rework
13
Audit, CAPA & NCR Workflows
14
Customer Complaints & Escapes
Why it’s difficult
Quality management is not a single checklist. It is a living system where requirements, process variation, materials, people, suppliers, equipment, inspections, and documentation all interact.
Quality data is fragmented across many systemsERP, MES, QMS, inspections, supplier records, drawings, photos, forms, and emails rarely line up cleanly.
Variation and risk change in real timeProcess drift, supplier issues, operator variation, gauge problems, and customer complaints require immediate action.
One defect can ripple across the full quality systemA single nonconformance can affect containment, root cause, CAPA, documentation, suppliers, audits, and customer trust.
AI-Powered Quality Command Center
Defect Signalsscrap, rework, NCRs, complaints
Inspection Datameasurements, gauges, sampling
Supplier Qualitycerts, lots, incoming checks
AI
Quality
RCA
Quality
RCA
Earlier risk detection
Fewer quality escapes
Lower COPQ
Faster CAPA closure
