Assess
Sources, research and practical examples are checked for substance, context and limitations.
FOXLEY QUALITY INTELLIGENCE
Understand AI.
Rethink quality.
FQI connects artificial intelligence with quality management—expertly contextualized, explained for practice, and critically assessed.
ASSESS • TRANSLATE • EVALUATE
Knowledge that works in practice.
Three steps to a robust assessment
What FQI contributes to quality work
Translate
AI developments are translated into the language of FMEA, 8D, Six Sigma, CAPA and inspection.
Evaluate
Opportunities, risks, validation and responsibilities are evaluated critically and transparently.
Latest FQI insights
Process optimisation
Before AI correction: separate measured deviations into traceable components
How quality teams distinguish measurements, simulation and estimated error components before AI proposes machine corrections.
PrüfstrategieProcess optimisationAI & automation
Read reportInspection processes
Checking only AI alerts leaves missed defects unknown
A Ford case study exposes a gap in AI detection evidence. An FQI evaluation plan includes components that produce no alert.
MesssystemanalyseInspection processesAudit
Read reportInspection processes
When the ranking is right but acceptance is wrong
AI evaluators can rank reports correctly yet accept the wrong ones. An FQI pilot separates ordering, decision thresholds and aggregate quality indicators.
MesssystemanalyseInspection processesAudit
Read reportData quality
When training data distort the model test
Synthetic training examples must not compromise an independent model test. An FQI plan checks data lineage, separation and realistic defect frequencies.
Inspection processesData qualityAI & automation
Read reportData quality
What an empty tool response actually establishes
A successful tool call can still lose crucial information. A bounded test process makes coverage and limitations visible.
RegressionstestAuditInspection processes
Read report
Topics & methods
AI & automation
Assessments of AI, LLMs, automation and agents in quality management.
FMEA
Risk analysis and failure mode effects in practice.
8D
Structured problem solving and root cause analysis.
Six Sigma
DMAIC, process improvement and data-driven decisions.
CAPA
Corrective and preventive actions in quality management.
Inspection processes
Inspection strategies, test reports and release processes.
Data quality
Validation, data foundations and reliable decision bases.
Knowledge management
Knowledge structuring, documentation and expert assessment.
Content is continuously expanded. The goal is a growing knowledge base of expert assessments between AI, quality and technical problem solving.