Inspections, complaints, audits, KPIs and continuous improvement — data-driven, traceable and with clear root-cause transparency.
Use case 01
Inspection results, complaints, CAPA status, audit readiness, supplier quality and standards compliance — condensed in one interface, always up to date.
The AI quality dashboard condenses operational and strategic KPIs from inspection, complaints, audit and supplier evaluation into one interface. Trends, deviations and early-warning signals are surfaced automatically — without manual reporting work.
Management, quality leadership and production see at any time where quality risks emerge — and can intervene before anomalies become complaints.
How it works
We connect CAQ, ERP, MES and other data sources — securely, locally and without data leaving your company.
Inspection KPIs, thresholds, CAPA logic and early-warning rules are tailored to your standards and process landscape.
We support and optimise continuously — the dashboard grows with your audit and process requirements.
Use case 02
A direct contact point for standards, inspection instructions, complaints and audit preparation — based on your own data and documents.
The AI assistant answers questions about inspection steps, standards requirements, complaint cases and audit topics in context and traceably — based on your internal quality documents.
Quality managers, inspectors and audit leads save time on research, inquiries and preparation. Complex questions are answered in natural language, with references to the underlying regulations and documents.
Use case 03
Standards, inspection instructions, audit reports, CAPA history and experience — accessible through a single question.
The knowledge management platform connects standards, inspection instructions, audit reports, CAPA history and internal guidelines into a central, intelligent access point. Anyone looking for a standard requirement, an inspection rule or a past complaint case finds the answer without long research.
New quality employees are productive faster, senior know-how is preserved when people leave, and audit preparation becomes significantly less stressful.
Use case 04
Ten areas where AI agents relieve inspection, documentation, audit and improvement processes and accelerate them.
Automatically analyses image, sensor and process data and detects quality defects, deviations or errors at an early stage through pattern recognition and intelligent inspection.
Quality issues become visible earlier and more systematically.
Automatically analyses production and quality data and identifies potential causes for errors, complaints or process deviations.
Correlations and patterns are detected faster.
Automatically creates inspection logs, quality reports and audit documentation based on existing process and inspection data.
Documentation effort is significantly reduced.
Automatically categorises complaints, prioritises critical cases and creates action suggestions for corrective and improvement measures.
Processing times are shortened.
Automatically checks processes and documentation for compliance with internal guidelines, standards and regulatory requirements.
Missing evidence or potential violations become visible at an early stage.
Supports quality approvals, analyses supplier and incoming goods data and detects anomalies or quality risks at an early stage.
Approval processes are managed more efficiently.
Automatically analyses standards, guidelines and quality requirements and enables fast access to relevant content through intelligent search.
Changes and relevant requirements are detected faster.
Automatically tracks corrective and preventive actions, prioritises open tasks and supports workflow and escalation processes.
Improvement actions are implemented more consistently.
Automatically monitors calibrations, maintenance intervals and inspection equipment availability and reminds users of required actions at an early stage.
Quality-related failures or exceedances are reduced.
Supports production employees through automated inspection instructions, action recommendations and context-aware hints directly at the workstation.
Errors are prevented before they occur.