Keep compliance connected
Bring process risks, control plans, audit evidence and corrective actions into a connected quality workflow.
Quality AI Workforce
QAI
Specialised AI Analysts across the quality lifecycle.
QAI performs defined quality-analysis work across supplier, process, customer-complaint and warranty evidence. It prepares findings and investigation priorities while authorised quality professionals retain approval authority.
Process capabilityCpK 1.18
Evidence-backed findingDrift detected before rejection limit
Sample Quality BriefEvidence-backed
A rejection increase appears after a material-lot transition and is concentrated on one machine.
SPC movement begins before rejection crosses the internal limit
The same defect code dominates the rejected quantity
No equivalent increase is visible on the adjacent machine
Recommended next step
Validate material and process parameters on the affected machine before expanding containment.
Illustrative product-output example. Values and findings are not customer results unless a published case study is linked below.
Beyond Conventional QMS
Don't just manage quality. Improve it.
Built for the shop floor. Trusted for compliance. Designed to help manufacturing teams connect quality evidence, investigate faster and prevent repeat losses.
Evaluating conventional enterprise QMS?
Evaluate how a quality platform supports both compliance and operational improvement. Ask more than whether a system documents compliance: how does it connect SPC, rejection and COPQ to complaints, 8D, PFMEA, Control Plans and action effectiveness?
See the cost of quality
Link shop-floor measurements and rejection evidence to recurring loss and COPQ priorities, subject to available integrations.
Investigate and prevent recurrence
Use QAI to assemble evidence and propose investigation priorities; engineers validate causes and approve actions.
Competitors offer differing quality-management, analytics and AI capabilities. This is a comparison of evaluation priorities, not a claim that other platforms lack these features. Ask for a demonstration of capabilities and integrations available in your deployment.
Why Teams Buy QAI
Buy QAI when quality engineers spend more time assembling evidence than preventing recurrence.
QAI reviews SPC, supplier evidence, PPAP, complaints and warranty information, then prepares findings and investigation priorities. Quality professionals retain technical validation and approval while repetitive analysis continues at scale.
Value reason 01
Find quality deterioration earlier
Bring process, product, supplier and historical context together before variation becomes an escape.
Value reason 02
Return engineering time
Reduce repetitive evidence gathering, cross-checking and case preparation around quality decisions.
Value reason 03
Scale consistent quality review
Apply the same evidence-led review discipline across growing case volumes, plants and suppliers.
Cost of waiting
Quality volume grows faster than manual review capacity. Delayed evidence creates more escapes, repeat complaints, late closures and Cost of Poor Quality.
How the value becomes measurable
Cases and evidence reviewed
Engineering time returned
Validated action accelerated
COPQ and escape impact measured
Customer complaint → 8D proof
A complaint-investigation workflow that previously required 6–8 hours of manual portal export, Excel preparation, PFMEA review, DMAIC work and 8D/A3 preparation is now prepared with QAI assistance in under 15 minutes for quality-team review. The digital workflow can run across lines, units and plants in parallel.
Choose Your First QAI Analyst
Give quality engineers more time to validate, solve and improve.
QAI supports repetitive evidence review and investigation preparation. Quality engineers, customers and authorised approvers retain technical validation, disposition and closure decisions.
Analyst Pack · 01
SPC Intelligence Analyst
Move from detecting variation to knowing where to investigate.
Stability, drift and out-of-control signals
Product, machine, shift and material context
Evidence-led investigation priorities
Intended outcome
Identify deterioration earlier and help reduce scrap, rework and customer-escape risk.
Quality engineers validate causes, containment and disposition.
Analyst Pack · 02
Incoming Shipment Quality Evidence Analyst
Review supplier evidence before a documentation gap becomes a production problem.
Certificate and inspection-evidence review
Part, revision, lot and test-result matching
Missing, inconsistent or unclear evidence
Intended outcome
Reduce repetitive verification and surface incoming-material risk earlier.
Authorised personnel retain hold, release and escalation authority.
Analyst Pack · 03
PPAP Preparation & Review Analyst
Reduce the engineering effort behind PPAP preparation and supplier review.
Submission completeness and consistency
Drawing, process flow, PFMEA and control-plan cross-checks
MSA, capability and inspection-evidence review
Intended outcome
Fewer review iterations and more engineering time for higher-value quality work.
Responsible engineers and customers retain approval authority.
Analyst Pack · 04
8D Customer Complaint Analyst
Turn evidence gathering into a structured investigation.
Containment and evidence assembly
Failure-pattern and cause-hypothesis support
Corrective-action and closure-readiness review
Intended outcome
Customer proof: complaint-to-8D preparation reduced from 6–8 hours of manual work to under 15 minutes with QAI assistance.
Engineers validate root cause, action effectiveness and closure.
Analyst Pack · 05
Warranty Claims Analyst
Review each claim with the available service, product and manufacturing evidence.
Eligibility and evidence completeness
Failure-history and supplier-context correlation
Routing for review, recovery or more evidence
Intended outcome
Faster claim handling, less leakage and better visibility into recurring field failures.
Authorised teams retain approval, rejection and recovery decisions.
What It Does
Built around the work your team performs every day.
Start with one operating decision, connect the evidence already available and expand as value becomes visible.
01Customer-requirement and quality-standard governance
02Product and process quality-risk prioritisation
03SPC drift, process capability and COPQ analysis
04Quality-escape, complaint and warranty evidence correlation
05CAPA effectiveness, PFMEA, PPAP and supplier-quality monitoring
Questions It Helps Answer
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Which product or process risk threatens customer requirements?
?
Where is a quality escape most likely to occur?
?
Which issue carries the greatest customer and COPQ exposure?
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Has the corrective action actually reduced recurrence?
What Your Team Receives
Continuous quality-governance brief
Prioritised product and process risks
Evidence mapped to customer requirements
Quality-escape and COPQ exposure
Recommended investigation with human validation
Works With What You Have
Your current systems remain the source of truth.
Connect directly where data is accessible. Use EdgeVISS where operational data is trapped inside machines, controllers or industrial protocols.
QMSQualitivissSPCInspectionNCR / CAPAPFMEAPPAPWarrantyCustomer complaintsSupplier quality
Operational Outcomes
Prevent quality escapes earlier
Prioritise the highest customer and COPQ risks
Reduce Cost of Poor Quality
Strengthen customer-requirement conformance
Ship with greater confidence
Deployment
Designed for industrial architecture
Deploy in the architecture that fits the plant, data-governance and latency requirements. Access remains governed and operational experts retain decision authority.
CloudOn-premiseEdgeHybrid
Product case study
Commercial Vehicle Warranty Analysis: Closure Within a 45-Day SLA
See how QAI assisted quality analysis and workflow audit as a warranty team handled four times the case volume with human approvals.
Use your existing QMS—or add the application layer
QAI can analyse evidence from the quality systems you already operate. Qualitiviss is available where teams also need SPC, PFMEA, PPAP, NCR and corrective-action workflows.
Start With One Real Decision
See what QAI finds in your own operational data.
Begin with one dataset, one recurring problem and one decision your team needs to improve.
Start QAI Trial