Capture signals
Vision, sensor, and line data are labeled and linked to batches, shifts, and suppliers.
/ Opérations et automatisation /
Détectez les défauts avant qu’ils n’atteignent le client.
Une surveillance de la qualité basée sur l'IA qui inspecte, classe et signale les problèmes dans la production, la prestation de services et les interactions avec les clients, à une échelle qu'aucun processus manuel ne peut égaler.
The problem
QA sampling misses what actually matters
-
QA sampling misses what matters
Manual review covers a small fraction of output. Issues that fall outside the sample go undetected until they escalate.
-
Quality data is collected but not acted on
Defect logs, customer complaints and inspection records exist but aren't connected. Patterns are invisible until they become crises.
-
QA teams focused on reporting, not prevention
Most QA effort goes into documenting problems after they occur, not into detecting and preventing them earlier in the process.
-
Defect patterns nobody connects
Issues repeat across lines, shifts or channels because root causes stay buried in logs instead of driving prevention.
Fonctionnement
Detect defects earlier—trace root cause faster
Classify and prioritise
Defect types, severities, and recurrence patterns route to the right engineers and CAPA workflows.
Improve the line
Feedback reduces false rejects and tunes SPC limits so quality and throughput move together.
Works alongside MES and QMS—audit trails match regulated industries.
Détectez les défauts avant qu’ils n’atteignent le client.
Ce qui est inclus
What you get when you run this with Thinkia
A governed layer across data, workflows, and handoffs—so teams ship safely and scale with metrics.
100% interaction or output coverage
AI monitors every transaction, interaction or production output — not a sample.
Defect classification
Automatically categorises quality issues by type, severity and root cause signal.
Pattern detection
Identifies recurring issues across time, product lines, teams or customer segments.
Real-time alerts
Notifies the right team the moment a quality threshold is breached.
Root cause analysis
Connects defect patterns to upstream process variables for faster resolution.
QA reporting automation
Generates quality reports and trend summaries without manual data assembly.
Propulsé par Thinkia Synapse
Résultats
What changes when this runs in production
Results vary by process type, data availability and existing QA infrastructure.
100% coverage
Every unit inspected vs. 5–15% with manual sampling.
–80%
Faster pattern identification vs. periodic manual review
–55%
Reduction in time to produce quality reports per period
Notre façon de travailler
From sample checks to continuous evidence across lines and shifts
Defect taxonomy
Defect taxonomy
Week 1–2
Failure modes, specs, and visual standards are aligned with engineering and operations.
Vision & data
Vision & data
Week 3–5
Cameras, lines, and labelling strategy are set; golden sets anchor model performance.
Line pilot
Line pilot
Week 6–9
Inline or end-of-line inspection runs in shadow; escapes and false rejects are tuned.
Plant network
Plant network
Week 10+
Rollout by site with central monitoring; change control when products or tooling shift.
Cycle time and lighting conditions affect vision models; scope follows stable SKUs first.
Aller plus loin
Quality, accuracy, and safer models
Commencer
Ready to scope this for your context?
We start with a focused session—no commitment—to map constraints and a sensible path.