AI Copilot
Ask the plant a question in plain language
Natural-language access to inspection, process and CAPA history with every answer traced to source records.
Enterprise Manufacturing Intelligence
Stop defects before they reach cost.
Trust Quality Assurance unifies inspection, SPC, machine and supplier data into one governed platform — so every plant detects drift early, acts on it with accountability, and proves control to auditors.
- Detect process drift in seconds, not at end of shift
- Cut scrap and rework with predictive quality models
- Prove control with audit-ready evidence on demand
Built for regulated, high-volume production
- ISO 9001
- IATF 16949
- AS9100D
- ISO 13485
- SOC 2 Type II
- 21 CFR Part 11
Stuttgart 01 / Line 3 / Cell assembly
Shift B · updated 12s agoLive
OEE
87.4%
+5.4 pt
First pass yield
98.2%
+0.6 pt
Scrap rate
0.42%
-54%
Unplanned downtime
12min
-38%
SPC · Seal width (mm)
1 point > UCL- Cp
- 1.61
- Cpk
- 1.42
- Subgroups
- 2,418
Defect Pareto · 7 d
287 total- Seal width variation92 · 32%
- Surface mark74 · 58%
- Porosity58 · 78%
- Misalignment41 · 92%
- Contamination22 · 100%
Machine health
4 assets- PRESS-0462%
- WELD-0294%
- AOI-1188%
Live alerts
2 need action- 09:41Seal width above UCL · Line 3M. Keller
- 09:22Scrap rate +0.18% vs. shift targetA. Duval
- 08:57Supplier lot B-118 held for re-inspectionS. Marchetti
AI Copilot: seal width drift correlates with die temperature on PRESS-04. Recommend recipe check before next batch.
94% confidence- production lines connected
- 180+
- enterprise availability SLA
- 99.95%
- signal to alert latency
- < 2 s
- countries in production
- 14
Standardising quality data across regulated, high-volume manufacturing operations in Europe, North America and Asia.
NORDWERK
Automotive tier 1
9 plants
VOLTA CELL
Battery cells
18 GWh
HELIOS
Electronics
22 SMT lines
FERRUM
Metal forming
6 plants
MEDIQA
Medical devices
ISO 13485
AEROSTRUCT
Aerospace
AS9100D
Platform
One operating layer for plant-wide quality
Replace spreadsheet handoffs and end-of-shift reports with a system that connects signals, decisions and corrective action across every site.
Reference architecture
From sensor reading to executive decision — and back to the line
Deploy at the edge for latency and resilience, aggregate in the cloud for cross-plant analytics, and keep decision authority with your quality organisation.
Layer 01
Plant floor · acquisition
OPC UA · MQTT · REST · secure file drop
- FactorySites, areas, lines, cells
- MachinePLC, CNC, press, welder
- SensorGauges, cameras, probes
- Edge deviceBuffering, inference at line
Layer 02
Cloud platform · intelligence
Governed quality model · SPC engine · ML inference
- Cloud data layerNormalised product, lot and characteristic model with full genealogy
- AI engineAnomaly detection, vision classification, predictive quality scoring
Layer 03
People · decision
Closed-loop action with owners, evidence and approvals
- DashboardLine, plant and network views
- Management reviewKPIs, escalation, accountability
- DecisionCAPA, recipe change, supplier action
Closed loop: approved decisions flow back to the line as parameter changes, tightened inspection plans and supplier requirements — then the platform measures whether they worked.
Yield trend
+2.1 pt
Scrap rate
-54%
OEE
+5.4 pt
Solutions by role
Built around the decisions your teams actually make
Each role works from the same governed data through the workflow they need: investigate, prevent, escalate and prove control.
Product
Depth where quality decisions happen
Move from signal to owner to evidence without exporting data between inspection, analytics and action systems.
Stuttgart 01 / Line 3 / Production & OEE
Shift B · updated 12s ago3 of 4 lines running
OEE
87.4%
+5.4 pttarget 85%
Availability
93.1%
+2.2 pt
Performance
95.6%
-0.4 pt
Unplanned downtime
12min
-38%shift to date
OEE breakdown · shift B
target 85%OEE
+5.4 pt
Availability
+2.2 pt
Quality
FPY 98.2%
Downtime reasons · 7 days
- Tool change46 · 41%
- Material starve31 · 68%
- Sealing unit fault24 · 89%
- Quality hold12 · 100%
Machine health
4 assets- PRESS-0462%
Die temperature +6 °C vs. recipe
- WELD-0294%
Within nominal parameters
- AOI-1188%
Model v4.2 · 96% confidence
- SEAL-0741%
Bearing vibration trending up
Applied AI
AI that explains itself on the shop floor
Manufacturing quality cannot accept a black box. Every prediction, classification and recommendation arrives with its evidence, its confidence and a named human decision point.
AI Copilot / Stuttgart 01
Grounded in plant data
Why did scrap increase on Line 3 during night shift?
Scrap rose from 0.42% to 0.60% between 22:10 and 03:40. The dominant cause is seal width drift on PRESS-04, correlated with a +6 °C die temperature deviation after the tool change.
Confidence
94%
Evidence
5 sources
Est. impact
€18.4k
- SPC subgroup 9 · seal width 4.19 mm above UCL 4.15 mm
- Maintenance log · die change 21:55, no thermal soak recorded
- Vision station 11 · porosity cluster on same 40-part window
Draft CAPANotify PRESS-04 ownerTighten inspection plan
Vision inspection / Camera C-14
Inline
Defect rate · 12 wk
Model
v4.2
Reviewed
100%
Suggested root cause
Die temperature deviation · PRESS-04
Root cause analysis
Correlated causes ranked by evidence
Cross-references process parameters, machine state, material lots, operators and shifts to shortlist probable causes.
Predictive defect detection
Warn before the part is scrap
Sequence models score in-process signals to flag drift minutes before parts leave specification.
Quality recommendations
Next best action, with expected effect
Suggests parameter, inspection-plan or containment changes ranked by expected yield and cost impact.
Smart CAPA
Draft corrective actions in seconds
Pre-populates problem statement, containment, root cause and verification plan from linked evidence for human approval.
Vision inspection
Consistent judgement on every part
Classifies surface, weld and assembly defects at line speed with confidence scores and reviewer feedback loops.
Machine learning operations
Models you can audit and roll back
Versioned models with drift monitoring, champion/challenger evaluation and per-plant retraining controls.
Risk prediction
See escape risk before shipment
Scores batches and supplier lots by escape probability using genealogy, process and inspection context.
Production optimisation
Protect yield and throughput together
Balances quality limits against throughput targets to recommend setpoints that hold capability.
Human in the loop
AI recommends, qualified people approve
Versioned and reversible
Model, threshold and prompt history retained
No cross-customer training
Your process data never trains another tenant
Industries
Configured for your production reality
Compliance language, data models and inspection workflows adapt to how each sector defines a critical characteristic.
Customer evidence
Measured against a documented baseline
Every engagement starts by recording current yield, scrap cost, investigation time and audit effort — so results can be defended in a capital review, not just a slide.
Automotive tier 1 supplier
9 plants · 4 countries · IATF 16949
- Situation
- Sealing defects were detected at end-of-line audit, on average 6 hours after the process had drifted. Investigations relied on manual export from three systems and took two engineers most of a shift.
- Deployment
- Inline SPC on 24 critical characteristics, vision inspection on two stations, and CAPA workflows tied to product genealogy. Rolled out on one line, then to nine plants in two quarters.
We stopped arguing about whose data was right. The line, the plant and the customer quality team now look at the same record, and the corrective action has an owner before the shift ends.
Verified outcomes
Scrap cost
-41%
vs. 12-month baseline
First pass yield
+2.1 pt
96.4% → 98.5%
Investigation time
-62%
8 h → 3 h median
Audit preparation
-3 days
evidence retrieved on demand
Figures are customer-reported, measured against a 12-month pre-deployment baseline. Results vary with process maturity, data availability and rollout scope.
Request the full case studyBattery cell manufacturer
2 gigafactories · 18 GWh
Coating thickness variation caused formation losses discovered weeks later.
Drift detected within one coating run; formation scrap reduced 28% in the first quarter.
-28%formation scrap
Electronics EMS
22 SMT lines · 3 sites
AOI and SPI data lived in separate silos, so repeat defects were invisible across lines.
Unified board-level defect model surfaced a shared stencil issue affecting six lines.
-34%rework hours
Medical device manufacturer
ISO 13485 · 21 CFR Part 11
Design history and inspection evidence were assembled manually for every audit.
Electronic signatures and immutable audit trail cut audit preparation from days to hours.
100%record traceability
Deployment
From first connector to closed-loop control
Start with the data sources you already trust, then expand from pilot line to multi-plant quality operations without replacing your core systems.
- 1
Week 0–1
Connect
Integrate MES, ERP, historian, PLC, camera and gauge sources through native connectors, OPC UA, REST or secure file drop.
Data access confirmed
- 2
Week 1–2
Normalise
Map products, lines, lots, stations and critical characteristics into one governed quality model with genealogy intact.
Quality model signed off
- 3
Week 2–4
Monitor
Enable live SPC, capability tracking, AI defect analytics and alert routing for the agreed scope and owners.
Alerts in production
- 4
Week 4+
Close the loop
Route NCR, CAPA, supplier and audit actions with evidence and effectiveness checks, then measure against the baseline.
Results vs. baseline
Integration surface
Connectors run at the edge or in the cloud and never require write access to your systems of record unless you enable it explicitly.
- OPC UA
- MQTT
- SAP
- Siemens
- Rockwell
- Ignition
- Kepware
- REST API
Business impact
Outcomes a plant controller will accept
The platform is measured on the operational metrics quality leaders already report: investigation time, scrap cost, capacity and audit readiness.
-62%
Faster investigations
Process, inspection and genealogy data arrive pre-correlated, so engineers stop rebuilding the same picture by hand.
median across 14 deployments
-41%
Lower scrap cost
Inline SPC and predictive alerts let teams intervene while the process is still correctable rather than after the audit.
vs. 12-month pre-deployment baseline
+5.4 pt
Higher OEE
Fewer quality holds and shorter containment windows return capacity that was previously absorbed by rework.
average across multi-line rollouts
100%
Traceable evidence
Audit records, approvals, AI decisions and CAPA history stay linked to the product and the production event.
of records in configured scope
Customer-reported figures measured against documented pre-deployment baselines. Results vary with process maturity, data availability and rollout scope; your pilot report states the baseline used.
Trust, security & compliance
Built for the data your auditors and IT team will question
Process parameters, recipes and quality records are among your most sensitive assets. The platform is designed so security review, validation and audit are routine rather than exceptional.
Security programme
SOC 2 Type II aligned controls covering access, change management, monitoring, vendor risk and incident response.
Role-based access control
Permissions scoped by site, line, product family and supplier, with SAML/OIDC single sign-on and SCIM provisioning.
Immutable audit trail
Every read, edit, approval and model decision is time-stamped, attributed and retained for the configured period.
Data integrity
ALCOA+ aligned records with electronic signatures, versioning and no destructive edits to released quality data.
Encryption
TLS 1.3 in transit, AES-256 at rest, customer-managed keys and secret rotation on enterprise agreements.
Availability
99.95% monthly SLA, multi-zone deployment, RPO 15 minutes, RTO 4 hours and edge buffering when the link drops.
Scalability
Proven at 2.4 billion inspection records per year across 180+ connected lines, with sub-second query on hot data.
Data residency
EU, US and APAC regions, with private cloud or on-premise deployment where data cannot leave the plant network.
| Standard | Scope | Platform support |
|---|---|---|
| ISO 9001 | Quality management system | Process control records, management review KPIs, documented improvement loop |
| IATF 16949 | Automotive QMS | Characteristic-level SPC, PPAP evidence, layered process audit support, escalation records |
| AS9100D | Aerospace QMS | Traveller, first-article and nonconformance traceability with configuration history |
| ISO 13485 | Medical device QMS | Design history linkage, controlled documents, verified corrective action effectiveness |
| FDA 21 CFR Part 11 | Electronic records and signatures | Unique signature binding, audit trail, retention policy and system validation package |
| VDA 6.3 | Process audit (German OEMs) | Process-step evidence, capability data and corrective action closure per question block |
| ISO/IEC 27001 | Information security | Control mapping, risk register and supplier security review artefacts on request |
99.95%
monthly availability SLA
15 min
recovery point objective
Annual
third-party penetration test
2.4 B
records processed per year
Start the evaluation
Find out where quality drift is hiding in your plant.
Bring one line, one defect family or one audit workflow. We will map the available data sources, quantify the cost of the current detection delay, and show the fastest route to measurable control.
45-minute technical walkthrough
With a solution architect who knows manufacturing data, not a scripted demo.
NDA before any data review
We can assess feasibility from sample exports without production access.
Written pilot scope
Baseline metrics, success criteria and exit conditions agreed up front.