Define
Approve the reference sample, technical specifications, tolerances and defect classes for each style.
BRANDAI-assisted quality control for apparel brands
Quality, visible
from factory
to brand.
Set one quality standard across suppliers. Connect garment images, specifications, AI-assisted findings and human decisions in a traceable workflow.
FROM APPROVED REFERENCE
TO DOCUMENTED DECISION
TO DOCUMENTED DECISION
01 / QUALITY INTELLIGENCE
For brands producing across borders
One standard. Better evidence. Earlier intervention.
01 / THE APPROACH
A shared standard
across every supplier.
The brand defines what good looks like. Suppliers capture evidence at agreed production stages. Vision QC AI highlights differences for qualified people to review.
Capture
The supplier provides dated images and identifies the garment, batch and production stage.
SUPPLIERCompare
AI-assisted checks flag visible defects, measurement deviations and differences from approved references.
VISION QC AIDecide
The quality team requests a correction, reviews new evidence or chooses a physical inspection.
BRAND02 / THE PLATFORM
Evidence that leads
to action.
The modules connect visual review, garment measurements and approved references to the same inspection record.
01 / DEFECTSFind and review defects
Highlight areas that need attention, classify them by type, location and severity, and keep the reviewer in control of the decision.
02 / MEASUREMENTSCheck measurements against tolerance
Use a style and size template to compare measurement points with approved specifications and flag out-of-tolerance results.
03 / COMPARISONApply one quality language
Shared defect classes, references and review rules help teams compare production evidence across suppliers and batches.
Illustrative examples from the project presentation; capabilities and performance are confirmed in the customer's production workflow.
03 / ECONOMIC IMPACT
A measurable case
for better quality control.
The quality programme compares the full cost of inspection against a like-for-like baseline agreed with the customer. Results are reviewed on real products and batches.
QUALITY CONTROL COST SAVINGS50%
Minimum saving in the proposed programme
Supplier quality teams work on site while external inspections focus on critical issues. The guarantee is assessed against an agreed cost baseline and equivalent inspection scope.
75–80%peak reductions where the quality workflow allows it
Cost comparison
INDEX · CURRENT QC COST = 100Proposed cost index composition
20
15
15
Photo capture & managementPlatform & central reviewRemaining physical checks
Method. Cost index 100 represents the customer's present cost per comparable lot. The 50-point programme target and 20/15/15 composition follow the supplied Vision QC AI study. Actual scope, baseline and results are established for each customer; peak savings depend on how quality control is organised.
04 / REVIEW & ACCOUNTABILITY
The evidence stays
with the decision.
Each finding can connect the garment and lot to original images, references, tolerances, a proposed severity and the brand's review outcome. Teams can track corrections without losing the history of what was seen.
- Garment, lot, stage and capture date
- Findings, measurements and reference points
- Correction requests and dated follow-up
- Supplier trends on comparable production
INSPECTION RECORDEXAMPLE VIEW
STYLE / LOTST-204 / 034INTERMEDIATE REVIEW
EvidenceImage set received
AI findingAnomaly to review
ReferenceApproved specification
Next actionQuality team review
ILLUSTRATIVE WORKFLOW · HUMAN DECISION REQUIRED
05 / FUTURE DEVELOPMENT
DEVELOPMENT CONCEPTFrom assisted review
to an automated line.
The planned inspection machine extends the software workflow to controlled imaging, automated handling and higher visual coverage. It is a future development project, with cycle times and costs to be validated on an operating line.
6,000garments per day in the study scenario
100%target visual coverage for the automated line
CONCEPT IMAGE / NOT A RELEASED PRODUCTFUTURE LINE / STUDY SCENARIO
Annual operating cost at 6,000 T-shirts per day
With 250 operating days and a $120 daily labour cost in the study, the model compares manual inspection with assisted inspection. Equipment purchase and installation are outside these operating figures.
$328k estimated annual operating cost difference
Source: Vision QC AI garment-line study scenario. These are modelled figures for a future machine, not observed customer results. Throughput, downtime, cost and visual coverage require validation on site.
06 / START WITH EVIDENCE
See it. Test it.
Decide with your team.
A demonstration and structured evaluation put the platform against the products, suppliers and quality criteria that matter to your business.
01
Demonstration
Review software, defect examples, measurements and reports with your quality and production teams.
02
Parallel evaluation
Compare AI-assisted findings with expert checks on the same garments and agreed production stages.
03
Measure the result
Assess total cost per lot, accuracy, coverage, turnaround and evidence traceability.
04
Agree the next step
Define the operational programme and commercial terms once the results meet your criteria.
07 / PRESENTATIONS
A place for the full story.
Product presentations and technical material will be published here when the public editions are ready. To send or request material, email support@visionnova.tech.
RESOURCE LIBRARYPresentation files coming soon
No files are publicly available yet.
LET'S TALK QUALITY
Bring your production
challenge to the table.
Arrange a demonstration with your quality and production teams.