Vision on the line
Neural inspection for print, seal, fill, label and surface defects, with defect classes tied to your quality ontology rather than to a generic taxonomy.
ASSETEDGE
For people who measure downtime in dollars per minute.
AssetEdge brings plant, line, fleet and equipment intelligence into one ontology, condition, inspection, quality vision, work history and the maintenance call made early enough to be worth making.
platform.flexday.ai / assetedge / plant intelligence
SITE 14 · CONVERTING LINE 3
OEE TODAY
81.4%
↓ 4.1 vs baseline
DEFECT RATE
0.31%
Vision-detected
OPEN WOs
7
2 overdue
Registration drifttrending · 3 shifts · unit 2
Vision: color variancecluster on one substrate lot
Likely causeanilox wear · 92% confidence
Recommended actionschedule in next changeover
Raise work orderShow the frames
WHAT IT ACTUALLY DOES
Sees the asset, reads the history, makes the call.
Condition monitoring tells you a number moved. AssetEdge tells you what it means, what probably caused it, and what to do before the next changeover.
SEES
READS
Every maintenance record
Work orders, technician notes, OEM manuals, failure histories and warranty claims, including the free-text where the real diagnosis usually lives.
JOINS
Process and sensor data
Historians, SCADA, MES, telematics and handheld feeds resolved against the asset hierarchy, so a line, a unit and a component mean the same thing everywhere.
EXPLAINS
Probable cause, with evidence
Correlates a defect cluster with a substrate lot, a shift, a setting change or a component's service age, and shows the frames and records behind it.
ADVISES
The maintenance decision
What to do, when to do it, what it costs to wait, and whether it can wait for the next planned stop. Scheduled through your CMMS, not around it.
SPREADS
What one site learned
A fix that worked at one plant becomes a watch condition at the other fifty-nine, automatically, with local context preserved.
PROVEN IN HEAVY INDUSTRY
Sixty plants, one asset ontology.
AssetEdge grew out of manufacturing work at real scale: neural quality inspection on packaging lines, plant intelligence agents rolled out across a sixty-site network, and fleet and terminal operations in freight.
The hard part in every one of those was never the model. It was getting fifty-nine plants to agree what an asset, a defect and a downtime event actually are, which is precisely what the ontology layer is for.
Asset hierarchy
Site, line, unit, component: one definition
Defect taxonomy
Your classes, not a generic library
Vision inspection
In-line, with confidence routing
Technician notes
Free text treated as primary evidence
CMMS integration
Work orders raised in your system
Cross-site learning
One plant's fix becomes everyone's watch
ASSETEDGE SPECIFICATIONFLEXDAY.AI
- ONTOLOGY
- Asset canon: site, line, unit and component hierarchy, defect and failure taxonomy, downtime event model, maintenance and work order structures
- SOURCES
- Historians and SCADA, MES, CMMS and EAM (SAP PM, Maximo), vision systems, telematics and handhelds, OEM manuals, warranty and quality records
- ENGINES
- SpeciphicView for inspection and inline vision · StructIQ for process, sensor and reliability analytics · SpeciphicAsk for manuals, notes and failure history
- OUTPUTS
- Defect classification and cause analysis, condition and reliability alerting, maintenance recommendations, work orders raised in your CMMS, cross-site watch conditions
- SECTORS
- Packaging and converting, food and beverage manufacturing, chemicals, transportation fleets and terminals, facilities
Bring one line and one recurring defect.
That's usually enough to show what AssetEdge does that a dashboard doesn't.