A manufacturing knowledge AI service that structures equipment, process, quality, work and production data to support predictive maintenance, quality prediction, production optimization and SOP automation.
Manufacturing · Smart Factory · Predictive Maintenance
OntoFactory connects fragmented manufacturing data to raise equipment reliability, quality prediction and production decision quality.
Knowledge graph structure
Linking equipment, process, quality and work data by relationships to sharpen root-cause analysis and decision quality on the shop floor.
Problems we solve
Equipment and quality data live apart, making causes hard to link.
Defect root-cause analysis relies on skilled workers' experience.
Equipment failures are managed reactively, so prevention is hard.
Smart-factory data stays at a view-only dashboard level.
Production plans and shop-floor execution data are not connected.
Core services
On ontology-structured manufacturing knowledge, we deliver predictive maintenance, quality prediction and a floor copilot.
Define equipment, process, quality and work concepts and relationships as a knowledge graph.
Predict failures ahead of time from sensor and maintenance history.
Detect anomalies early through the relationship of process variables and outcomes.
Trace the root-cause process and equipment through multi-hop reasoning.
Present uptime, defect rate and production trends with causes and evidence.
Structure work standards and SOPs to answer floor questions precisely.
Answer operators' natural-language questions grounded in the knowledge graph.
Expected impact
Failure prevention
Shift from reactive to preventive.
Lower defect rate
Cut recurrence with cause-based analysis.
Higher productivity
Optimize flow across process, plan and floor.
Better decisions
Speed up judgment with grounded insight.
Plant floor tools
P&ID connection checks
Turns P&ID drawings into a plant knowledge graph and checks whether the piping actually connects. It finds broken connections and empty nozzles and reports each with a severity and a cause. On the public DEXPI example drawing it found 4 connections without both ends attached.
Maintenance failure analysis
Normalizes free-text maintenance records into failure modes, causes, actions and parts. It ranks which failures stopped each piece of equipment, how often and for how long, and drafts an FMEA.
It finds broken connections in a P&ID drawing, marks them on the drawing, and a person makes the call.
The drawing is the DEXPI 2.0.1 specification reference drawing (DEXPI e.V., CC BY 4.0), split for the demo. It is not a customer or plant drawing. The app screens are in Korean.
See each finding on the drawing
The 3 defects the engine found are listed in review order, and their positions are numbered on the drawing. The reviewer picks a cause and records a decision.
It turns maintenance records that each person wrote differently into the same words, and shows which failures repeated and how often.
Screens with synthetic example data. The numbers come from 42 example injection molding maintenance records, not from a real plant. The app screens are in Korean.
Every value has evidence, and a person checks it
Shows the original sentence, the normalized values and the phrase each value came from side by side. The reviewer approves, corrects or rejects.
To review it with your own drawings or maintenance records, let us know.
Contact us