QFactory links values from PLCs, sensors, barcodes, cameras, ERP, and MES to item, process, and equipment records. People review AI-generated master-data drafts against source documents before saving them. Vision results remain linked to source images.
Start with the places where people retype values, search for missing records, or cannot trace a change.
Equipment and sensors, MES and ERP, and inspection records each keep their own screen. Two systems report different numbers for the same job, so somebody has to reconcile them by hand.
See equipment and system connectionsA value written down on the floor goes into a spreadsheet, and that spreadsheet is typed into the system again. Every transfer costs time and introduces errors.
See shop-floor screensDevelopment requests, drawings, and inspection photos pile up in folders. They never connect to items, routings, or quality history, so a person re-reads them every time.
See document and image readingAn anomaly alert or a report draft appears, but if nobody owns the next step it stops at the screen. The work never moves forward.
See automated tasksWhen master data is corrected or a judgment is reviewed, missing evidence and change history make the cause hard to trace.
See review and change historyExisting Equipment
We collect operating state and production values from the machines you already run, then attach them to work, quality, and equipment records. For older equipment, we begin with the connection method it can support.
See equipment connectivityCollect data on the floor, connect it to work records, use AI where it helps, and keep people in control.
Operators record work where it happens, and the office sees the same value.
Work order review, material issue, production results, inspection entry, and equipment checks are handled on shop-floor screens. That removes the paper-then-spreadsheet-then-system round trip.
Work orders, production results, inspection entry, lot traceabilityTechnology detailReview master-data drafts and trace results
AI drafts master data from documents and produces inspection results from images.
AI drafts master data from documents and produces inspection results from images. Master-data drafts stay linked to source documents, while vision results stay linked to source images.
Document reading, OCR, vision inspection, permission-scoped data searchTechnology detailDraft master data and connection settings from documentsInspect images and read displaysFind records and prepare the next task
AI and programs handle repetitive work while people review evidence and decide priorities and exceptions.
Master-data drafts built from documents, equipment communication setup, and report generation each run as a defined sequence, and every step leaves a record of what it produced.
Master-data drafting, protocol setup, report generation, schedule and task managementTechnology detailPrepare reports and shop-floor logs
Review anomalies and possible causes with the supporting data.
Sensor, production, and quality records are checked for anomalies and candidate causes. Programs do the arithmetic; AI drafts the explanation.
Anomaly detection, condition prediction, quality and cost analysisTechnology detailFind anomalies in production and equipment records
Older machines do not have to be replaced first. We start from the equipment already in use.
PLCs, sensors, barcodes, and cameras are connected, and data from ERP, MES, spreadsheets, and external APIs is attached to manufacturing records. For meters with no communication port, we are testing camera-based display reading.
Modbus, OPC UA, EtherNet/IP, MQTT, Asset Administration Shells (AAS), data collection and equipment informationTechnology detailConnect equipment to work records
People review AI-generated master-data drafts before saving them.
Vision results remain linked to source images. The system records who changed master data and when. Model-version tracking is in development.
User permissions, review steps, change history, audit trailTechnology detailReview master-data drafts and trace results
Projects using document reading, vision inspection, equipment data collection, and production management.
Development requests are read to draft item and partner master data, routings, and material lists, and an employee reviews the draft before it is saved.
Capabilities usedIndustrial AI agentsAutomation workflows
Product video was analyzed for quality grading, with labeling, a statistics database, and a review queue for incorrect classifications.
Capabilities usedIndustrial AI agentsOperational analytics
Robotic welding, quality, PLC, and IoT data were connected, and a shop-floor computer judged missing parts and wrote the result into the MES record.
Capabilities usedEquipment and system connectivitySite apps
An embedded equipment-data collection device, server software, a database, and shop-floor monitoring functions were developed.
Capabilities usedEquipment and system connectivityOperational analytics
These are public product materials and internal development materials. They are not customer operating screens.
Collect each source value and attach it to the right record. People review document-derived master-data drafts before saving, while vision results remain linked to source images.
Use values the current operation already produces. Nothing is invented that the site does not measure.
A number means nothing until it is attached to an item, a routing step, and a machine. Only then does AI read it.
People compare document-derived master-data drafts with the source before saving. Vision and analysis results remain linked to evidence when used in the next task.
Test a small scope first, check the saved records, then move to the next process.
AI drafts master data from documents and produces inspection results from images. Calculation software handles exact numbers. People review master-data drafts, set priorities, and handle exceptions.
Understands documents and questions, then finds the relevant evidence across operating records.
Calculate exact values for cost, inventory, accounting, and production capacity.
Apply defined equipment commands, operating limits, and workflow rules.
Decide customer promises, priorities, risk, approvals, and exceptions.
People compare AI-generated master-data drafts with source documents before saving them. Vision results remain linked to source images for later review.
Master-data drafts remain linked to source documents, and vision results remain linked to source images.
Programs calculate cost and output. AI prepares explanations from those validated results.
People compare master-data drafts with source documents before saving. Vision results remain linked to source images.
See the scope and current stage of equipment connections, master-data drafts, analysis, vision inspection, reporting, review workflows, and work support.
Bring values in from PLCs, sensors, robots, cameras, ERP, and MES, then attach them to item, process, and equipment records.
Read development requests, drawings, and specifications to draft item data, material lists, and routings.
Find anomalies and candidate causes in production, quality, and sensor records. Define the problem and benchmark before choosing an analysis method.
Use camera images to inspect missing parts and surface condition, and read values from gauges and display devices.
Query real QFactory data to draft tables, charts, and explanations. The scope is expanding to quality, food-safety, and cost reports.
Show AI-generated master-data drafts with source documents for review before saving. Keep vision results linked to source images.
Manage schedules, projects, and work status. QFactory Agent is being developed to find information within user permissions and suggest the next review step.
We review your equipment, documents, and systems, then begin with one machine, process, or task.
Plan the first test