When AI is wrong on a factory floor, the line stops. So we ground every answer in the ontology, hand over control only as far as it has been verified, and pursue autonomy only in factories that have been standardized.
A general chatbot produces plausible sentences, but a factory floor demands evidence, not plausibility. VEXPLOR retrieves across five paths at once to cross-check the evidence, and verifies the reasoning path itself as a graph.
Because an ontology already defines how items, equipment, processes and quality connect, an answer can only come from within those defined relationships.
FIG.3 — KNOWLEDGE GRAPHAAF splits the AI's authority into five levels. The factory decides how far to allow, and commands that violate physical constraints are blocked by SHACL rules before execution. It is designed to meet the governance requirements of regulated industries (IATF 16949, FDA, ISO 13485).
Data viewing and status queries only
READ-ONLYSubmits its judgments as proposals only
PROPOSE · HUMAN DECIDESExecutes after operator approval
HUMAN-IN-THE-LOOPExecutes automatically within a verified scope
BOUNDED AUTONOMY · AUDIT LOGImmediate intervention on safety events
SAFETY OVERRIDEThe three core AIs — predictive maintenance, quality prediction and process optimization — built and applied as field projects.
SITES: NKID · ALPET (IN PROGRESS)Expanded into an agent platform on top of the ontology. Six sites across Gen-1 and Gen-2 combined.
SITES: JUSTEM · SUNYOUNG KOREA · SPEEFOX GEOMDAN · RPS (4 SELECTED 2026)We led the consortium developing the national manufacturing-data standardization reference model (AAS/IEC 63278), and the product is designed to meet the requirements of regulated industries such as IATF 16949, FDA and ISO 13485. Equipment integration follows open standards like OPC-UA and MQTT.
20+ build sites, documented by name and technical stack.