VEXPLOR
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M-03 · VEXPLOR INSIGHT

When the AI is wrong, the factory stops.So every answer is checked against the ontology.

Insight is the hallucination-free manufacturing AI analytics module. It retrieves through five parallel paths to cross-check the evidence, and delivers predictive maintenance, anomaly detection, quality prediction and vision AI.

HYBRID GRAPHRAG ×5 (4-WAY + GOT) · NEUROSYMBOLIC CAUSAL (PEARL SCM) · PREDICTIVE AI
VEXPLOR INSIGHT · 4-WAY RAG Q&A
Insight 4-Way RAG question answering — product screen
FIG — ANSWERS THAT COME WITH THEIR ONTOLOGY EVIDENCE · PRODUCT SCREENScreen data is illustrative
QUERY → RETRIEVE ×5 [ GRAPH · VECTOR · SQL · DOC · GOT ] → ONTOLOGY CHECK [ cross-check against 311+ relations · discard ungrounded paths ] → CAUSAL [ PEARL SCM ] → ANSWER + EVIDENCEFIG — HALLUCINATION-FREE PIPELINE · Every answer comes with its evidence
01PROBLEMPLAUSIBLE IS NOT ENOUGH

One quality incident,
three hours of root-cause analysis.

The data is there, but the answer comes late. General-purpose chatbots produce plausible wrong answers — and on a factory floor, the price of a wrong answer is downtime.

  • Hybrid GraphRAG, 5 pathsGraph, Vector, SQL and Doc 4-Way, plus GoT (Graph of Thoughts), cross-checking the evidence for every answer against the ontology.PATHSGRAPH · VECTOR · SQL · DOC + GOT — PARALLEL RETRIEVAL · EVIDENCE CROSS-CHECK
  • Neuro-symbolic causal inferenceIt answers "why" with causation, not correlation (Pearl SCM based).ENGINEPEARL SCM · 6-TIER MEMORY
  • Process prediction AIPredictive maintenance (LSTM AE+CNN), quality prediction (LSTM-CNN+Attention), anomaly detection, vision AI.MODELSLSTM AE+CNN · LSTM-CNN+ATTENTION · ANOMALY DETECTION · VISION AI
PRODUCT BENCHMARK3 hours → 30 secQuality root-cause analysis
PRODUCT BENCHMARK2 hours → 1 minEquipment impact analysis
PRODUCT BENCHMARK4 hours → 1 minRecall scope identification

In rollout across six manufacturing-AI project sites.

Predictive maintenance, quality prediction and agents are being built as on-site projects.

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