Product Vision
The Problems PlantPulse AI Solves
Manufacturing sites today face three core challenges:
Data Silos
SCADA, MES, ERP, and document systems each operate in isolation. Identifying a single anomaly means moving between multiple systems to gather data, organizing it in Excel, and digging through manuals — a process that takes anywhere from 30 minutes to an hour.
Reactive Operations
Most plants rely on rule-based alarms. By the time an alarm sounds, the problem has already occurred. Preventive maintenance is schedule-based, so it is performed regardless of actual equipment condition.
Dependence on Skilled Personnel
Core technical knowledge resides in the experience of a handful of veterans. When they retire, that tacit knowledge is lost, and training new staff takes a long time.
The PlantPulse AI Approach
PlantPulse AI addresses these problems through a single natural language interface.
"주입기 상태 분석해줘"
From this one question, the AI automatically:
- Queries real-time sensor data (Unified MCP, 116 data tools)
- Performs anomaly detection and prediction (TimeSeries-Insight, IBM Granite TSPulse)
- Maps the equipment relationship structure (knowledge graph — Unified MCP, Neo4j)
- Searches relevant manuals (RAG — LightRAG, hybrid search)
- Generates a comprehensive analysis report (AURA LLM, industry-specific model)
The entire process completes in under 10 seconds.
What Sets It Apart
| Conventional Solutions | PlantPulse AI |
|---|---|
| Dashboard-centric — the user has to go looking | The AI finds and delivers the answer — natural language questions |
| Single-domain analysis | Cross-domain integrated analysis (sensors + OEE + alarms + documents) |
| Rule-based alarms | AI-based anomaly detection + prediction |
| General-purpose AI | Industry-specific AURA LLM — trained on the manufacturing domain |
| Cloud-dependent | On-premises/edge deployment — DGX Spark supported |
| English-centric | Korean-native — understands field terminology |
| Expert-only tooling | Usable by anyone — natural language interface |