Product Vision
The Problems PlantPulse AI Solves
Manufacturing sites today face three core challenges:
Data Silos
SCADA, MES, ERP, and document systems all operate in isolation. Pinpointing a single anomaly means moving between multiple systems to gather data, organizing it in Excel, and digging through manuals — 30 minutes to an hour of work.
Reactive Operations
Most plants rely on rule-based alarms. By the time an alarm sounds, the problem has already occurred. Preventive maintenance is interval-based, performed without regard to the actual condition of the equipment.
Dependence on Experienced Personnel
Critical technical knowledge rests on the experience of a few veterans. When they retire, that tacit knowledge is lost, and training new staff takes a long time.
The PlantPulse AI Approach
PlantPulse AI solves these problems through a single natural-language interface.
"주입기 상태 분석해줘"
From this one question, the AI automatically:
- Retrieves real-time sensor data (integrated MCP, 116 data tools)
- Performs anomaly detection and prediction (TimeSeries-Insight, IBM Granite TSPulse)
- Maps the equipment relationship structure (knowledge graph — integrated 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.
There is a single entry point for asking. Copilot, the app people actually work in, raises problems the AI has already found as suggestions backed by evidence; a person approves them, and the loop closes through action, outcome, and recurrence verification, while repetitive checks are handed off to automation. It is a field decision app used in three ways — ask, suggest, automate (the sidebar has New Chat, Suggestions, and Automation; asking starts with New Chat) → Copilot — The Field Decision App
What Sets It Apart
| Conventional Solutions | PlantPulse AI |
|---|---|
| Dashboard-centric — the user has to go looking | The AI finds problems and proposes them with evidence — ask in natural language when you don't know |
| 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 | Native Korean — understands field terminology |
| Expert tool | Usable by anyone — natural-language interface |