AURA LLM — Industry-Specific Model
Overview
AURA is an industrial large language model (LLM) developed in-house by PlantPulse.
Built on the highest-performing open-source models and further trained (fine-tuned) on manufacturing and industrial domain data, it accurately understands the language, context, and technical nuances of the factory field.
Available Models
| Model | Purpose | Characteristics |
|---|---|---|
| AURA LLM | Conversation / analysis / report generation | Industrial fine-tuning, multimodal |
| AURA Embedding | Vector embedding | Optimized for RAG search |
| AURA Reranker | Reranking of search results | Precise relevance scoring |
Fine-Tuning Data
Official Standards Body Data
| Source | Content |
|---|---|
| KATS (Korea Occupational Safety and Health Agency) | Mandatory SOP guides based on the Occupational Safety and Health Act |
| ISO/IEC | Quality management SOP templates from international standards (ISO 9001, etc.) |
| OSHA/NIOSH | U.S. industrial safety standards, industry-specific safety procedures |
Field-Validated Data
| Data Type | Content | AI Capability Gained |
|---|---|---|
| Regulatory / safety documents | Internal safety manuals, MSDS, legal precedents | Improved accuracy in regulatory compliance and hazardous-situation response |
| Q&A / discussion data | Q&A among field technicians, in-house forums, incident reports | Better contextual understanding and ability to propose concrete solutions |
| Unstructured logs | Equipment failure records, maintenance logs, operator notes | Stronger predictive maintenance and troubleshooting inference |
Multimodal Support
- Text: Natural-language conversation, technical document comprehension, report generation
- Images: Equipment photo analysis, instrument panel reading, drawing interpretation
- Tables/Charts: Data table analysis, chart interpretation
AURA vs. General-Purpose LLMs
| Item | General-purpose LLM (GPT, Claude, etc.) | AURA |
|---|---|---|
| Industrial terminology comprehension | General level | Manufacturing-expert level |
| SOP/regulation interpretation | Lacks context | Interpretation grounded in legal basis |
| Data location | Cloud (transmitted externally) | On-premises (kept internal) |
| Cost structure | Per-token billing | Fixed cost, unlimited use |
| GPU requirements | Large GPU or API | Runs on low-spec hardware such as DGX Spark |
| Multimodal | Supported | Supported (specialized for industrial imagery) |
| Embedding / reranker | Requires separate models | Provided as an integrated package |
| Customization | Limited | Additional training on field data possible |
Technical Characteristics
Operation on Low-Spec GPUs
AURA runs in low-spec GPU environments such as NVIDIA DGX Spark. It enables on-premises deployment without large cloud GPUs, and is lightweight enough to run on edge servers in the factory field.
Full On-Premises Support
Dependence on external cloud APIs is eliminated, so sensitive factory data never leaves the site. AI services continue to operate even when network connectivity is lost.
Native Korean
AURA accurately understands the terminology used on Korean manufacturing floors. It interprets field terms such as "degradation," "interlock," and "trip" in their proper context, and understands the context of Korean occupational safety and health regulations.
Use Cases
| Scenario | How AURA Is Used |
|---|---|
| AI copilot conversation | Core conversation engine of PlantPulse AI Chat |
| Document search optimization | Improved RAG search accuracy via AURA Embedding |
| Anomaly root-cause interpretation | Cause inference from sensor data combined with industrial knowledge |
| Automatic report generation | Automatic drafting of daily and per-shift operations reports |
| Safety regulation lookup | Instant reference to KATS/ISO/OSHA-based regulations |
| New-hire training support | Expert-level answers to field questions |