AI Platform Technical White Paper
This is the PlantPulse AI Platform Technical White Paper. It explains the structure of the API modules that make up the AI platform — what each module is responsible for, which APIs it exposes, and which data it owns — along with the safeguards that keep answers from being wrong and keep the platform from writing to the plant carelessly. It is aimed at technical reviewers (IT · OT architects, AI adoption evaluators).
Download
| Language | Pages | File |
|---|---|---|
| Korean | 22 pages | PlantPulse AI Platform Technical White Paper (PDF) |
| English | 22 pages | PlantPulse AI Platform Technical White Paper (PDF) — same content as the Korean edition |
Revision 2026.11. Only figures recorded as measured in the documentation are included, and every page cites its sources.
What's inside
| Chapter | Contents |
|---|---|
| 01 | Problem and approach — decision app · three principles |
| 02 | Overall structure — layers · gate · connections |
| 03 | Module map — one owner per body of data |
| 04 | Decision app — Ask · Tasks · Automate |
| 05 | External entry — public API · on-behalf |
| 06 | MCP gateway — tools · permissions · search |
| 07 | Time-series insight — findings · forecast |
| 08 | Digital thread — graph and connectors |
| 09 | RAG — documents · video · drawings |
| 10 | Document understanding · vision |
| 11 | Engineering calc · automation sandbox |
| 12 | Voice · file storage · long-term memory |
| 13 | Inference stack · admin console · edge proxy |
| 14 | Safeguards ① — evidence guard · «unknown» |
| 15 | Safeguards ② — scope · approval · audit |
| 16 | Deployment — on-prem · air-gapped · signed |
| 17 | Monthly release and hardware tiers |
| 18 | Evaluation ① — machine-scored benchmarks |
| 19 | Evaluation ② — measured · what's next |
If we may recommend one reading order
Start with Chapter 03 (Module map). All fourteen modules are in a single table, each leading to its own chapter. If reliability is your first question, you can go straight to Chapters 14 · 15.
See also
Architecture · AURA Technical White Paper — how the models are built and measured · Data Lake Technical White Paper