AI agents integrated with live FN Cloud IoT streams and ERP/CMMS data that autonomously monitor plant conditions, identify developing issues, escalate alerts with contextual explanations, and automatically generate maintenance work orders. Operators interact via natural language chat.
Agents don't just notify — they generate work orders, escalate issues, and recommend corrective actions autonomously.
Ask your plant data questions in plain language and receive data-grounded explanations instantly.
RAG over your operational knowledge base ensures responses are specific to your plant, not generic.
Agents monitor continuously without operator attention, escalating only when genuine intervention is needed.
Work orders and procurement requests generated by agents flow directly into your existing CMMS and ERP systems.
Agents continuously monitor live IoT streams, identifying developing issues before they become failures.
Ask questions like "Why did Line 3 efficiency drop this morning?" and receive data-grounded explanations.
When IoT anomalies are detected, agents automatically generate and route corrective maintenance work orders.
Escalation alerts include contextual root cause analysis, not just alarm codes.
Agents connect to live FN Cloud data streams and write back to CMMS and ERP systems automatically.
Retrieval-augmented generation over your SOPs, maintenance logs, and historical data ensures grounded, accurate responses.
LLM-Based Reasoning EngineLarge language model reasoning engine interprets IoT data in operational context to generate human-readable explanations and recommendations.
Retrieval-Augmented Generation (RAG)RAG pipeline over plant SOPs, maintenance history, and equipment manuals ensures factually grounded responses.
Multi-Agent OrchestrationSpecialized sub-agents handle energy, equipment health, quality, and safety monitoring in parallel for comprehensive plant coverage.
Agents connect to live FN Cloud IoT streams, CMMS data, and ERP records, monitoring all feeds simultaneously.
When sensor patterns deviate, the reasoning engine analyses context, retrieves relevant historical data, and determines the most probable root cause.
Agents take pre-approved autonomous actions (creating work orders, sending notifications) and escalate issues requiring human decision with full context.
Operators interact with the agent via chat interface — asking questions, requesting reports, or querying historical patterns in plain language.
Agents monitor turbine, generator, and auxiliary system health continuously, generating predictive maintenance work orders before equipment failures.
Agents monitor GMP-critical environmental parameters and automatically escalate deviations with cGMP-compliant documentation.
Agents track process variables and safety interlock status, automatically alerting operators to developing safety risks with contextual explanation.
Agents monitor multiple plant locations simultaneously, providing centralized intelligence without requiring site-specific operator attention.
Agents automatically generate shift handover summaries with key events, anomalies, and pending work orders from the preceding shift.
Respond to management queries about plant performance, energy consumption, and maintenance KPIs in natural language from live data.