A proprietary AI-native lifecycle. AI does the heavy lifting, accelerating builds and testing. Humans guide the strategy, ensure safety, and make the executive decisions.
Edge Discovery & Telemetry Analysis
The Fused Multi-Modal Context Agent rapidly parses factory manuals, P&IDs, and SOPs using Docling and LlamaParse. Discovery agents simultaneously map available CCTV and contextual sensor streams.
CognusOps domain experts meet with plant stakeholders to capture operational nuances, establishing precise use cases like hazard mitigation or swing zone safety limits.
Multimodal Knowledge Graphing
Multi-Modal Fusion Microservices generate rich scene descriptions and contextual embeddings, indexing them into self-hosted databases like Milvus or Qdrant.
Operations SMEs validate the physical-to-digital layout, verifying the GUID-to-tag registry to ensure digital twins map accurately to live TimescaleDB telemetry.
Agentic Workflow & Guardrails
AI agents generate the structured schema for LangChain-orchestrated specialized agents, explicitly detailing the logic for Event-Driven Workflow Automation and Autonomous Robot Tasks.
Engineers define "Tier 1" deterministic rules and physics-based limits (e.g., ISO 10816 vibration zones), establishing guardrails to reserve immediate safety-critical actions for the deterministic fast path.
Sovereign Edge Hardware
Simulation tools dynamically calculate compute and concurrency requirements based on the facility's expected tag volume and dense video stream ingestion rates.
Network architects design a robust Purdue-aligned topology in a Level 3.5 DMZ with zero inbound ports. Hardware is sized from T1 (Jetson Orin NX) for pilots to T3 (RTX 6000 Ada) for plant-wide offline resilience.
Parallel Stack Deployment
The local generative AI stack is deployed via Ollama or vLLM to host MLLMs like LLaVA. Small Language Models (3B–14B) are LoRA-tuned locally on the plant's unique vocabulary and historical work-order corpus.
Technicians install edge hardware nodes, establishing control loops via EMQX MQTT broker and TimescaleDB. Edge vision is configured using NVIDIA DeepStream for low-latency ingestion.
Intelligent V&V
The anomaly detection pipeline (Autoencoder + Isolation Forest) is tested. The Real-Time Risk Agent applies visual safety guardrails to correlate simulated events, like matching sensor pressure drops with thermal visual leak confirmation.
Plant SMEs validate Agentic work orders and test RAG "Ask the Twin" queries, ensuring the SLM answers strictly by citing the database and never hallucinates from model memory.
Security Hardening
Compliance agents automatically audit the deployment against IEC 62443 requirements, verifying LUKS encryption for data at rest and mTLS for data in transit across edge nodes.
IT security validates identity protocols (Client IdP/RBAC) and performs the ultimate zero-trust check: verifying that the entire stack can operate with the internet cable fully unplugged.
Orchestrated Cutover
GitOps orchestrates the seamless deployment of the containerized K3s stack and Longhorn storage. Unsupervised anomaly models initiate their 2-4 week baseline learning period.
The deployment lead executes the cutover to the Multimodal Sovereign Visualization Portal, achieving the milestone of detecting anomalies before traditional SCADA alarms trigger.
Copilot Enablement
Generative AI auto-generates compliance reports, end-of-shift summaries, and structured draft work orders injected directly into the CMMS via strictly constrained JSON schema.
Field technicians utilize the grounded RAG Troubleshooting Copilot. By clicking an asset in the 3D twin, they receive guided instructions mapped directly to local OEM manuals and SOPs.
Continuous Edge MLOps
As real failure events accumulate, the anomaly models upgrade from condition monitoring to learned Remaining Useful Life (RUL) estimation using advanced survival models.
A Human-in-the-Loop continuous feedback cycle is established. Every dismissed false alarm or edited work order acts as labeled data, compounding the edge model's accuracy month over month.