Know which equipment needs attention 7-14 days before failure — not after. End-to-end AI model lifecycle management purpose-built for industrial equipment: predictive maintenance, virtual metrology, and semiconductor process control.
Graph-based failure prediction across equipment dependency chains. Identify which equipment needs attention 7-14 days before failure — not after.
Predict wafer quality from equipment sensors without physical measurement. Reduce metrology sampling by up to 60% while maintaining yield targets.
Native SPC & APC integration with real-time recipe optimization. Automated run-to-run control that adapts to equipment drift and environmental changes.
Production-ready MaaS (Model as Service) trained on equipment and device-specific ontology — not generic cloud AI services forced into a manufacturing context.
ModellusFabrica™ provides cloud native platform to design, implement and deploy MaaS for equipment and IIoT devices
Multi-class equipment fault detection and classification using temporal pattern recognition. Trained on equipment-specific failure modes — not generic anomaly detection. Distinguishes between 50+ fault types across lithography, etch, deposition, and metrology equipment.
Estimate remaining useful life of critical components — both consumable and durable parts such as epoxies, implanter plasma, wafer load port bearings, laser gases. Schedule maintenance during planned maintenance windows, eliminating unplanned outages.
Multivariate anomaly detection across correlated near real time sensor data streams. Detects subtle equipment degradation that single-variable thresholds miss — including wafer alignment drift, recipe drift, reticle contamination, and multi-chamber correlation anomalies.
Graph-based recipe path optimization using equipment performance clustering. Find the optimal process parameters to maximize yield while maintaining equipment health — automatically adapting to chamber-to-chamber variation.
From experiment ML model tracking to production serving with MaaS (Model as Service) — fully integrated with the Syntrixia® data lakehouse and agentic AI platform.
Track ml model experiments, hyperparameters, metrics, and artifacts. Compare model versions with full reproducibility.
Distributed ml model training on GPU clusters. Automated feature engineering using data from the lakehouse using data pipelines.
One-click ml model deployment to production inference endpoints. Auto-scaling based on equipment count and sensor data volume.
Real-time model performance monitoring. Automatic drift detection and retraining triggers when equipment behavior changes.
Purpose-built AI agents that collaborate autonomously via agent-to-agent protocol — each agent is an expert in a specific equipment telemetry domain, orchestrated by a central coordinator agent.
Stream Processor — Real-time telemetry ingestion and CEP at 5,000+ pts/sec
Data Translator — Cross-protocol conversion (OPC UA, MQTT, SECS/GEM)
Data Storer — Intelligent storage orchestration across the telemetry lakehouse
Anomaly Detector — SPC, APC, ML model based pattern detection and analysis
Data Processor — Historical trends and time-series forecasting
Alert Processor — Severity classification and early warning notification
RAG Agent — 6-phase adaptive retrieval with semantic cache
Research Agent — Web search and domain knowledge ingestion for model training
Command Executor — Sandboxed code execution for ad-hoc analysis & execution
+ Coordinator Agent (orchestration) • ML Model Trainer • ML Model Evaluator • Equipment Ontology Agent • Notification Agent • Report Generator Agent
153+ MCP tools across backend microservices — all discoverable via Model Context Protocol (MCP)
Telemetry AI agent has instant access to the full platform capability stack — from database queries to model registries to equipment ontology — all discoverable and invocable through A2A and MCP protocols, supporting custom Multi-Agent workflow design.
| Capability Category | Services | MCP Tools | What Agents Can Do |
|---|---|---|---|
| Structured Data Access | 2 | 34 | Query telemetry databases, execute SQL, manage schemas, cache query results |
| Data Search & Retrieval | 2 | 28 | Full-text search across documents, vector similarity search, hybrid data retrieval |
| Telemetry Data Lakehouse | 3 | 30 | Manage data catalogs, object storage, table versioning, federated SQL queries |
| Graph Analytics | 1 | 14 | Equipment dependency graphs, fault propagation analysis, recipe optimization |
| ML Model Lifecycle | 1 | 7 | Track model experiments, register models, compare runs, deploy to production |
| Data Schema & Governance | 1 | 3 | Manage data and equipment event schemas, version compatibility, data format validation |
| Equipment & Device Ontology | 1 | 18 | Query equipment & device hierarchies, sensor relationships, fault classification rules |
| Data Virtualization | 1 | 12 | Cross-source queries spanning lakehouse, databases, and external systems using fedrated SQL |
| Development & Compute Clusters | 2 | 7 | Dynamic cluster provisioning, interactive notebooks, distributed compute, ad-hoc analysis |
| AI Agent RAG & Knowledge Management | 1 | 4 | Search equipment vendor documentation, crawl technical resources, extract knowledge |
| Total | 16 | 153+ |
All MCP tools are registered with MCP registry and custom MCP tool development is supported - automatically discoverable by AI agents with no manual configuration required. Agents select the right tool for each task based on context, equipment type, and data availability.
ASML EUV lithography process control, Applied Materials CVD/PVD chamber optimization, Lam Research etch endpoint prediction, wafer-level defect classification correlated with equipment telemetry. 32 built-in CEP (Complex Event Processing) patterns detect equipment degradation before it impacts yield.
Server component RUL estimation (CPU, memory, storage, PSU), rack-level thermal anomaly correlation, firmware regression detection across fleet, and predictive power optimization — reducing unplanned downtime by up to 40%.
Models trained on equipment-specific ontology — not generic time-series algorithms. The knowledge graph informs every prediction with equipment context, fault history, and maintenance records.
Once data pipelines are set up, AI agents autonomously trigger model training, evaluate results, and promote to production — without human-in-the-loop for routine operations. ML Engineers focus on ml model development, not MLOps pipeline babysitting.
Models read directly from the DataFabrica™ Lakehouse medallion zones. Bronze for raw training data, silver for features, golden for production inference. No data pipelines to maintain between ML model and data platforms.
See how Syntrixia® ModellusFabrica™ AI delivers equipment-native predictive intelligence — from semiconductor fabs to hyperscale data centers.