The industry's first unified agentic AI platform that transforms scattered equipment sensor data into predictive intelligence — reducing unplanned downtime, preventing yield loss, and delivering measurable ROI from the telemetry your organization already produces.
Connect every piece of equipment in your facility — regardless of vendor or protocol. One centralized telemetry data plane that eliminates data silos, on-premises or in the cloud.
Detect equipment failures before they happen. 32 built-in CEP patterns and 15 autonomous AI agents identify anomalies, transform raw telemetry, and alert your team in sub-seconds.
Your data, fully governed and secure. Multi-tenant data mesh with unified catalog, data virtualization, and zero cross-tenant leakage — enabling Data-as-a-Product at enterprise scale.
Predict failures 7-14 days before they happen. End-to-end AI model lifecycle — from training to production serving — purpose-built for predictive maintenance, virtual metrology, and fault classification.
Purpose-built for high-velocity industrial telemetry — from sensor to insight in sub-seconds. Every layer works together so your engineering team doesn't have to stitch solutions together.
Interactive dashboards, KPI monitoring, natural language queries, executive reporting
15 specialized AI agents — anomaly detection, alert processing, data translation, RAG, research — orchestrated autonomously via agent-to-agent protocol
Graph-based equipment modeling with fault propagation analysis, cross-vendor sensor normalization, and rules-driven fault classification — the intelligence backbone
Open-format lakehouse with ACID transactions, time-travel versioning, data virtualization (cross-tenant shortcuts), and federated SQL query engine
Distributed stream processing and batch analytics with multi-node parallel compute — processing thousands of data points per second in real time
EUV lithography systems, process chambers, data center servers, network equipment — real-time sensor data acquisition at the edge
The industry's first graph-based device ontology purpose-built for manufacturing equipment. No existing platform offers equipment-specific knowledge graph modeling with fault propagation analysis.
Define equipment taxonomy, sensor hierarchy, and fault relationships using visual ontology editor
Connect equipment sensors, map data points to ontology nodes, configure ingestion rules
Auto-generate equipment dependency graph, fault propagation paths, and cross-system relationships
Run AI-assisted validation, monitor ontology drift, visualize equipment topology in real time
Push ontology to production, agents auto-discover new models, iterative refinement with zero downtime
Total cycle time: 1-2 business days for a new equipment model (vs. months with traditional approaches)
Trace how a failure in one component cascades across interconnected equipment. Identify root cause in seconds instead of hours of manual investigation.
Unified sensor data model across equipment from different manufacturers. One ontology connects ASML, Applied Materials, Lam Research, and KLA data seamlessly.
AI agents leverage the knowledge graph to reason about equipment relationships. Anomaly detection informed by equipment context, not just raw numbers.
Secure, governed, multi-tenant data management designed for enterprise organizations with strict isolation, compliance, and Data-as-a-Product requirements.
Complete namespace-level isolation with dedicated compute, storage, and network policies per tenant. Identity federation and centralized secret management ensure zero cross-tenant data leakage. Row-level security and column masking at the policy engine level.
Centralized metadata management with automated data discovery, data lineage tracking, and data quality scoring. Every dataset is a governed product with clear ownership, SLA, and schema versioning — enabling true Data-as-a-Product across the enterprise.
Open-format lakehouse with ACID transactions, time-travel, and schema evolution. Data virtualization shortcuts enable cross-tenant and cross-domain data access without physical data movement — query data where it lives, no duplication required. Federated SQL queries across the entire data mesh.
Enterprise-scale parallel compute comparable to leading cloud data platforms — process petabytes of telemetry data across distributed nodes.
Sub-second complex event processing with windowed aggregations, pattern matching, and stateful computations across millions of sensor streams simultaneously.
Multi-node parallel compute engine that auto-scales across available nodes. Run terabyte-scale batch jobs with fault tolerance, exactly-once semantics, and per-tenant compute isolation.
Purpose-built AI agents that collaborate autonomously via agent-to-agent protocol — each expert in a specific telemetry domain, orchestrated by a central coordinator.
Stream Processor — Real-time telemetry ingestion at 5,000+ pts/sec
Data Translator — Cross-protocol conversion (OPC UA, MQTT, SECS/GEM)
Data Storer — Intelligent storage orchestration across the data mesh
Anomaly Detector — SPC, Z-score, control chart analysis
Data Processor — Historical trends and batch forecasting
Alert Processor — Severity classification and SLA-aware escalation
RAG Agent — 6-phase adaptive retrieval with semantic cache
Research Agent — Web search and domain knowledge ingestion
Command Executor — Sandboxed code execution for ad-hoc analysis
+ Coordinator Agent (orchestration) • ML Trainer • Model Evaluator • Ontology Agent • Workflow Agent • Report Generator
130+ tools across 13 integrated backend services — all discoverable via Model Context Protocol
See how Syntrixia turns the 90% of unused sensor data into predictive intelligence — in a 30-minute live demo with your data.