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 sensor data your organization already produces.
Connect every equipment and device in your facility — regardless of vendor or protocol. One centralized telemetry data collection plane that allows data collection on-premises or in the cloud.
Sensor telemetry data process plane that provides various sensor data transformation and CEP capabilities. 15 autonomous AI agents transform raw sensor data, identify anomalies, and alert them in sub-seconds.
Your data, fully governed and secure. Multi-tenant data mesh with unified data catalog, data virtualization, and built-in data security — enabling Data-as-a-Product at enterprise scale.
Design AI models to predict failures 7 ~ 14 days before they happen. End-to-end AI model lifecycle — from training model to model serving as MaaS. Purpose-built for predictive maintenance, virtual metrology, and fault detection & classification (FDC).
Knowledge graph that models the full equipment lifecycle — machine, subsystem, component, and sensor. An industry-agnostic upper model governs domain-specific layers for semiconductor, data center, medical, and telecom, letting AI agents reason across multi-vendor fleets and trace fault causality fab-wide.
Purpose-built for high-velocity industrial telemetry data — from sensor to insight in sub-seconds. Every layer works together so your engineering team doesn't have to stitch solutions together. Additional AIOps capability is available for hyper-scale data center and telecommunication network provider.
Interactive and configurable metrics dashboards, KPI monitoring, natural language queries, executive operational reporting, AIOps support
16 specialized AI agents — anomaly detection, alert processing, data translation, RAG, research, and more — orchestrated autonomously via agent-to-agent protocol (A2A)
Graph-based equipment modeling with fault propagation analysis, cross-component sensor normalization, equipment uniformity detection, and AI-driven fault detection and classification with traceability
Open-format lakehouse with ACID transaction support, time-travel versioning, data virtualization (multi-datasource correlation), 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
Medical devices, manufacturing equipment, data center servers, network equipment — real-time sensor data acquisition at the edge and operational facilities
The industry's first graph-based equipment ontology purpose-built for manufacturing equipment and devices. 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-equipment relationships
Run AI-assisted validation, monitor ontology drift, visualize equipment topology in real time
Push equipment ontology to production, agents auto-discover new models, iterative refinement with zero downtime
Total cycle time: 1~2 business days for a new equipment ontology 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 models and manufacturers. One ontology model connects same process equipment from 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 data isolation, security, compliance, and Data-as-a-Product (DaaS) 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 data security and column masking with secure policy engine.
Centralized metadata management with automated data discovery, data lineage tracking, and data quality measurement. Every dataset is a governed product with schema versioning, clear ownership and SLA — enabling true Data-as-a-Product (DaaS) across the enterprise.
Open-format lakehouse with ACID transactions, time-travel, and schema evolution. Data virtualization enables cross-domain data access within the tenant 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 (CEP) with windowed aggregations, pattern matching, and stateful computations across millions of sensor streams simultaneously.
Multi-node parallel compute engine that auto-scales across all available compute nodes. Run terabyte-scale batch jobs with fault tolerance, exactly-once semantics, and per-tenant compute isolation.
Every organization on Syntrixia® gets its own identity boundary and its own isolated workspace, while the platform services underneath stay shared. One operational estate to run; complete separation for the people using it.
Each organization has its own identity boundary. Every session carries the caller's four-level tenancy claim.
Fail-closed policy enforcement. Every query is rewritten with the caller's tenancy filter and sensitive fields are masked before results are returned.
Ingestion · stream processing · workflow orchestration · data catalog and lineage · model serving · vector search · knowledge graph · observability.
Separation lives on the record, not in duplicated storage:
Shared platform, separated tenants — siloed where it protects your data, pooled where it saves you money.
Each organization is siloed at the identity and workspace layer, while storage, analytics and models stay pooled for cost and scale.
Organization, business unit, site and facility travel with the data as machine keys plus display labels — so every row knows exactly where it came from and who may see it.
Catalog access control, workspace isolation with quotas, and row-level filtering operate independently. No single misconfiguration exposes another organization.
Designed to meet privacy, residency and regulated-industry requirements, with per-organization audit trails and lineage.
The same isolation model deploys on-premises, in a private region, or in a public cloud region. Data residency is a deployment choice, not a re-architecture.
See how Syntrixia® turns the 90% of unused sensor data into predictive intelligence — in a 30-minute live demo with your data.