Engineered for scale. Honed for agility.
Industrial sensors have been deployed for 20–30 years. Refineries run 50K–200K PI tags at 1Hz. Less than 10% of that data drives closed-loop action today. The unit-economics liberation — somewhere between $100B and $300B per year — is gated on one engineering problem: the systems-integration layer. Full-Stack Edge AI is the substrate that solves it. Ruggedized on-prem compute, a 20–50 adapter connector library, LLM-driven schema mapping that compresses weeks of integration into hours, safety-bounded write-back, and air-gappable variants for ITAR, NERC CIP, and CMMC L3.
The Cognite generation took weeks. We take hours.
Industrial AI has had one bottleneck for a decade: making the customer’s PI Asset Framework, OPC UA address space, MES tags, and ERP records readable by a model. Cognite, Element, C3.ai, Uptake, AspenTech each shipped systems-integration projects that took weeks per asset. The economics never worked.
Full-Stack Edge AI compresses the integration layer to hours. LLM-driven schema mapping reads customer tag dictionaries, PI Asset Framework hierarchies, and OEM-specific vocabularies, then aligns them against canonical schemas with quality-validation guardrails. ~20–50 pre-built connectors already cover historians (PI / IP.21 / Proficy / SIMATIC / PHD / Exaquantum), MES (AVEVA / Forge / Opcenter / FactoryTalk / Tulip / FactoryLogix), ERP (SAP / Oracle / Dynamics / Infor / IFS), CMMS, LIMS, PLM, OPC UA, MTConnect, WITSML, IEC 61850 — and CAE bridges to Ansys, Aspen, Eclipse, HYSYS, ABAQUS, CATIA-Simulia, Teamcenter, Windchill where the operator’s engineering loop participates in the closed loop.
What the substrate guarantees
Schema mapping. Connector breadth. Latency at the asset.
Three guarantees that turn AI from a cloud-shaped feature into industrial infrastructure — schema mapping that compresses weeks to hours, a connector library that already covers ~90% of the installed base, and inference that lives where the assets do.
LLM-driven schema mapping
The systems-integration problem that took the Cognite / Element / C3.ai generation weeks per asset, compressed to hours. LLM reads customer tag dictionaries, PI Asset Framework hierarchies, and OEM vocabularies; semantic alignment against canonical schemas; quality validation on units / ranges / periodicity. Every customer onboarded sharpens the mapping for the next.
Connector library, not a project
~20–50 pre-built adapters covering ~90% of installed industrial data sources globally. Historians, OPC UA, MTConnect, WITSML, IEC 61850, MES, ERP, CMMS, LIMS, PLM. Plus CAE bridges (Ansys, Aspen, Eclipse, HYSYS, ABAQUS, CATIA-Simulia, Teamcenter, Windchill) where the engineering loop participates. Authentication, sampling-rate, unit, and quality-code handling per source.
On-prem-first, latency-honest
Sub-50ms BESS regulation response. EAF furnace sub-second control. Autonomous cell ms-scale. 100K PI tags × 1Hz × 100 plants = TB/day to cloud — the math doesn’t work. Edge runtime on Jetson AGX Orin, NVIDIA Thor, AMD MI300, Hailo, Coral, Groq. TensorRT-optimized, INT8/INT4 where appropriate.
The substrate in action
From tag dictionary to inference, in under two seconds.
A customer’s PI Asset Framework, OPC UA address space, or OEM tag vocabulary maps to canonical schemas via LLM-driven alignment, validates against unit / range / periodicity guardrails, and activates inference at the edge — without weeks of bespoke integration.
Weeks of integration → hours of mapping + validation
LLM-driven schema mapping reads the customer’s tag dictionary, validates against canonical schemas, and activates inference — all before the integrator’s first invoice would have landed.
Compute. Connectors. Mapping. Test surface.
Ruggedized on-prem nodes, ~20–50 connectors covering most of the installed base, LLM-driven schema mapping, plus an engineering-test deployment surface that extends North into the hardware-development-tooling adjacency.
Jetson AGX Orin / Thor / MI300 / Hailo / Coral / Groq. Fanless, vibration-resistant, DIN-rail mount. TPM-rooted identity. Multi-tenant slicing where multiple workloads share a node. (Deep OT security treatment on the OT Security page.)
20–50 pre-built adapters: PI / IP.21 / Proficy / SIMATIC / PHD / Exaquantum historians; AVEVA / Forge / Opcenter / FactoryTalk / Tulip / FactoryLogix MES; SAP / Oracle / Dynamics / Infor ERP; Maximo / Infor EAM / Fiix CMMS; LabWare / SampleManager / STARLIMS LIMS; Teamcenter / Windchill / 3DEXPERIENCE / Aras PLM.
Tag dictionary ingestion, semantic alignment against canonical schemas, quality validation on units / ranges / periodicity. Every onboarding improves the next. Weeks → hours.
Same architecture extends to test-rig DAQ (LabVIEW / NI PXI), engine test cells, wind-tunnel, vibration shakers, EMC chambers. The deployment surface stretches into the hardware-development-tooling adjacency.
From data accumulated to value liberated.
One node, the connector library, LLM-driven mapping, sub-50ms inference — provisioned in hours, paying back in days.