An industrial data plane is the layer where data from physical systems is acquired, structured, and prepared before it reaches any downstream system. This layer does not exist in traditional industrial architectures. KŌJŌ Stack establishes it at the first mile.
The Data Boundary
On one side: operational systems-PLCs, sensors, SCADA, and historians. These generate raw, protocol-bound data at high frequency, with no inherent structure beyond device-level addressing.
On the other: enterprise and cloud systems-analytics platforms, AI/ML pipelines, data lakes, and business applications. These require structured, contextualized, reliable data to function.
KŌJŌ Stack is the data boundary between them. It is the layer where data is acquired, structured, filtered, and routed-where raw telemetry becomes usable. This is the industrial data plane. Without it, every downstream system must independently reconstruct data from raw, protocol-bound sources.
OT Domain
PLCs · Sensors · SCADA · Historians
Raw, protocol-bound, unstructured
Data Boundary
KŌJŌ Stack - First-Mile Data Plane
Ingest · Structure · Condition · Route
IT / Cloud Domain
Analytics · AI/ML · Data Lakes · Apps
Structured, contextualized, reliable
Left Side of the Boundary
Industrial data originates at PLCs, sensors, SCADA systems, and historians-each speaking different protocols with different timing models, data formats, and addressing schemes. Data here is raw, unstructured, and protocol-bound.
Subscriptions, Browse, security policies
Register polling, coils, discrete inputs
Native protocol, no OPC server required
COV subscriptions, polling modes
Allen-Bradley, Rockwell ecosystems
100% spec-compliant MQTT industrial standard
Utility SCADA, polling + unsolicited modes
Broker ingress, topic filtering
The problem: each protocol has its own transport, timing model, data representation, and addressing. Without a structuring layer, every downstream system must independently solve protocol translation, context mapping, and reliability-leading to fragmented, inconsistent, and brittle architectures.
The Data Plane
The industrial data plane is where raw telemetry becomes structured, contextualized, and reliable. KŌJŌ Stack owns twelve responsibilities that define what data exists downstream and how it behaves. Every responsibility executes at the edge, with deterministic behavior and bounded latency.
Downstream systems do not define data structures-KŌJŌ Stack does.
This is the layer where industrial data becomes usable.
Protocol-native ingestion at deterministic intervals or server-push subscriptions. Each adapter speaks the native language of the device-no translation gateways. Timestamps, quality indicators, and device metadata are preserved at the point of origin.
Normalization, scaling, unit conversion, and enrichment using the CEL expression engine. Report-by-Exception (RBE) deadband filtering reduces data volume by 90%+ while preserving all meaningful state transitions. Transforms are pure functions-same input, same output, always.
Every data point is mapped into an ISA-95 compliant Unified Namespace hierarchy: Enterprise → Site → Area → Line → Cell. Tags carry identity, timestamp, quality, and source context. The namespace is the contract between all producers and consumers.
Event-driven, deterministic pipelines with bounded latency. Data is structured and prepared before downstream systems consume it-triggered by state changes, not arbitrary polling intervals. Events are delivered in a consistent, deterministic sequence within local pipeline execution paths. Behavior is reproducible and auditable.
Durable local buffering maintains data continuity during network interruptions. Data is persisted before acknowledgment. On reconnection, buffered data replays in order with original timestamps preserved. Delivery guarantees are a function of the buffering and replay mechanism, not external SLAs.
External modules communicate with the core runtime over typed, versioned interfaces. Each module has independent lifecycle: deploy, start, stop, update, and remove without affecting other components. OEMs and developers extend the data plane with new protocol adapters, transforms, or connectors-without modifying core code.
The SDK enables developers to extend the data plane to any system-building custom source adapters, destination connectors, and processing modules that integrate natively with the runtime. Partners and OEMs use the SDK to embed KŌJŌ Stack capabilities into their own products. The SDK is the extensibility layer of the data plane.
Workloads execute alongside data pipelines in a shared runtime, ensuring processing occurs where data is acquired and structured. AI inference, protocol adapters, analytics engines, and custom logic operate as managed workloads with lifecycle control, health checks, and secret injection-co-located with the data plane at the edge.
Per-pipeline throughput, latency (p50/p95/p99), error rates, and backpressure metrics. Module health monitoring with connection status, buffer depth, and protocol diagnostics. Hash-chained audit trail for compliance. Operational control requires operational visibility.
120+ REST API endpoints with OpenAPI 3.0 specification and interactive Swagger UI. Every platform capability - sources, pipelines, destinations, modules, workloads, secrets, certificates - is API-accessible. Designed for automation, CI/CD pipelines, and infrastructure-as-code workflows.
Centralized deployment, orchestration, and monitoring of all edge nodes across sites, regions, and environments. Push configuration updates, namespace models, and pipeline definitions with controlled rollout. Aggregate telemetry and health across the entire fleet from a single control interface.
A fleet-aware MCP (Model Context Protocol) server deployed at the edge enables AI agents to discover, query, and diagnose edge deployments. OAuth 2.0 identity with three-axis scoping, 22 read-only tools, and team-level isolation - designed for safe autonomous operation across distributed edge infrastructure.
Right Side of the Boundary
Cloud platforms, historians, data lakes, AI/ML systems, and enterprise applications depend on structured, reliable data produced by the first-mile data plane. KŌJŌ Stack determines what data these systems receive-and in what form.
Lakehouse with time travel
Event streaming, SASL/TLS
Time-series historian
Cloud IoT, QoS 0/1/2
X.509 certificate auth
JSONL/CSV/Parquet batch export
JSONL/CSV/Parquet, BigQuery compatible
Time-series historian, Line Protocol
The result: every downstream system receives data that is already structured, contextualized, and quality-annotated. No ETL pipelines to reconstruct meaning. No data quality issues to debug. No protocol-specific logic in analytics code.
How Data Moves
KŌJŌ Stack does not passively transport data from source to destination. It actively determines what data exists downstream by ingesting, structuring, processing, and distributing it. Data is structured and prepared before downstream systems consume it.
Protocol-native acquisition from OT systems
ISA-95 contextualization and schema normalization
CEL transforms, RBE filtering, quality annotation
Buffered, reliable delivery to every destination
KŌJŌ Stack determines what data exists downstream.
No downstream system sees data that the data plane has not explicitly acquired, structured, and routed. This is first-mile data ownership.
Architectural Position
KŌJŌ Stack does not sit beside existing systems-it sits between them. Every system above this boundary receives data that has been explicitly acquired, structured, and prepared. Every system below it remains unchanged.
The architecture is the product.
One deployment boundary. One data plane. One place where industrial data is acquired, structured, and prepared before it enters the digital world.
