Rayven Insights
An IoT + AI stack is the layered set of technologies that collects data from physical assets, processes it in real-time, and uses artificial intelligence to turn that data into decisions or automated actions. Without a coherent stack, sensor data stays trapped in silos and AI models never reach production. This post breaks down each layer, explains where stacks typically break, and shows what a working industrial deployment looks like.
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Book a free call →An IoT + AI stack is not a single product. It is a sequence of connected capabilities: devices and sensors generate data, connectivity infrastructure moves that data, a data layer stores and structures it, AI and analytics models interrogate it, and applications present the output to operators or trigger automated responses.
The layering matters because each layer has a different job. A sensor cannot reason about what it measures. A machine learning model cannot collect data it never receives. An application cannot act on a prediction that was never generated. When one layer is absent or disconnected, the whole chain breaks - and that is the most common reason industrial AI projects stall before delivering value.
95% of AI projects never ship - most because the data infrastructure underneath them was never fit for purpose. Getting the layers right from the start is the difference between a proof of concept that sits on a shelf and an operational system that changes how a facility runs.
A well-structured industrial stack has five identifiable layers. Each maps to a distinct operational function:
| Layer | What it does | Industrial examples |
|---|---|---|
| Integration | Connects OT, IT, IoT devices, APIs, and data streams in real-time | PLCs, SCADA, historian systems, fleet telematics |
| Data | Processes, stores, cleanses, and structures data for AI use | Time-series normalisation, ETL pipelines, model training datasets |
| Execution | Runs AI models, workflows, predictive analytics, and automation logic | Predictive maintenance triggers, anomaly alerts, agentic AI agents |
| Presentation | Delivers outputs to operators via apps, dashboards, or conversational AI | Field apps, control room dashboards, mobile alerts |
| Security and Governance | Controls access, enforces compliance, and manages data residency | Role-based access, audit logging, on-premise hosting options |
Each layer must exchange data with the one above and below it. A gap anywhere in the sequence prevents the stack from functioning as a whole.
An IoT stack (see pure IoT Platform)- the infrastructure of devices, connectivity protocols (MQTT, OPC-UA), edge gateways, and data ingestion pipelines - solves the problem of getting operational data into a usable form. An AI stack - the infrastructure of data pipelines, model training environments, inference engines, and serving layers - solves the problem of generating intelligence from that data.
Neither is sufficient on its own. An IoT stack without AI produces enormous volumes of largely uninterpreted data. An AI stack without IoT has no real-time operational signal to work with; it is reasoning against stale exports or manual inputs.
The operational case for combining them is straightforward: industrial assets are continuously generating signal - vibration, temperature, pressure, throughput, location - and the only scalable way to act on that signal is automated intelligence. A unified stack where real-time integration feeds directly into AI execution is what separates reactive operations from predictive ones.
Most stacks do not fail at the sensor. They fail at the seams between layers - particularly at data ingestion, data quality, and model deployment.
Common failure points:
66% faster than traditional development is achievable when organisations use a unified stack rather than assembling these layers from separate vendor products.
Consider a large-scale mining or resources operation. Assets are distributed, connectivity is inconsistent, and operational decisions - shift scheduling, maintenance dispatch, dust suppression - happen under time pressure.
A working stack in this context:
Operations like those at Anglo American and Glencore operate across exactly this kind of complexity. The Rayven Platform delivers an Industrial AI Data Fabric - a unified environment where all five layers operate as a single system rather than a collection of integrated point solutions. With 240+ deployments live across 24+ industries, the architecture is proven at industrial scale.
Timelines vary by approach. Building a custom stack from open-source components and point-solution vendors typically takes 12 to 24 months before anything reaches production - and that assumes the integration, data engineering, and DevOps capability exists in-house.
A unified platform approach compresses this significantly. The average deployment time on the Rayven Platform is three weeks, with full solutions delivered in two to 12 weeks depending on scope.
The difference is not corners being cut. It is the elimination of integration work between layers. When the integration, data, execution, presentation, and governance layers are pre-built and pre-connected, the configuration effort replaces the engineering effort.
Done-for-you delivery is available for organisations that want a fixed scope and fixed price, with Rayven's team building the solution end-to-end. Hybrid delivery allows organisations to take ownership progressively once the foundation is in place.
Vendor selection for an IoT + AI stack should be evaluated against five operational criteria:
The Rayven Platform overview covers how each of these is addressed in a single, unified environment. For industrial organisations evaluating options, the practical question is whether the vendor has deployed at the scale and complexity of your operation. A 5/5 rating across 140+ reviews reflects consistent delivery - not just platform capability on paper.
For organisations weighing custom integration approaches against a unified platform, the total cost of maintaining point-solution stacks over three to five years is almost always higher than it appears at procurement.
Not if the platform is built correctly. A well-structured stack embeds AI capabilities - predictive analytics, anomaly detection, automated workflows - into the execution layer in a way that operational teams can configure and monitor without writing code. The Rayven Platform includes 11 native AI capabilities designed for operational users. Data science expertise helps when building custom models, but day-to-day operation does not require it.
Edge AI - inference that runs on a local device or gateway before data reaches the cloud - reduces latency and maintains functionality when connectivity is unreliable. Cloud AI provides greater compute for complex models and centralised management. Most industrial stacks use both: edge for time-critical decisions (shutting down equipment on a fault signal) and cloud for pattern recognition across longer time horizons (predictive maintenance scheduling). The integration layer must support both.
Yes, with the right integration layer. Legacy SCADA systems, historians, and PLCs often use proprietary protocols (Modbus, DNP3, OPC-DA). A platform with broad data integration support - including protocol translation at the edge - can connect these systems without replacing them. This is one of the most common integration challenges in heavy industry, and it is solvable without ripping out existing infrastructure.
Security must be addressed at every layer: encrypted data transmission from devices, role-based access control at the data and presentation layers, and full audit logging of automated decisions. For Australian industrial operators, data residency - keeping operational data onshore - is often a compliance requirement. A platform that embeds security and governance as a native layer, rather than a bolt-on, is far easier to audit and maintain than a point-solution stack.
Any industry where physical assets generate continuous operational data and where operational decisions happen under time pressure. Mining, energy, utilities, infrastructure, logistics, and food production are consistently high-value applications. The Rayven Platform operates across 24+ industries, with deployments for organisations including Viva Energy, NSW Ports, Fulton Hogan, and Wattwatchers. The commonality across all of them is the same: real-time data from physical assets, connected to AI-driven decisions.
For most industrial operators, yes. A unified platform eliminates integration maintenance between layers, reduces vendor management overhead, and provides a single audit surface for governance. Best-of-breed stacks can outperform on individual layers - a specialist time-series database may outperform a general-purpose data layer on specific workloads - but the integration tax compounds over time. 66% faster than traditional development is the consistent outcome when layers are pre-integrated rather than assembled from separate products.