AvignaCube™: The AI-Native IoT Platform Built for OEMs

The installed base is becoming an increasingly consequential source of intelligence for equipment manufacturers. Yet the value of connected equipment remains constrained when telemetry, service records, technical documentation and operational decisions reside in disconnected systems. An OEM may have visibility into individual assets without possessing the contextual understanding required to improve service, develop digital offerings or act on emerging problems.

AvignaCube™ addresses this gap through an AI-native AIoT data platform built on Microsoft Azure. It brings device connectivity, unified data, knowledge graphs and agentic AI into one product, designed for equipment OEMs and enterprises that need to operationalize connected solutions without owning the entire complexity of platform development.

“The next generation of industrial intelligence will not be defined by how much equipment an OEM connects, but by how effectively it turns connected equipment into decisions, action and digital value.”

Why Traditional IoT Platforms Reach a Limit

The first generation of IoT platforms established the foundations of connected operations. Today, an equipment manufacturer may operate multiple product lines across regions, support warranty and AMC obligations, maintain field service operations and manage an installed base that generates data in several formats. Devices, portals and service tools can accumulate independently, leaving information available but difficult to interpret as a connected whole.

Traditional IoT platforms commonly treat AI as an additional analytical capability. AvignaCube places AI at the centre of the platform, with the knowledge graph, retrieval-augmented generation and multi-agent orchestration forming part of its core.

What Makes AvignaCube an AI-Native IoT Platform?

AI-native is not simply a reference to adding a chatbot to an existing IoT application. In AvignaCube, the claim refers to how the platform handles data, context and intelligence from the beginning.

Incoming telemetry, images, audio and documents can be enriched, tagged and embedded as they enter the platform. This gives downstream applications access to AI-generated context rather than requiring every application to construct its own enrichment process.

The platform also uses a knowledge graph as its default data model. Assets, components, sites, faults and procedures are represented through relationships, allowing the system to reason over operational context.

Consider a question about a pump connected to a production line. The relevant information may include the pump’s telemetry, the line it feeds, its fault history and the procedures associated with its maintenance. A knowledge graph provides the structure for these relationships to be queried together.

This is particularly important when the objective is not simply to retrieve a value, but to understand what that value means.

From Data Collection to Decisioning

AvignaCube combines several capabilities within its AI-native architecture:

• Unified data ingestion: Structured, semi-structured, unstructured and multimodal data can be handled through one platform.
• Knowledge graph and semantic context: Assets, components, sites, customers, faults and procedures are connected within a shared model.
• Retrieval-augmented generation: A managed RAG pipeline and vector store support grounded answers across applications and copilots.
• Agentic orchestration: Multi-agent workflows can plan, retrieve information, call tools and recommend or take action within approved guardrails.
• Natural-language access: Engineers, service managers, executives and technicians can ask questions in plain language and receive grounded, cited answers.

The result is a platform designed to move from “what happened?” towards “why did it happen, what should happen next, and who needs to act?”

Built for Equipment OEMs, Not Generic IoT

The requirements of an equipment OEM extend beyond device connectivity.

An OEM may need to launch connected offerings across several product lines, maintain brand ownership of customer-facing applications, support multiple tenants and operate across geographies with different data residency requirements.

AvignaCube is built for this operating model. Its OEM-first architecture includes white-label portals and applications, multi-tenant capabilities, tenant isolation and configurable pricing and packaging constructs for digital services. The platform is intended to help manufacturers retain ownership of their customers and data while creating differentiated connected experiences.

This matters because digital transformation is not complete when equipment becomes connected. The larger commercial question is what the OEM can build on that connectivity.

Digital services, outcome-based SLAs and analytics-as-a-service are among the revenue opportunities identified for AvignaCube. The platform provides the data and intelligence foundation for OEMs seeking to develop these offerings.

Microsoft Azure as the Platform Foundation

AvignaCube is built end-to-end on Microsoft Azure.

Its architecture incorporates Azure IoT services for connectivity, Azure Data Explorer for time-series analytics, Cosmos DB for operational and graph data, Microsoft Fabric and OneLake for unified analytics, and Azure AI Foundry and Azure OpenAI for AI capabilities.

The architecture also includes Azure Machine Learning, Azure AI Vision, Azure AI Search and Azure Digital Twins, alongside security, governance and observability services.

This Azure-native foundation supports the platform’s intended enterprise requirements: cloud-native scale, security, regional data residency and extensibility through APIs and SDKs.

The platform is also designed to support model, prompt and agent lifecycle management. These are treated as production assets that require versioning, evaluation and governance.

For organizations building AI-driven industrial applications, this is a material consideration. A production AI system needs more than an inference endpoint. It needs a governed architecture through which intelligence can be developed, evaluated and operationalized.

What Can OEMs Build with AvignaCube?

The platform’s capabilities extend across several industrial use cases.

In manufacturing, it supports OEE monitoring, predictive maintenance and AI-guided quality inspection. In energy and utilities, it supports grid and asset monitoring, demand forecasting and AI-assisted outage diagnosis. For OEM remote monitoring and service, it supports equipment health, warranty analytics and white-labelled customer portals.

Its AI capabilities include anomaly explanations, predictive maintenance insights, executive narratives and natural-language queries over telemetry.

A service manager could ask which assets in a fleet have experienced significant changes in performance and what actions were taken. A technician could receive a recommendation grounded in equipment history, manuals and service procedures. An executive could receive a summary of what changed and why it matters.

These are not separate data silos. They are experiences built over the same intelligence foundation.

A Platform for the Next Stage of Industrial Digital Transformation

The central proposition of AvignaCube is straightforward: connected equipment should become a source of operational intelligence, not merely a source of telemetry.

For CIOs and CTOs evaluating an IoT platform, the relevant questions therefore extend beyond device protocols and dashboard capabilities.

Can the platform unify operational data and technical knowledge? Can it support grounded AI experiences? Can OEMs build differentiated applications under their own brand? Can it scale across product lines and geographies without requiring a separate platform for every use case?

AvignaCube is designed around these requirements.

Looking for an AI-native IoT platform for your equipment business?

Explore how AvignaCube can support connected solutions, predictive maintenance, AI-driven service intelligence and digital service development.

Talk to Avigna.AI about building your connected equipment strategy with AvignaCube™.