AZURE INTEGRATION · BUILT FOR EVERYTHING ELSE

Azure integration that reaches the systems other tools can't.

Q: How is Azure ML different from Azure AI Foundry?

Azure ML covers the classic ML lifecycle; [Azure AI Foundry](/solutions/data-integration-platform/ai-ml-platforms-integration/microsoft-azure-ai-foundry-integration) targets generative AI agents and models. Rayven feeds and operationalises both from the same real-time layer.

Connect Azure Machine Learning to every OT, IoT, app + operational system your models run on. Feed training and managed endpoints with live operational features, and push predictions into the systems that act.

Get my Azure integration plan → Talk to us

★★★★★5/5 · 140+ reviews Australian-built 99.9% platform uptime

Live demo · Claude querying Azure + ops via Rayven MCP

What's the status of the OT, IoT + operational systems → Azure ML feature store flow today?

All flowing. Live features, not batch is live. No blockers flagged.

OT, IoT + operational systemsAzure ML feature store

Trusted by 240+ teams across Australia + globally

THE REALITY

Azure is brilliant at what it does. Useless at everything else your data + AI needs to touch.

Datastores refresh on a schedule, managed endpoints score on inputs that trail operations, and predictions sit in a blob nobody wired back into the systems that act.

The integration tool you've already tried probably can't fix it.

What Zapier + iPaaS reach

App-to-app. Useful, until it isn't.

Connectors for Azure, Slack + a thousand other cloud apps. Cheap + fast for the easy half of integration. Where they hit a wall:

Real-time OT, IoT and SCADA data that should feed Azure ML features and endpoints
ERP, work-order and operational apps that Azure ML predictions should drive
Operational databases outside Azure holding the source data
Streaming event sources that should invoke Azure ML endpoints in real-time

What Rayven reaches

OT, IoT, ERP, apps + operations. One platform.

The integration platform built for what other tools can't (or won't) reach - plus an AI data fabric, MCP server + app builder layered on top.

Azure + every ERP, database and enterprise data system
OT/IoT, ERP, apps, databases, streaming + custom-built connectors.
Files, legacy SQL, FTP + custom-built connectors
Real-time bidirectional sync, tier-aware + rate-limit-safe
AI Data Fabric, MCP, automation + custom apps included

WHAT YOU CAN BUILD

Azure + Rayven: AI, automation + tools that reach every system you run.

5 examples of what Rayven customers run on Azure + integrated systems. Built in weeks, not months.

OT, IoT + operational systems → Azure ML feature store

Live features, not batch

Real-time OT, IoT, SCADA and app data streams into the Azure ML feature store and datastores, so training and inference use features that match current operations.

Top 5 accounts trending toward churn?

5 flagged. Usage drop + support backlog + late renewal. Owners notified.

AzureOTProduct DB

Azure ML endpoints → ERP + operational apps

Predictions become actions

Azure ML managed endpoint outputs post to ERP, work orders and operational apps in real-time, turning a prediction into a task.

Which won deals are stuck pre-invoice?

3 stuck. 2 drafts in accounting, 1 awaiting PO. Auto-finalise?

AzureERP + operational appsXero

Operational events → Azure ML endpoints

Event-triggered inference

State changes and threshold breaches invoke Azure ML managed endpoints in real-time with live features attached, so scores fire on real events.

Status check - is everything in sync?

All current. Last sync 38 seconds ago, no backlog.

AzureOperational events

Operational systems → Azure ML pipelines

Live training pipelines

Operational records and signals stream into Azure ML pipelines so models retrain against live operations without manual data prep.

Anything that needs my attention right now?

3 items. 1 deal stuck, 1 account flagged, 1 task overdue.

AzureOperational systems

Azure Machine Learning → Dashboards + portals

Surface predictions to teams

Predictions and monitoring outputs surface in operational dashboards, field apps and portals where operations and engineering teams act on them.

Which devices flagged risk in the last 24 hrs?

11 events. 3 critical - linked to 2 enterprise accounts.

AzureDashboards + portalsIoT

BEYOND INTEGRATION

Integration is the start with Rayven.
Here's what comes with it.

Rayven isn't just a Azure connector. It's the operational software platform underneath - so once Azure is connected, four more capabilities come online by default.

AI-ready data layer (AI Data Fabric)

Azure data joined with ERP, IoT + legacy - contextualised, unified and structured so AI models, agents and tools can use it without ETL prep work.
Learn more →

Live AI access (Rayven MCP)

Claude, ChatGPT + Gemini get live, governed access to Azure and every other connected system. Your AI stops guessing - cites real Azure records + numbers.
Learn more →

Automation + AI agents

Workflows + AI agents act on Azure data without human intervention - escalate at-risk accounts, trigger operational work, auto-update forecasts, draft customer comms.
Learn more →

Custom apps + portals

Unified customer portals, ops dashboards, mobile field apps, partner portals - built on top of integrated Azure data. No separate BI tool, no separate dev stack.
Learn more →

All on top of your existing stack. No rip + replace. Azure stays Azure. Rayven works on what you've already built. Specifically tuned for Azure ML feature-store freshness, managed-endpoint invocation and prediction writeback.

The Rayven

AI Data Fabric

MULTIMODAL DATA IN

THE RAYVEN PLATFORM

AI CAPABILITIES OUT

SCADA + OT

Databases

Streaming Feeds

IoT + Sensors

Documents + PDFs

Video + Images

Audio + Voice

Manual + Forms

APIs + Webhooks

AI Agents

Predictive AI + ML

Conversational AI

Real-time Training

AI-led Execution

Multimodal AI

Anomaly Detection

Forecasting

Vision + Edge

Gen AI Summaries

AZURE INTEGRATION · WHICH PATH

Pick the right tool for your stack.

Azure integration looks different at every scale. Honest comparison of the four common paths - Rayven only wins on some of them.

Capability

Rayven

Zapier · Workato

MuleSoft · Boomi

Build it yourself

Azure to other cloud apps

Yes

Best in class

Yes

DIY effort

Azure to legacy DB / files / FTP

Yes

Partial

Yes

DIY effort

Real-time, tier-aware + rate-limit-safe

Yes

Throttled / paid

Yes

Hard to get right

Azure ML pipeline feed + prediction writeback

Built in

No endpoint feed

Custom build

Hard to get right

AI-ready data layer + MCP for AI

Built in

Custom apps + portals on connected data

Built in

Not included

Separate product

Build it

Australian-built · sovereign deploy

Available

US-hosted

Configurable

You decide

Cost vs enterprise iPaaS

Significantly lower

Cheap at low volume

Premium pricing

Internal cost

Time to live

2-12 weeks

Days · per Zap

3-9 months

6-18 months

WHY RAYVEN · NOT JUST ANY CONNECTOR

The integration is the easy bit.
The platform + team behind it is the difference.

99.9% platform uptime

Platform + team. One commercial.

Not five vendors stitched together. Rayven is the platform AND the Australia-based expert team that scopes, builds + supports it. One commercial. No licence stacking, no finger-pointing.

MLOPS PIPELINE FEED

Built for Azure ML's endpoint and pipeline model.

Rayven authenticates with Microsoft Entra ID and streams live operational data into Azure ML datastores and the feature store through its REST APIs, keeping features fresh. Managed endpoint outputs write back to ERP and apps in real-time, so an Azure ML prediction becomes an operational action, not a blob.

5/5 · 140+ REVIEWS

We stay after go-live.

No handoff. No disappearing. Rayven is a long-term partner when your Azure evolves, your stack changes, or you push further. 24/7 support. Same team, same platform, same commercial.

DEPLOY HOW YOU WANT

Hosted your way, where you need.

Deploy as cloud, private cloud + on-premise - in Australia, the US, the UK, or anywhere else your Azure data needs to live. Your residency, your rules, your local compliance environment.

FAQS

The questions Azure admins, RevOps + IT ask first.

If you're evaluating Rayven for Azure integration, these are the things worth knowing before booking a call.

The 30-minute call covers

Your Azure + connected stack today
What's reachable + which systems to bring in first
Live walkthrough of Rayven against your scenario
Time, scope + delivery estimate
Honest read on whether Rayven's the right fit

What is Azure Machine Learning integration?

Azure Machine Learning integration connects Azure ML to your operational stack - OT, IoT, ERP, apps and databases - so models train on live features and predictions reach the systems that act. Rayven delivers it as part of an AI data fabric, not a single-purpose connector.

How does Rayven connect Azure Machine Learning to other systems?

Rayven uses Azure ML REST APIs and the SDK with Microsoft Entra ID auth to stream operational data into datastores and invoke managed endpoints, then writes predictions back to targets. See the Rayven Platform for how the layers fit.

How is Azure ML different from Azure AI Foundry?

Azure ML covers the classic ML lifecycle; Azure AI Foundry targets generative AI agents and models. Rayven feeds and operationalises both from the same real-time layer.

Does Rayven replace Azure Machine Learning?

No. Rayven is not an ML platform and does not replace Azure ML - it is the real-time AI data fabric that feeds your endpoints live features and operationalises their predictions. Your training and serving stay on Azure ML.

What are common Azure Machine Learning integration use cases?

Live feature data, event-triggered inference, live training pipelines and pushing predictions into ERP and operational apps. See the automations above for specifics.

What are the technical specifics of Azure Machine Learning integration with Rayven?

Rayven integrates Azure Machine Learning through its REST APIs and Python SDK v2 with Microsoft Entra ID authentication, respecting workspace and datastore scoping and managed-endpoint quotas. Training jobs, pipelines, the feature store, the model registry and managed online and batch endpoints are handled as first-class flows, with MLflow support. Live operational data - OT, IoT, SCADA, ERP and app events - streams into datastores and the feature store via 1,228+ connectors, OPC-UA, MQTT, CDC and API, so models never train or score on stale schedules. Prediction outputs write back to operational targets like ERP, work orders and dashboards with field-level mapping. Azure ML is a distinct service from Azure AI Foundry; Rayven feeds both. Validated against Azure ML managed online and batch endpoints, Rayven stays the AI data fabric around Azure ML - it never becomes or replaces your model platform.

Sectors

Job Roles

Overview

Set Engagements

Overview

We Partner With

Library

Our Company

Azure integration that reaches the systems other tools can't.

Azure is brilliant at what it does. Useless at everything else your data + AI needs to touch.

App-to-app. Useful, until it isn't.

OT, IoT, ERP, apps + operations. One platform.

Azure + Rayven: AI, automation + tools that reach every system you run.

Live features, not batch

Predictions become actions

Event-triggered inference

Live training pipelines

Surface predictions to teams

Integration is the start with Rayven.
Here's what comes with it.

AI-ready data layer (AI Data Fabric)

Live AI access (Rayven MCP)

Automation + AI agents

Custom apps + portals

Rayven is a five-layer platform ALL your integrations + much more
can run on.

Pick the right tool for your stack.

The integration is the easy bit.
The platform + team behind it is the difference.

Platform + team. One commercial.

Built for Azure ML's endpoint and pipeline model.

We stay after go-live.

Hosted your way, where you need.

The questions Azure admins, RevOps + IT ask first.

The 30-minute call covers

What is Azure Machine Learning integration?

How does Rayven connect Azure Machine Learning to other systems?

How is Azure ML different from Azure AI Foundry?

Does Rayven replace Azure Machine Learning?

What are common Azure Machine Learning integration use cases?

What are the technical specifics of Azure Machine Learning integration with Rayven?

Azure + AI integration.
In weeks, not months.

Sectors

Job Roles

Overview

Set Engagements

Overview

We Partner With

Library

Our Company

Azure integration that reaches the systems other tools can't.

Azure is brilliant at what it does. Useless at everything else your data + AI needs to touch.

App-to-app. Useful, until it isn't.

OT, IoT, ERP, apps + operations. One platform.

Azure + Rayven: AI, automation + tools that reach every system you run.

Live features, not batch

Predictions become actions

Event-triggered inference

Live training pipelines

Surface predictions to teams

Integration is the start with Rayven.Here's what comes with it.

AI-ready data layer (AI Data Fabric)

Live AI access (Rayven MCP)

Automation + AI agents

Custom apps + portals

Rayven is a five-layer platform ALL your integrations + much morecan run on.

Pick the right tool for your stack.

The integration is the easy bit.The platform + team behind it is the difference.

Platform + team. One commercial.

Built for Azure ML's endpoint and pipeline model.

We stay after go-live.

Hosted your way, where you need.

The questions Azure admins, RevOps + IT ask first.

The 30-minute call covers

What is Azure Machine Learning integration?

How does Rayven connect Azure Machine Learning to other systems?

How is Azure ML different from Azure AI Foundry?

Does Rayven replace Azure Machine Learning?

What are common Azure Machine Learning integration use cases?

What are the technical specifics of Azure Machine Learning integration with Rayven?

Azure + AI integration.In weeks, not months.

Integration is the start with Rayven.
Here's what comes with it.

Rayven is a five-layer platform ALL your integrations + much more
can run on.

The integration is the easy bit.
The platform + team behind it is the difference.

Azure + AI integration.
In weeks, not months.