Artificial Intelligence & Machine Learning
Use the data in SAP and your other systems to forecast, detect problems early and automate decisions. We build machine learning that runs in production, on Databricks and Microsoft Fabric, connected to SAP.
AI and ML that make it past the pilot
Most companies already have the data for useful machine learning. It sits in SAP, CRM, IoT devices and customer channels, just not in one place.
We bring that data together and build models that forecast, flag anomalies and support decisions, then put them into the systems people use every day. Our work is built on SAP, the Microsoft Fabric analytics platform and the Databricks Lakehouse Platform.
Why AI projects stall
- SAP and non-SAP data kept in separate silos
- Data pipelines built differently each time
- No clear ownership or version control for models
- Models that never get connected to business processes
- No way to score data in real time
Because of these gaps, many AI projects stop at the pilot stage.
The data foundation: Databricks and Microsoft Fabric
Databricks combines data engineering, machine learning and analytics on Delta Lake and Apache Spark, so data preparation, feature engineering and model training happen in one place.
Microsoft Fabric is a SaaS analytics platform that brings together OneLake, Power BI, Data Factory and real-time analytics, with governance across all of it.
We connect both with SAP S/4HANA and other systems to build shared data pipelines that feed reporting and AI models.
What machine learning is used for
- Forecasting: demand, revenue and operational risk
- Automation: less manual work in repetitive steps
- Customer insight: segments and personalization from behavior data
- Anomaly detection: fraud, equipment failures and supply disruptions
- Real-time decisions: acting on streaming data as it arrives
How we deliver
We take a model from data to daily use, not just to a notebook:
- End-to-end pipelines from data to model to deployment
- Supervised, unsupervised or reinforcement learning, depending on the problem
- Models connected to SAP and other business systems
- MLOps for retraining, validation and monitoring
- Architecture for real-time scoring in the cloud and in SAP
Examples by function
- Finance: credit risk scoring and fraud detection
- Supply chain: demand forecasting and inventory planning
- Sales and marketing: customer segmentation and churn prediction
- Operations: predictive maintenance from sensor and event data
- HR: attrition prediction and workforce planning
Keeping models useful over time
A model that is accurate on launch day can drift as the business changes. We plan for that from the start:
- Scheduled retraining
- Monitoring for data drift and falling accuracy
- Connection to the processes that use the predictions
- Feedback from users and outcomes back into the model
Combining MLOps and DataOps this way means faster releases, steadier predictions and less risk from models that quietly get worse.
From data to a model in production
- 1DataBring SAP and other data into one platform.
- 2FeaturesClean and prepare the signals the model learns from.
- 3ModelTrain and test, then choose the simplest model that works.
- 4DeploySend predictions back into SAP, apps or reports.
- 5MonitorWatch for drift and retrain on a schedule.
Why ITRadiant for AI and ML
Our advantage is the connection to SAP: models are only useful when their results reach the people and processes that act on them.
- SAP data understood: we know where the useful data sits in SAP and how to extract it properly
- Right method for the problem: we pick supervised, unsupervised or reinforcement learning based on the use case, not fashion
- Production from day one: we plan deployment, monitoring and retraining before we train the first model
- Modern data platforms: hands-on work with Databricks and Microsoft Fabric
Frequently asked questions
What are AI and machine learning?
Technologies that let systems learn patterns from data and use them to make predictions or automate decisions.
How do AI and ML improve business performance?
They improve forecasts, automate routine decisions and spot risks or anomalies earlier, so people can act faster.
What is predictive analytics?
Using machine learning models trained on historical and current data to predict what is likely to happen next, such as demand or equipment failures.
How do AI systems connect to SAP and other platforms?
Data flows from SAP and other systems into platforms such as Databricks or Microsoft Fabric, where models are trained. Predictions are then sent back into SAP or shown in reports and apps.
What is MLOps?
The practice of training, deploying and monitoring machine learning models in a repeatable way, so they keep working reliably in production.
Have a forecasting or detection problem in mind?
Describe the decision you want to improve and the data you have. We will tell you honestly whether machine learning is the right tool.
