In collaboration with Aplus Technology
Complementary offering with a technology partner
Data science and AI: put your data to work, improve processes deliberately
Together with our technology partner Aplus Technology, we help companies analyse existing data, apply machine learning where it makes sense and automate recurring processes – with a point of contact in Vienna.
What data science and AI are suited for
Many companies hold data from business applications, ERP systems, sensors or documents – yet often only a small part of it is used. Data science and artificial intelligence can help reveal relationships, support decisions and reduce routine work.
Whether an AI approach makes sense depends on the data available, the objective and the constraints. That is why we start with a sober assessment – not with a ready-made tool.
Data analytics and business intelligence
Derive KPIs and relationships from existing data and present them clearly:
- Exploratory data analysis
- KPI and reporting concepts
- BI dashboards
- Analysis of operating and sensor data
- Data quality checks and improvement
Machine learning
Models that learn from data – for example for forecasting, classification or detecting anomalies:
- Forecasting models (predictive analytics)
- Classification and pattern recognition
- Anomaly detection
- Model training, testing and evaluation
- Machine learning pipelines
- Model operation and monitoring By arrangement
AI strategy and implementation
From first idea to a viable solution – with a clear weighing of benefits, effort and risks:
- Identifying suitable use cases
- Assessing data and feasibility
- Proof of concept
- Chatbots and digital assistants
- Generative AI and large language models (LLMs)
- Integration into existing applications
Automation
Model recurring, rule-based processes digitally and reduce media breaks:
- Process automation
- Automated data processing
- Notifications and workflows
- Connection to existing systems, e.g. HCL Domino
Data engineering
The foundation of every analysis: capture, combine and provide data reliably:
- Data pipelines and data preparation
- Combining data sources
- Data modelling
- Interfaces and APIs
- Data storage in the cloud or on-premises By arrangement
How we work
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Clarify goal and data
Which question should be answered, which process improved – and what data is available for it?
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Check feasibility
We assess data quality, effort and benefit and recommend whether and how an approach is worthwhile.
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Run a pilot
A manageable proof of concept shows what works in the real environment.
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Integrate and operate
We integrate proven solutions into your systems and accompany day-to-day operation.
Realistic expectations
AI is not a sure-fire success. We do not promise specific savings, accuracy levels or results. What can be achieved depends on factors such as:
- Amount and quality of data
- Clarity of the question
- Integration into existing processes
- Data protection and compliance requirements
- Acceptance by users