AI. Innovation. Everything in between.

Price optimization, also known as dynamic pricing or demand pricing, enables companies to optimize price reductions. Optimal discounts minimize cannibalization and maximize revenue at the same time. Dynamic pricing is one of the easiest transformations a company can achieve. They have a direct effect on the end result and can be introduced within a few days.

By creating connections between different goods, which are typically ordered together, commission times can be drastically reduced.

Recommendation systems use customer data to retain and improve customers with personalized recommendations via email, website search, or other channels.

To prevent malfunctions or poor maintenance from leading to expensive downtime, an algorithm predicts the risk of equipment failure and the need for maintenance.

AI can predict service cases and thus increase accuracy when planning service and maintenance.

Use machine learning to take your inventory and supply chain optimization to the next level

Predicting energy demand and managing energy production.

By using entity and sentiment analysis, emails can be automatically classified, which requires up to 40% less manual work.

With the help of chatbots, companies can create a kind of more dynamic FAQ page or use it internally as a tool to increase the productivity of their own employees.

Financial risk scoring

Automated monitoring of clusters of side effects in patient feedback, forums, or EHRs, e.g. after the launch of a new drug.

Extraction of relevant information from emails, forms or photos to pre-qualify liability and comprehensive claims.

NLP analyses doctors' letters and clinic reports for clarity, risks and medical advice for more structured communication with patients.

Machine learning classifies hospital cases faster, comprehensibly and auditable for billing and quality assurance.

Automated search and summarization of thousands of studies on protein behavior, receptor action, or metabolism.

Automatic reading, allocation and further processing of delivery notes, bills of lading and invoices.

According to Leufkens et al. (2012), typical colonoscopy screenings miss 22% - 28% of polyps and 20% - 24% of adenomas. Using machine learning, the live 2D image from colonoscopy can be reconstructed into a 3D model of the large intestine, which highlights segments that require additional coverage.

Predictive models for bed utilization, emergency room figures or staff-intensive services improve planning & shift management.

Early identification of delays, bottlenecks, or data inconsistencies using supervised and unsupervised learning processes.

Risk analysis and dynamic developments in the investment and capital market sector — with the help of predictive models based on daily updated parameters.

Computer vision to identify and classify packages, pallets, or damaged units.