AI. Innovation. Everything in between.

Individuals or companies can be categorized based on descriptive variables. For example, new customer groups can be identified.

With a model, past customer life cycles and the likelihood of migration can be analyzed. By using an acute value analysis (state of the art shapely additive explanation value analysis), it is shown which measures need to be taken to reduce the probability of migration.

Imitation style makes it possible to transfer creative products (e.g. pieces of music or paintings) to other works.

Annual reports can be formulated by generating texts. The AI enables stylistic nuances and aggregation of data that would not be possible with classic text templates.

Analysis of market data to forecast further developments

Find micro-influencers who drive the highest level of engagement with your brand. Create lists of potential micro-influencers based on personality profiles, topics, locations, and more.

Avoid “garbage in, garbage out” by ensuring the quality of your data with appropriate intelligent data cleansing processes and tools

Combine your data from various sources into meaningful and valuable information

Authenticate customers without passwords using biometrics to improve customer satisfaction and reduce problems associated with forgotten passwords

Analysis of patient data enables accurate case prioritization and triage in real time

Use machine vision to automatically tag your images to help users find relevant products.

With AI, the effects of gene edits can be predicted that classical computer programs cannot calculate.

Use AI to attract, filter, contact, and hire the best people

Manage employee performance effectively and fairly without damaging their motivation

Personalize your sales content and analyze its effectiveness to enable continuous improvement

By assigning individual sales to marketing and sales campaigns, end-to-end customer contact can be optimized.

AI can be used to minimize the waste of defect-free products and to ensure 100% waste of defective products. It prevents over-disposal of products.

Based on actual and previous statements, it is possible to predict whether or not a settlement will take effect within a specific period of time.

To find similarities between payment data and information from invoices, we train a neural network.

AI can predict whether an application will be accepted or rejected in court.

AI can label houses according to their energy efficiency class. This reduces the manual work involved in assigning energy efficiency classes to households. This can also be used as a pre-scan option.