Data Science

We assist you through all stages of a data science project resulting in reliable data-driven predictive and prescriptive analytics.

From data collection, exploration and cleaning through model development and testing to impact evaluation, explanation and visualization - our experts are there to ensure the highest quality of the process.

Make advantage of decision intelligence and digital automation to discover the true power of your data and advance the industry forward.

Classical Machine Learning and Deep Learning

Supervised, unsupervised and reinforcement machine learning. A broad spectrum of analysis techniques including clustering, regression, dimensionality reduction, pattern mining and classification with Tensorflow, PyTorch, Scikit-Learn and Pandas.

Natural Language Processing

Text similarity, classification, summarization, translation, sentiment analysis, entity extraction and topic modelling on massive text datasets using seq2seq, Attention, CNNs, RNNs and Memory Networks.

Computer Vision

Processing image and video data for image segmentation, object, edge or pattern detection, facial recognition, feature matching, automatic tagging and image classification.

Graph Algorithms and Embeddings

Graph-based machine learning with NetworkX, GraphSage, Neo4J or Tigergraph is well-suited for highly connected data such as social networks, web pages or GPS routes.

Graph algorithms for Centrality, Community Detection and Graph Similarity facilitate tasks such as scheduling, route optimization, link prediction and detecting social or interest clusters. Graph embeddings allow injecting network data into neural networks.

Explainable AI

Open the algorithmic blackbox by adding and explanation layer to your model. SHAP values and XAI techniques break down the decisions of Artificial Intelligence into human-readable factors and increase transparency and decision accessibility for all relevant stakeholders.