Data Science & AI

Unlock the full potential of your data and harness the power of AI

Enhance your decision-making with advanced analytics and predictive modeling by transforming data into valuable insights. Drive innovation with cutting-edge AI solutions, and improve efficiency through intelligent automation. Embark on your data science and AI transformation journey today!


Data Science and AI encompass a diverse range of technologies and methodologies aimed at extracting insights from data and enabling intelligent decision-making. These categories span across Probabilistic Technologies, Deterministic Technologies, and Neuro-Symbolic Systems, each offering unique capabilities to tackle various challenges in today’s digital landscape.


Probabilistic Technologies

Probabilistic technologies use probabilities to make decisions and predictions. These systems consider variable data patterns to provide informed and flexible results, incorporating different possible scenarios.

Neuro-Symbolic Systems

These combination of rule-based and data-driven AI systems use the advantages of both approaches to solve complex problems by combining human-like decision-making processes with powerful pattern recognition.

Deterministic Technologies

Deterministic technologies follow predefined rules to achieve accurate and predictable results. These systems work without random elements, providing consistent and reliable solutions for repeatable and structured tasks.

Business Use Cases

Explore how data science and AI can revolutionize your business operations and deliver unprecedented value.

Probabilistic Technologies | Neuro-Symbolic Systems

Automated contract review and compliance monitoring

Large Document Assistant, OCR Pipelines, Information and Knowledge Extraction

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Probabilistic Technologies | Deterministic Technologies

Cleansing and consolidation of customer data

Entity Resolution, Golden Record Building, Information and Knowledge Extraction

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Probabilistic Technologies | Deterministic Technologies | Neuro-Symbolic Systems

Fraud detection and prevention

Fraud and Malicious Event Detection, Novelty Detection, Similarity Based Matching, Network Analytics, Path Analytics, Node Analytics

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Probabilistic Technologies | Deterministic Technologies | Neuro-Symbolic Systems

Efficient document management and analysis

Database Assistant, Large Document Assistant, Information and Knowledge Extraction

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Probabilistic Technologies | Deterministic Technologies

Improved customer service interaction through knowledge graphs

Q&A over Document Stores, Knowledge Graph Building, Triple Extraction

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Neuro-Symbolic Systems

Personalized financial advice and product recommendations

Similarity Based Matching, Multi-Factorial Recommendation

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Deterministic Technologies

Supply Chain Management

Network Analytics, Path Analytics, Node Analytics

More details coming soon…

Technical Capabilities

Technical capabilities encompass the range of skills, tools, and methodologies to implement and manage advanced technological solutions.

Document Processing automates the digitization and processing of documents to minimize manual work. This technology extracts relevant data and content from texts, resulting in efficient information retrieval.

Data Disambiguation and Normalization focuses on improving data quality by identifying and removing duplicate entries and correcting inconsistencies. This leads to a uniform and consistent database.

Anomaly Intelligence detects abnormal patterns and anomalies in data. This pattern recognition technology plays an essential role in fraud prevention by identifying potentially fraudulent activities and events. Additionally, it is useful in novelty detection, recognizing new, previously unseen patterns in data that may not necessarily be harmful.

Multi-agent systems utilize distributed intelligence through the use of multiple autonomous agents that cooperate and communicate to solve complex tasks and achieve common goals. Integrated agents are based on multiple GenAI Models that can use extern tools.

Graph-RAG leverages graph structures to improve information retrieval, providing more precise and contextual responses by utilizing linked data. Integrating Retrieval Augmented Generation (RAG) principles, it combines retrieval-based and generative AI models. This approach supplements the knowledge of large language models (LLMs) with specific, up-to-date information from graph structures, resulting in more accurate, relevant, and human-like responses while minimizing inaccurate answers.

Recommender and Matching Systems analyze user preferences and behavior to make customized suggestions. They offer relevant products or services based on individual preferences.

Graph Data Science examines relationships and structures in data through network analysis. The technology identifies important nodes and connections in networks through path and node analysis to gain valuable insights and enable optimization.

Ontology Building and Semantic Data involve creating structured frameworks (ontologies) that define the relationships between concepts and data. This enables machines to understand and process the meaning of information, leading to more accurate data integration, retrieval, and analysis. It enhances data interoperability and supports advanced applications like knowledge management and semantic search.

Technical Use Cases

Explore the specific functionalities and applications of technology solutions.

Integrates GenAI technologies to act as an interface between the user and the database system in order to reduce manual tasks and improve the efficiency of databse usage.

Facilitates the processing and analysis of large documents to increase the efficiency and accuracy of information retrieval by pre-processing the documents with NLP-technologies and incorporating GenAI-models for information retrieval.

Enables precise answers to specific questions from large document collections to improve access to important information.

Identifies and extracts relevant data points from unstructured text to increase data availability and utilization.

Structures complex data relationships and concepts from data and unstructured sources.

Recognizes subject-predicate-object relationships in text to create structured knowledge representations for deeper analysis.

Organizes and classifies unstructured texts by topic to enable targeted analysis and better data organization.

Links and visualizes data with the help of ontologies into a single source of truth to improve data and knowledge integration and decision making

Automates text recognition and extraction, making physical documents digitally accessible and searchable.

Recognizes and consolidates different representations of the same entity to ensure data consistency.

Creates a reliable, consolidated data set from multiple sources to provide a single source of truth for data analytics.

Identifies unusual or new patterns in data to help detect new opportunities or threats.

Analyzes data for suspicious patterns and anomalies to enable proactive security and risk mitigation measures.

Finds relevant matches in large data sets to support accurate recommendations and better decision making.

Analyzes multiple data sources and considers multiple factors to generate customized suggestions.

Analyzes networks and their structures to provide valuable insights and optimization opportunities.

Tracks and analyzes transaction paths within networks to optimize processes and detect anomalies.

Evaluates the importance and connections of nodes in networks to identify critical actors and vulnerabilities.