At OmegaLambdaTec, we see ourselves as Data Science and AI ecosystem partner dedicated to developing and implementing groundbreaking data-driven innovations. Realizing value from data requires the orchestrated collaboration of specialized competencies. Our role in the data economy focuses on providing leading expertise and tailor-made DeepTech components in the competence areas of data analytics, artificial intelligence, machine learning, simulations, digital twins, and synthetic training data.

The diverse benefits for our partners and clients through our forward-looking Algorithms-as-a-Service business model are evident:

  1. Faster – from idea to market readiness in under 6 months
  2. Better – leading algorithms for enhanced value
  3. More innovative – fresh ideas and Data Science solution approaches for your challenges
  4. Lower risk – combined experience from over 200 developed Smart Data solutions
  5. Cost-effective – use our Data Science team instead of building your own
  6. More focused – every partner concentrates on their core competencies

Phase Model

With our established phase model, we accompany your company from the initial steps and ideas for data-driven use cases to becoming a leading Data Science and AI champion. During our Data Science & AI Workshops (Phase 1), we collaboratively develop, assess, and prioritize relevant use cases for your company and create a tailored AI roadmap. Data exploration and data discovery studies (Phase 2) yield rapid results when investigating the feasibility of a solution concept and validating sufficient data quality and completeness. During the prototype and demonstrator development (Phase 3), the entire solution path is crafted, all necessary algorithms are developed or adapted, a functioning version is implemented and tested, and the business potential of the solution is evaluated. The full Smart Data solution development (Phase 4) transitions the new application into operational implementation, generating the anticipated value from then on. As the pinnacle for the data economy, we also collaborate with you to develop innovative, scalable data-driven products and new Smart Data services (Phase 5).

Abb. OmegaLambdaTec Phasenmodell für die Umsetzung von Smart Data Innovationen.


We follow a data-driven, Data Science-based approach using scientific methods and a deep understanding of the problem domain. The approach starts with model and theory-based solution concept (Physical Analytics) and supplements these with machine learning and other artificial intelligence (AI) techniques when they contribute to improving the solution. In general, we combine theoretical-physical solution concepts with rigorous data analyses approaches and advanced simulations. This approach ensures maximum transparency and interpretability of the results, which is often a necessary prerequisite for well-founded business decisions.

Our approach and project implementation is agile, experimental, and iterative, involving the rapid development of prototype algorithms and interim results that are continually validated, reviewed, and improved. All algorithm and software developments take place within the Python framework, making software code and resulting tools universally applicable and scalable. Implementation phases are divided into agile development cycles typically lasting two weeks. After each development cycle, regular video conference meetings are held to present current development results and align on the next steps with the project team. New development phases typically commence with a kickoff workshop to plan the implementation roadmap with the entire project team. Additional working meetings in smaller groups may be conducted as needed to discuss details such as data status, interim results, or user feedback.


The OmegaLambdaTec team combines unique Data Science expertise in data analysis, Physical Analytics, simulations, machine learning, and deep learning. We are thought leaders and have received various awards in advanced analytics areas such as (1) data-driven forecasting, (2) anomaly detection and classification, (3) simulation-based optimization, (4) automated image processing, and (5) digital twin and scenario simulations. Many innovative Smart Data solutions combine various methodological approaches and core Data Science topics to achieve an optimal result quality for specific tasks and objectives.

For many Smart Data solutions, the following interconnected development steps are necessary:

  1. Evaluation of data sources, data availability, data quality, and automated data correction for raw data.
  2. Comprehensive representation of all relationships, connections, and boundary conditions relevant to the target questions.
  3. Theoretical development of a consistent mathematical model and quantitative relationships for the use case.
  4. Implementation of a suitable simulation or optimization framework for the optimal solution of the use case, considering all relevant dependencies.
  5. Calibration and validation of the simulation or optimization model using existing historical data.
  6. Examination of all relevant solution scenarios, evaluation of the result quality, and quantification of the business case.

OmegaLambdaTec’s mission is the development and implementation of leading tailor-made Smart Data and AI solutions for the data-driven use cases of our corporate customers and partners. Our strengths and unique value propositions become particularly visible when innovative use cases demand the highest standards in Data Science methodology, require novel solution approaches, exhibit complex interdependencies, or involve challenging data limitations.

Abb. Data Science Methoden-Übersicht: (A) Physical Analytics Modell zur Untersuchung der zeitlichen Entwicklung der Trinkwasser-Temperatur im Netz. (B) Simulation synthetischer Füllstandsdaten von Altkleider-Containern zur Evaluierung des Business Cases und Lösung des Routen-Optimierungs-Problems. (C) Machine Learning Modell zur automatisierten Vorhersage (blaue Kurve) des Gaspreises (schwarz) für den nächsten Tag. (D) Deep Learning Verfahren zur Identifikation und Extraktion von Tunnelstrecken-Informationen aus handgezeichneten Karten.
Abb. Beispiele für Advanced Analytics Ansätze zur voll-automatisierten Datenverarbeitung in Echtzeit: (A) Korrektur-Algorithmus für Sensor-Daten mit teilweise falsch übermittelten Messwerten. (B) Anomalie-Identifikation im Daten-Stream. (C) Echtzeit-Optimierung dezentraler Energie-Systeme. (D) Echtzeit-Optimierung des vollen Produktionsprozesses für Grünen Wasserstoff.

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