Behind CODIAC stands a highly qualified team.

Sarah heads Data Model & Process Automation at CODIAC. At Google she was Language Lead (NLP) for the German-language voice products and substantially improved the precision of the Google Assistant. She combines machine learning with semantic models and turns excellent research into scalable, production-ready processes.

Matthias owns CODIAC's semantic core model, with a focus on learning ontologies and machine learning. At Google he was Domain Lead for Neural Semantic Parsing of the Google Assistant; he completed his M.A. in German linguistics with the top grade of 1.0. At CODIAC he makes the legal matters of tenancy law machine-readable.

Rebekka develops knowledge-based extraction methods and assessment algorithms for complex documents at CODIAC. For six years she worked at Google on the NLU component of the Google Assistant, then as a computational linguist on a spoken-dialogue system for Audi. She brings this strength to the modelling of tenancy law.

Carla brings five years of research at the University of Hamburg in corpus linguistics and semantics; in her doctorate she analysed semantic change in the German language using word embeddings and corpus analysis. At CODIAC she applies this expertise to tenancy and property law and makes legal meaning structures modellable.

With her M.A. in Digital Media Communication, Annika specialises in the social law domain at CODIAC, translating complex rule sets into clearly structured data models and comprehensible workflows. This makes the logic of social-law advice machine-processable and a key pillar in scaling our data models across different areas of law.

Beke combines analytical rigour with a strong talent for structure. A business-education graduate (M.A.) with research experience at the Fraunhofer Institute, she organises complex content into comprehensible systems for the recourse, traffic and insurance law domain at CODIAC.