Selected projects demonstrating precision, consistency, and applied linguistic expertise across legal and AI-driven environments.
Resolving conflicting terminology across national and European legal systems to ensure consistency and legal precision.
The project involved translating a set of legal documents containing decisions issued at different levels of judicial proceedings.
The terminology-heavy content referenced both national legislation and European legal instruments. Available reference materials included official translations of laws, EU regulations, and existing institutional translations, often using inconsistent terminology.
As a result, key legal terms lacked consistent equivalents, certain concepts had no direct one-to-one correspondence in English, and frequent repetition of critical terms required strict internal consistency.
A targeted terminology research strategy was developed, drawing on authoritative legal textbooks, official legislative translations, and EU regulatory terminology.
The hierarchy and applicability of sources were carefully evaluated, and the findings were consolidated into a structured working glossary to ensure consistency across all documents.
The client required the use of a single equivalent for certain terms across all contexts. This created a need to balance legal precision, consistency, readability, and client expectations.
Terminology was systematically refined by analysing recurring terms and aligning their usage across documents. Where necessary, adjustments were made to meet client requirements while preserving clarity and legal coherence.
Preparing a clean and consistent dataset through localisation and large-scale terminology alignment for AI training.
The project involved translating and annotating a large dataset of English-language user utterances intended for use in an AI-driven system.
The dataset contained a high volume of short, spoken-style phrases, many of which included:
As a result, direct translation into Russian did not always retain the intended functionality and naturalness of the utterances.
In addition, consistency across repeated or similar source segments was critical for training purposes.
The issue of non-transferable local features was identified during the translation process and escalated.
In coordination with the project team, the strategy was adjusted from direct translation to targeted localisation.
The workflow included:
The dataset required systematic verification to ensure that identical or similar source phrases were rendered consistently across the entire corpus.
This introduced a layer of large-scale consistency control and quality assurance.
A structured review process was implemented to: