Tilde AI Lab:
Research and development on multilingual AI

Tilde is a pivotal language technology research hub in the Baltic region with 30+ years of experience in European research projects and numerous local projects in Estonia, Latvia and Lithuania. Our team of in-house researchers cooperate with leading European research centres to advance the state-of-the-art in areas of language technologies such as machine translation, conversational AI and dialogue systems, speech recognition and synthesis, and foundational language models.

In-depth research expertise areas

Machine translation

We innovate in domain-adapted, adaptive, and term-aware neural machine translation, developing methods for robustness, mitigation of biases, and large language models.

Conversational AI

We focus on multilingual natural language understanding, semantic indexing, LLM-based retrieval-augmented generation, and personalisation.

Speech technologies

We research cascaded and end-to-end speech recognition and speech translation, multi-speaker and multilingual speech synthesis, real-time speech recognition, automatic subtitling and dubbing.

Text analysis

We develop multilingual named entity recognition, anonymisation and pseudonymisation, term recognition and extraction, morphological analysis, lemmatization, part-of-speech tagging and other tools.

Knowledge management

We maintain the largest termbank in Europe - EuroTermBank, and develop term management and electronic dictionary tools.

TildeOpen LLM now powers our machine translation

The model has been fine-tuned for machine translation and is fully integrated into Tilde MT, delivering higher-quality results across 34 European languages.

Current research projects

CoRDS: Confident Data-Driven Decision Support

CoRDS is a the Marie Skłodowska-Curie Actions (MSCA) Doctoral Networks programme project aiming to address gaps in AI -powered decision support tools by advancing the research on data-driven optimization methods and training a new generation of experts skilled in the combination of Operations Research (OR) and trustworthy Machine Learning (ML). CoRDS goes beyond the state-of-the-art data-driven optimization methods by proposing decision support frameworks that combine OR and ML methods and enable robust, transparent, and fair solutions that reflect user preferences and address complex, uncertain real-world scenarios.

Large Language Models for the European Unione

TheĀ LLMs4EU projectĀ aims to preserve European linguistic and cultural diversity in the digital age through cooperation between economic and academic actors. The project brings together Europe’s leading players in the field of generative AI to ensure that European companies and especially SMEs have access to the tools and resources to become competitive regarding language technologies and especially Large Language Models (LLMs). The project aims to make LLMs and all the tools necessary for their exploitation in all EU languages available in open data by capitalizing on existing European programs and competencies.

Language Innovations: Nimble, Guided, User-friendly, Agile and Robust Solutions Eliminating Defence Challenge

The LINGUARISE-DC project addresses the pressing need for advanced natural language processing in Europe’s defence sector. International missions face challenges such as linguistic diversity, limited training time, dependence on human interpreters, and security risks. LINGUARISE-DC seeks to transform language capabilities with an advanced human language technology (HLT) platform, designed to enhance European Union-led coalition missions and special operations.

Latest publications

269

Raivis SkadiņŔ, Daiga Deksne, Andris Hohbergs, RÅ«dolfs Jaunzars, Andrejs Petrovs, JustÄ«ne RÅ«dule and Mārcis Pinnis. 2026. Dataset and Baseline Implementation for Retrieval-Augmented Procurement Validation. Lecture Notes in Networks and Systems, vol. 1949. Springer.

268

Rinalds Vīksna and Mārcis Pinnis. 2026. Pseudonymisation for Morphologically Rich Languages. Proceedings of 29th International Conference on Text, Speech and Dialogue. Lecture Notes in Artificial Intelligence, Springer.

267

Daiga Deksne (Tilde), Raivis SkadiņŔ (Tilde), Mārcis Pinnis (Tilde), Andris Hohbergs, RÅ«dolfs Jaunzars, Andrejs Petrovs and JustÄ«ne RÅ«dule. 2026. Retrieval-Augmented Generation for Procurement Validation. Proceedings of the 18th International Conference on Agents and Artificial Intelligence, 4048-4055.

Our research team

marcis-pinnis

Mārcis Pinnis

Dr sc. comp., Chief AI Officer

Toms_Bergmanis

Toms Bergmanis

Dr M. Inf., Researcher

Martins_Kronis

Martins Kronis

M. sc. comp., Researcher/Developer

jurgita|_kapociute

Jurgita Kapočiūtė-Dzikienė

Dr sc. comp., Researcher

raivis-skadins

Raivis SkadiņŔ

Dr sc. comp., CTO

andrejs-vasiljevs

Andrejs Vasiļjevs

Dr sc. comp., Co-founder, Member of the Board
inguna-skadina

Inguna Skadiņa

Dr sc. comp., Chief Scientific Officer
matiss-rikters

Matīss Rikters

Dr sc. comp., Researcher
daiga-deksne

Daiga Deksne

Dr philol., Mg. sc. comp., Mg. psych., Software Architect

Rinalds-viksna

Rinalds Vīksna

Mg. sc. comp., Researcher

Davis_Nicmanis

Dāvis Nicmanis

Mg. sc. comp., Researcher/Developer

Roberts_Rozis

Roberts Rozis

BSc. comp., Data Group Manager