Generative AI & LLMs
RAG architectures, domain adaptation and fine-tuning, distillation into smaller models, Turkish evaluation sets and on-premise deployment.
Why we work in this area
Language models are now easy to reach; the hard part is making them work with a particular organisation's data, terminology and risk tolerance. In most organisations the problem is not the model's capability but the disorder of the data and the fact that its output cannot be verified.
There is a separate gap on the Turkish side. Machine-translated international evaluation sets give misleading results because of Turkish morphology and domain terminology. That is why we build our own evaluation sets for every field we work in.
What we work on
- Retrieval-augmented (RAG) architectures over enterprise data: chunking strategies, hybrid search, re-ranking and source attribution
- Domain adaptation and fine-tuning; distilling large models into smaller, cheaper ones
- Domain-specific evaluation sets for Turkish and automated evaluation pipelines
- On-premise deployment for organisations with data security requirements, plus cost–latency optimisation
From our research projects
One of our funded projects focuses on building a software protection system that adapts itself to changing conditions by combining language models with reinforcement learning. Unlike classical static approaches, the system updates its strategy according to the new situations it encounters.
A technical assessment for your AI project
Your project's feasibility, risks and timeline are assessed in a technical consultation.