Areas of concentrated research activity
The disciplines in which our R&D activity is conducted.
The areas are not independent of one another. Monitoring a vision model on a production line relates to model operations; a language model's use of tools relates to agent architectures; the dynamic updating of protection strategies relates to reinforcement learning. A significant proportion of our projects draw on more than one area concurrently.
Problem definitions outside this list are also assessed. Regardless of discipline, the initial stage is a feasibility review.
Generative AI & LLMs
RAG architectures, domain adaptation and fine-tuning, distillation into smaller models, Turkish evaluation sets and on-premise deployment.
Explore
Computer Vision
Object detection and tracking, video understanding, vision-language models, document intelligence and OCR, real-time inference on edge devices.
Explore
AI Agents
Tool use, multi-step planning and multi-agent orchestration; MCP integration, human-in-the-loop workflows and safety guardrails.
Explore
MLOps & Model Operations
Evaluation infrastructure, observability, hallucination and regression tracking, continuous retraining.
Explore
Reinforcement Learning & Decision Systems
Decision policies that adapt to changing conditions, reward design, training in simulation and safe transfer to the field.
Explore
AI Security & Privacy
Robustness against adversarial attacks, prompt injection defence, learning without sharing data, and model confidentiality.
ExploreA technical assessment for your AI project
Your project's feasibility, risks and timeline are assessed in a technical consultation.