Our Climate Commitment

At Zartis, we believe responsible AI starts long before deployment — it begins with the choices we make at the design stage. We prioritise selecting the right model rather than defaulting to the largest one, and we favour intelligent model combinations over brute-force approaches. Our engineering decisions focus on building architectures that perform efficiently while reducing their energy demands. This means cutting unnecessary processing, avoiding duplication, preventing data bloat, and shaping data pipelines that are genuinely lean and conscious of their environmental impact.

Mastering AI isn’t just about achieving high accuracy; it’s about doing so efficiently. Our teams work with models that deliver meaningful value without imposing excessive computational or environmental cost. Instead of retraining systems from scratch, we fine-tune where possible and rely on smaller or domain-specific models when they meet the task. We combine symbolic AI, classical machine learning and LLMs in ways that avoid wasted compute. Retrieval, compression and caching techniques form part of our toolkit to further reduce resource use, and we always prioritise fast, effective inference over oversized models that add little practical benefit.

Sustainable AI isn’t a limitation — it’s a competitive advantage, and we treat it as such.