From prototype to production: why LLM projects stall on the way

That most AI projects never get past being an impressive demo is one of the most common problems in the field. The root of it is usually organisational. The research teams building the model and the software engineers keeping the live system standing tend to work to entirely different metrics and goals. Research focuses on the accuracy of the model; engineering cares about response time, cost and availability.

At werea we do not run these two disciplines as isolated teams — we put them at the same table from day one. Every model is designed against production-grade infrastructure while it is still an idea. For a model to count as successful it has to work not only in the lab but under real user load, within budget, and without interruption in live systems.