The Kalman filter is a data assimilation algorithm that optimally estimates a system state given a model, past, and current observations. This is suitable to initialize numerical predictions. The smoothers also consider future observations in the assimilation process. These are more appropriate to build reanalyses. A smoother designed for oceanic reanalyses is introduced. It is derived from the Singular, Evolutive, Extended Kalman (SEEK) filter and the fixed-lag Kalman smoother. The major difficulty in the derivation lies in the management of model error. Then, extending the SEEK filter with the smoother function is straightforward. The additional computational cost is tiny, what makes the smoother suitable for applications with large systems. The SEEK smoother is tested in twin experiments. The model is an ocean general circulation model in a 1/4 degree double-gyre configuration and simulated satellite observations of Sea Surface Height are assimilated. The smoother slighlty but significantly improves the filter results. To conclude, an outlook to forthcoming real data assimilation experiments and reanalyses making will be presented.