The present paper is a technical report of KonKretiKa, a system for computation of concreteness indexes of words in context, submitted to the English track of the CONcreTEXT shared task. We treat concreteness as a bimodal problem and compute the concreteness indexes using paradigms of concrete and abstract seed words and distributional semantic similarity. We also conduct sigmoid transformation to achieve greater similarity to the psycholinguistically attested data, and apply dynamic adjustment of static indexes for sentential context. One of the modifications of the presented system ranked third in the task, with rs = .6634 and r = .6685 against the gold standard.