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Background: Single cell transcriptomics offers an avenue for predicting, with improved accuracy, the gene networks that are involved in the establishment of the first direct cell–cell interactions between the blastocyst and the maternal luminal epithelium. We hypothesised that in silico modelling of the maternal–embryonic interface may provide a causal model of these interactions, leading to the identification of genes associated with a successful initiation of implantation. Methods: Bulk and single cell RNA-sequencing of endometrial epithelium and scRNAseq of day 6 and 7 trophectoderm (TE) were used to model the initial encounter between the blastocyst and the maternal uterine lining epithelium in silico. In silico modelling of the maternal–embryonic interface was performed using hypernetwork (HN) analysis of genes mediating endometrial–TE interactions and the wider endometrial epithelial transcriptome. A hypernetwork analysis identifies genes that co-ordinate the expression of many other genes to derive a higher order interaction likely to be causally linked to the function. Potential interactions of TE with non-ciliated luminal cells, ciliated cells, and glandular cells were examined. Results: Prominent epithelial activities include secretion, endocytosis, ion transport, adhesion, and immune modulation. Three highly correlated clusters of 25, 22 and 26 TE-interacting epithelial surface genes were identified, each with distinct properties. Genes in both ciliated and non-ciliated luminal epithelial cells and glandular cells exhibit significant functional associations. Ciliated cells are predicted to bind to TE via galectin–glycan interaction. Day 6 and day 7 embryonic–epithelial interactomes are largely similar. The removal of aneuploid TE-derived mRNA invoked only subtle differences. No direct interaction with the maternal gland epithelial cell surface is predicted. These functional differences validate the in silico segregation of phenotypes. Single cell analysis of the epithelium revealed significant change with the cycle phase, but differences in the cell phenotype between individual donors were also present. Conclusions: A hypernetwork analysis can identify epithelial gene clusters that show correlated change during the menstrual cycle and can be interfaced with TE genes to predict pathways and processes occurring during the initiation of embryo–epithelial interaction in the mid-secretory phase. The data are on a scale that is realistic for functional dissection using current ex vivo human implantation models. A focus on luminal epithelial cells may allow a resolution to the current bottleneck of endometrial receptivity testing based on tissue lysates, which is confounded by noise from multiple diverse cell populations.