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Interiors is the closest expression of identity we have. Your clothes change every day, but you live inside your home every day and absorb the sensory feedback from the objects you choose. People aren’t architecting entire spaces; they are trying to furnish their homes with pieces that feel like them. The difficulty is that products that look similar on paper can feel completely different in mood, intention, and personality.
We model the underlying taste identity behind interior choices so shoppers can find pieces that feel instinctively right. Instead of overwhelming catalogs and generic filters, discovery becomes a steady recognition of what resonates and a clearer sense of who they are through the things they live with.
Interior products carry a latent quality that is hard to express in words. Semantic style terms like “modern”, “classic”, “minimalist" or “mid-century” compress an enormous range of nuance into one piece of text. Two pieces described the same way can feel entirely different because the design’s personality comes from the amalgamation of its proportions, palette, materials, and the overall quality it exudes. Language cannot capture that spectrum. A shopper’s preference for one item over another is tied to their own specific composite personality, cultural exposure, and worldview, and most systems have no way of understanding or matching that. This leaves shoppers with overwhelming catalogs that all appear similar on paper but feel incompatible in practice.
Most tools assume taste is categorical rather than stable, yet emotional and nuanced.
Functional filters silo high-affinity products that customers never see as systems treat interiors like taxonomy rather than personal expression.
Style descriptors and semantics flatten differences in overall aesthetic.
These systems cannot recognize the composite nature of taste or why two “modern” objects resonate so differently with different people.
PSYKHE AI captures interiors at a granular, latent level, reading products through the full spectrum of design signals: proportion, palette, materiality, personality, and overall exuding quality. Our psychographic embeddings do the same for people, modeling not just whether someone leans classic or modern, but to what degree, in what tone, with what worldview, and with what emotional drivers. Taste is a specific composite unique to each person, and our system maps that composite to the precise blend of qualities in each product. This creates matches that feel instinctively right and impossible to recreate through tags or labels. (We know the reason why you like walnut wood.)
Shoppers discover pieces that reflect who they are rather than navigating rows of products that sound similar but feel disconnected. They experience less friction and greater confidence because the system understands their taste on a spectrum, not in binaries. Merchandising teams gain recommendations that maintain aesthetic coherence without manual styling, and businesses see stronger engagement and higher-value purchases because people choose items that resonate with their deeper identity.
