For decades, the digital transformation of real estate has focused on asset management, marketing or 3D visualization, apart from the most economically critical phase, the one which even precedes the first shovel, has remained largely artisanal. Feasibility studies, regulatory constraints, volumetry, financial arbitrations, architectural design: the upstream side of real estate development remains a fragmented chain and highly dependent on successive human interventions.
It is precisely this phase that Davis, a young Franco-American company, is trying to industrialize today, founded by MEHDI RAIS and AMINE CHRAIBI, only a few months ago. Davis announces a pre-seed fundraising of €4.7 million, from Heartcore Capital and Balderton Capital, with participation from Evantic, Yellow VC and Entrepreneurs First. Behind this operation lies an ambition broader than architectural automation: to drastically reduce real estate development time and transform a historically sequential industry into infrastructure driven by generative models.
Thus Davis claims to be able to reduce certain upstream phases from several months to a few days thanks to a combination of artificial intelligence and human expertise. The company centralizes regulatory, technical and economic data in order to generate feasibility studies and directly usable architectural designs and architects then intervene to examine, adjust and validate the results before delivery.
With Gaudi-1, the company says it has developed an approach distinct from traditional diffusion models used in image generation. Instead of operating in pixel space, the model would operate in space, directly manipulating architectural components (rooms, walls, circulation, fixtures) in order to produce configurations compatible with the regulatory and economic constraints of a real project.
This specialization of models probably constitutes one of the strong trends in the current AI ecosystem. After the phase dominated by general models, many start-ups are now developing architectures trained on specific business constraints. In the case of real estate, AI must not simply produce a convincing image but must integrate town planning rules, technical constraints, financial ratios, local standards and very concrete operating logics.
The company already claims to be working with several leading developers and plans several hundred projects by the end of 2026.
