Davis raises $5.5m pre-seed to compress real-estate development from months to days

Davis Raises $5.5m Pre-Seed to Compress Real-Estate Development

May 6, 2026 - 7:51 am

The Paris-based AI-native real-estate company, founded by Entrepreneurs First alumni Mehdi Rais and Amine Chraibi, has secured funding from Heartcore Capital and Balderton Capital, marking an unusual cap-table for a pre-seed round. The technical innovation behind Davis is more intriguing than the headline figure suggests.

There has been a notable trend in 2026: securing European AI seed rounds with co-leading investors becomes increasingly challenging, despite impressive headliners. On Tuesday, Paris-based Davis announced a $5.5 million pre-seed round with Heartcore Capital and Balderton Capital as the lead investors.

The investor list also includes Evantic, Yellow VC, Entrepreneurs First, and an angel network comprising operators and researchers from Meta, Black Forest Labs, Hugging Face, Supabase, Spore.Bio, and former team members of SpaceMaker, an architectural AI startup acquired by Autodesk in 2020.

Davis focuses on bridging the gap between raw land data and credible architectural concepts, a process traditionally taking weeks or months. It aims to streamline this process with an integrated workflow that considers regulatory, technical, and market data as constraints. Davis delivers feasibility studies and design concepts within days, ensuring human architects review every output before client delivery.

The company's approach:

Davis is not selling software; instead, it offers outcomes directly to developers and investors, similar to traditional architectural consultancies. Its unique model allows for pricing as a service rather than a SaaS product, capturing a higher value per project, and overcoming tooling adoption challenges in PropTech integration.

Davis' AI models generate buildings as structured compositions of rooms, walls, layouts, and architectural elements, differing from recent tools relying on continuous diffusion models for image and video generation. This distinction aligns with research trends in this domain.