The startup develops spatial data technology that utilizes volumetric and spatial datasets to create detailed 3D models for interior professionals. This enables real estate agents to efficiently capture and manage spatial data, enhancing portfolio optimization and maximizing property value.
Funding
$38.2M raised to dateRaised to date based on public sources. This may differ from the amount the company actually raised and is based only on what is publicly available on the internet.
Founders
Product
Problem
The real estate industry lacks a standardized, reliable method for capturing and managing interior spatial data, leading to inefficiencies in property management, higher carbon emissions, and a lack of transparency in real estate transactions. Existing methods often rely on manual measurements or inconsistent data capture techniques, resulting in inaccurate or incomplete datasets.
Solution
Pupil provides a platform for creating verified spatial data and digital twins of interior spaces, establishing a new standard for accuracy and reliability in the real estate sector. Using a combination of capture-as-a-service, a verified data library, and a proprietary tech stack, Pupil digitizes buildings with millimeter-level accuracy. The platform leverages computer vision and AI to transform raw data into 3D digital reconstructions, enabling applications such as virtual viewings, remote diligence, and energy profiling. By taking buildings online, Pupil aims to drive efficiency, sustainability, and transparency in the built environment, reducing carbon emissions and transforming real estate operations globally.
Target Audience
Pupil targets residential and commercial real estate professionals, property insurance providers, banks, mortgage lenders, government agencies, and AEC (architecture, engineering, and construction) firms seeking accurate spatial data and digital twins for various applications.
Features
- Capture-as-a-service with trained Digital Surveyors ensuring a complete data chain of custody
- Verified spatial data library containing millions of square feet of captured interior space
- Proprietary cloud infrastructure supporting seamless API integrations
- AI-powered engine for semantic and instance-level segmentation of interior spaces
- Digital twins with guaranteed accuracy to 99%
- 3D reconstruction from volumetric and image-based spatial datasets
- Real-time collaboration features within the Pupil Dimensions software
- Integration with AWS infrastructure for secure and scalable data storage