A New Era of Home Building Powered by Quantitative AI

At OpenHouse Research, we believe in pushing the boundaries of what's possible in the home building industry through cutting-edge technology. Inspired by Meta's Large Concept Models (LCM) and their new approach to embedding language-agnostic and modality-agnostic concepts into high-dimensional spaces, we share our most recent research as part of our ongoing effort and commitment to advancing technology for the home building industry. We hope these ideas will inspire more people to join us and create solutions tailored to the challenges faced by today's volume home builders:

1. Embedding Quantitative States Inspired by the LCM Architecture

Similarly to how Meta's LCM embeds abstract concepts into a shared embedding space, our Quantitative AI Engine encodes quantitative states governed by the laws of physics into vector embeddings. Our approach integrates physical constraints directly into the embeddings, ensuring predictions that align closely with real-world dynamics—a significant advancement over traditional methods. This enables us to capture the fundamental principles that drive real-world dynamics, from cause-effect of management decisions to resource allocation.

2. Managing Temporal Complexity with Diffusion Probabilistic Models

Diffusion modelling techniques address the complexities of incomplete data, lagged temporal dependencies in time series, and the generation of new quantitative states, offering capabilities that go beyond traditional data modelling approaches. Quantitative AI models can uncover hidden patterns and relationships while maintaining scalability for diverse home-building applications. This capability empowers our home builder partners with deeper insights into market demand and operational efficiencies, setting Quantitative AI models apart from traditional methods.

3. Building a Dynamical System Simulation Engine

By combining transformer architectures with diffusion models, we are creating a framework that embeds multivariate states across different times and models them as quantitative state machines. This innovative framework enables us to simulate real-world constraints and market volatilities with unmatched accuracy, positioning it as a next-generation tool for home-building optimization. Our ambition is to incorporate both the laws of physics and real-world constraints into Large Quantitative Models, enabling us to simulate the dynamics of market volatilities and operational constraints, optimize operational decisions in real-time, and eventually serve as the intelligence layer of AI agents.

Why This Matters for the Future of Home Building

Predictive and actionable insights can make all the difference in an industry where cycle times and profitability are critical. A high-performance Quantitative AI Engine equips home builders with the tools to:

• Accurately Predict Market Demand: Gain precise insights into evolving trends.
• Reduce Cycle Times: Proactively pinpoint inefficiencies and implement solutions to prevent disruptions from cascading through construction.
• Enhance Return on Net Assets (RONA): Strategically align production capacity with market demands to maximize profitability and operational efficiency.

At OpenHouse.ai, advancing AI and technology is a means to an end—empowering the future of home building. By blending state-of-the-art AI research with practical industry applications, our goal is to help home builders achieve greater efficiency, profitability, and homeowner satisfaction. Ultimately, we believe that helping home builders meet market demands with improved efficiency and quality allows the industry to build better homes for the market.

I'd love to hear your thoughts on how we can continue to evolve technology for our industry. We invite more industry leaders and tech founders to discuss how we can shape the future of home building together.