AI-driven HVAC sizing, grounded in building science.
RookStack develops residential HVAC sizing technology that applies data, modeling, and machine learning to improve sizing accuracy and reduce systematic oversizing.
What we do
RookStack builds algorithms and software that more accurately size residential heating and cooling equipment using building characteristics, climate data, and performance modeling.
The goal is straightforward: reduce oversizing, improve comfort, and enable better real‑world efficiency.
Focus areas
- Right-sized equipment recommendations
- Performance-aware sizing methodologies
- Scalable workflows for software deployment
- Applied validation and continuous refinement
Who we work with
RookStack collaborates with organizations that want better residential HVAC sizing outcomes and scalable deployment pathways for data-driven methods.
- Research institutions evaluating sizing accuracy and performance outcomes
- Industry partners building products or workflows that depend on better sizing
- Technology platforms integrating sizing into digital customer journeys
- Energy and electrification stakeholders focused on real-world performance
What collaboration can look like
- Method evaluation and benchmarking
- Integration planning and technical scoping
- Data-sharing frameworks and validation design
- Prototype pilots with clear success metrics
Contact
Send a message and we’ll get back to you.