Inviscid AI provides a SaaS platform that builds physics‑informed neural network digital twins of building HVAC networks, delivering full‑scale airflow and thermal predictions in milliseconds. By ingesting real‑time IoT sensor data and integrating with BMS protocols, the system automatically adjusts set‑points for continuous optimization, achieving energy reductions of 30 %+ without compromising occupant comfort.
Funding
Funding not disclosed
Founders
Product
Problem
Conventional computational fluid dynamics (CFD) simulations for HVAC systems require hours of compute time and significant licensing costs, producing results that are often obsolete by the time they are delivered. This latency forces facility operators to rely on static set‑points, leading to energy waste and missed opportunities for comfort‑driven control.
Solution
Inviscid AI delivers a SaaS platform that creates physics‑informed neural network (PINN) digital twins of building HVAC networks. The neural operators generate full‑scale airflow and thermal field predictions in milliseconds, enabling continuous, closed‑loop optimization. Real‑time streams from IoT temperature, airflow, and occupancy sensors are ingested and fused with the digital twin, while bidirectional integration with existing building management systems (BMS) applies AI‑derived set‑points automatically. The platform’s cloud‑hosted analytics maintain state‑of‑the‑art accuracy (L2 error ≈ 0.002) while reducing simulation latency by three orders of magnitude. As a result, customers achieve energy reductions of 30 % + without compromising occupant comfort or equipment uptime. Onboarding is demo‑driven, after which the solution operates under a subscription model.
Target Audience
Primary customers are facility managers, building engineers, and operations teams responsible for HVAC optimization in commercial office towers, data centers, and industrial plants. The solution also serves real‑estate owners seeking portfolio‑wide energy efficiency.
Features
- Physics‑informed neural operators trained on high‑fidelity fluid dynamics datasets, delivering sub‑0.003 L2 error across standard PDE benchmarks.
- Millisecond‑scale full‑building airflow and thermal simulations that replace hour‑long traditional CFD runs.
- Automated ingestion of real‑time IoT sensor data (temperature, airflow, occupancy) via MQTT/REST APIs.
- Native BMS connectors (BACnet, Modbus, LonWorks) for autonomous HVAC set‑point adjustments.
- Interactive web dashboard with live heat‑map visualizations, hotspot alerts, and predictive failure diagnostics.
- RESTful API and SDKs (Python, JavaScript) for custom integration into existing energy‑management workflows.
- HIPAA‑ and GDPR‑compliant cloud storage with end‑to‑end encryption and role‑based access controls.