Instead of waiting weeks for engineers to simulate how air moves through a building, Inviscid AI's neural networks do it in seconds and automatically adjust the HVAC system to save energy.

Traditional computational fluid dynamics (CFD) simulations for building airflow can take days to weeks to complete, require specialized engineering talent, and produce static snapshots that are outdated by the time they're delivered. Inviscid AI replaces this entire workflow with Physics-Informed Neural Networks (PINNs) that embed thermodynamic and fluid dynamic equations directly into the neural network's loss function, ensuring every prediction respects physical laws. These surrogate models are trained on building geometry, historical sensor data, and environmental conditions, then deployed as real-time inference engines within the digital twin. As IoT sensors stream live temperature, humidity, pressure, and occupancy data, the PINN model continuously recalculates optimal airflow patterns and feeds updated setpoints directly to the building management system. This closed-loop architecture eliminates the latency between insight and action, enabling dynamic zone-by-zone HVAC optimization that adapts to changing occupancy, weather, and equipment conditions in real time — something no traditional BMS or periodic CFD study can achieve.

It's like replacing a weather forecast that takes a week to compute with a meteorologist who can predict the exact breeze in every room of your office building before you even feel warm.

Inviscid AI builds a virtual replica of your data center that predicts where heat will build up before it happens and automatically adjusts cooling to prevent outages while slashing electricity bills.

Data centers consume approximately 1–2% of global electricity, with cooling accounting for up to 40% of total facility energy use. Traditional thermal management relies on over-provisioned cooling systems and static airflow configurations, leading to massive energy waste and persistent hotspot risks. Inviscid AI's digital twin ingests the full 3D geometry of the data center — rack layouts, raised floor configurations, CRAC/CRAH unit positions, containment structures — and overlays real-time telemetry from thousands of temperature, airflow, and power sensors. The PINN-based simulation engine models convective heat transfer, turbulent airflow mixing, and equipment thermal output simultaneously, producing a continuously updated thermal map of the entire facility. When the model detects an emerging hotspot or identifies cooling capacity being wasted on already-cool zones, it autonomously adjusts variable-speed fan drives, chilled water valve positions, and airflow dampers through direct BMS integration. The physics-informed approach ensures that recommended actions won't create downstream thermal problems — a critical safety guarantee that pure black-box ML cannot provide. This enables data center operators to safely reduce cooling redundancy, defer capital expenditure on new cooling infrastructure, and maintain SLA compliance even during demand spikes or equipment failures.

It's like having a chess grandmaster who can see 50 moves ahead playing against your data center's heat — except every move also lowers your electric bill.

Inviscid AI predicts how many people will be in each part of a building throughout the day and automatically pre-adjusts energy systems to avoid expensive peak electricity charges — and even gets paid by the utility for doing it.

Peak demand charges can represent 30–50% of a commercial building's electricity bill, yet most buildings operate HVAC, lighting, and plug load systems on fixed schedules that ignore actual occupancy and utility pricing signals. Inviscid AI's platform combines its physics-informed simulation engine with occupancy prediction models trained on historical badge-in data, Wi-Fi device counts, CO2 sensor trends, and calendar system integrations. The system forecasts zone-by-zone occupancy hours in advance, then runs the digital twin forward in time to determine the optimal pre-conditioning strategy — cooling or heating zones before occupancy peaks arrive so the building can coast through high-tariff periods with reduced active energy draw. Simultaneously, the platform monitors real-time grid signals (pricing, frequency, demand response event notifications) and calculates the building's available flexibility — how much load can be shed or shifted without violating comfort constraints, as verified by the physics-informed thermal model. This transforms the building from a passive energy consumer into an active grid participant, capable of enrolling in utility demand response and ancillary services programs. The physics-informed guarantee is critical here: unlike rule-based or black-box approaches, the PINN model can certify that a proposed load reduction won't cause thermal comfort violations two hours later due to thermal mass dynamics, giving building operators and grid operators confidence in the committed flexibility. This creates a new revenue stream while simultaneously reducing the building's largest controllable cost category.

It's like a restaurant chef who knows exactly how many guests are coming, pre-preps everything during off-peak grocery prices, and then gets paid by the power company for not turning on the deep fryer during rush hour.