Inviscid AI

Roadmap & Position in PropTech

Creates digital twins of buildings and data centers to autonomously optimize energy and cooling.

Company Overview

Builds a real-time, physics-informed AI simulation platform that creates digital twins of commercial buildings and data centers to autonomously optimize energy consumption and operational costs using Physics-Informed Neural Networks (PINNs).

What They're Building

The company's public product roadmap & what they're committed to building.

Inviscid AI describes real-time digital twin simulations for commercial buildings and data centers, neural network surrogates replacing traditional CFD solvers, and direct BMS/IoT integration for closed-loop HVAC and energy optimization. Autonomous operational optimization with physically plausible AI recommendations grounded in thermodynamic and fluid dynamic laws.

Latest Intelligence

Zeitgeist tracks private signals to determine where the company is heading strategically.

Data Center Cooling Gets Real-Time Digital Twins

May 13, 2026

Confidence:

High

New Intel: Inviscid AI is using physics-informed neural networks to create real-time digital twins for data center cooling and building energy use. That targets a power and cooling bottleneck for AI infrastructure.

Founder and Key Execs

Ziming Qiu, Co-Founder

Co-Founder

Kabir Jain, Co-Founder

Co-Founder

Founder Force Multiplier

Inviscid AI combines deep academic expertise in Physics-Informed Neural Networks with real-time digital twin architecture, enabling simulations that are orders of magnitude faster than traditional CFD while remaining physically accurate, a capability most competitors lack.

Funding History

2025 | Kabir Jain and Ziming Qiu co-found Inviscid AI.
2025 | Accepted into Y Combinator batch.
2026 | No public funding rounds disclosed; estimated ~$500K (YC standard deal).

Competitors

Digital Twin Platforms

Willow (building digital twins), Autodesk Tandem.

AI Building Optimization

BrainBox AI (autonomous HVAC), PassiveLogic (autonomous building control), Turntide Technologies.

Traditional BMS/Analytics

Siemens Building X, Honeywell Forge, Johnson Controls OpenBlue, SkySpark.

Data Center Cooling

Nautilus Data Technologies, Colovore, Schneider Electric EcoStruxure.

Inviscid AI

's Moat:

Physics-informed neural networks produce results that obey thermodynamic laws, unlike pure ML approaches that can generate physically impossible outputs. Real-time simulation (vs. hours for traditional CFD) enables continuous optimization rather than point-in-time analysis. PhD-level PINN expertise is a narrow talent pool.

How They're Leveraging AI

Physics-Informed Neural Networks

Real-time HVAC airflow simulation using Physics-Informed Neural Networks to replace weeks-long CFD analysis with seconds-fast digital twin inference for commercial buildings.

Digital Twin Thermal Optimization

Predictive digital twin for data center thermal management that autonomously optimizes cooling infrastructure to prevent hotspots and reduce power usage effectiveness (PUE).

Occupancy-Driven Energy Forecasting

Occupancy-adaptive energy forecasting and demand response optimization that dynamically adjusts building systems based on predicted occupancy patterns and grid signals.

AI Use Overview:

Using physics-informed neural networks as real-time CFD surrogates, digital twin thermal optimization, and occupancy-driven energy forecasting.

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