Inviscid AI
Winter 2026 NewReal-time Physics Simulations for Industrial Facilities & Data Centers
Inviscid AI builds physics-informed AI solutions that transform how buildings and data centers operate. By combining real-time IoT sensor data with computational fluid dynamics (CFD) modeling, we create digital twins that simulate building performance in real time and autonomously optimize operations. Our platform optimizes airflow patterns and ventilation strategies to eliminate dead zones, improve air distribution, and reduce the load on mechanical systems. On the energy side, we minimize HVAC power consumption, reduce cooling costs, and lower overall operational expenses while maintaining optimal thermal comfort and indoor air quality. Beyond immediate operational efficiency, we optimize equipment scheduling and maintenance cycles by predicting system behavior under different conditions, allowing facilities managers to proactively address issues before they become problems. Our physics first approach ensures that we're not just optimizing against historical patterns, but optimizing based on a deep understanding of how air, heat, and energy actually move through your building, enabling us to find solutions that traditional rule-based or purely data-driven systems would miss.
AI Investor Summary
Inviscid AI is building a real-time physics-informed AI platform to autonomously optimize operations in industrial facilities and data centers. By integrating IoT sensor data with CFD modeling, they create digital twins that reduce energy consumption and improve efficiency. The company is well-positioned to capitalize on the growing demand for sustainable and optimized building management.
Key Highlights
- ● Innovative application of real-time physics-informed AI for building operations.
- ● Strong technical talent with Google and University of Waterloo backgrounds.
- ● Addresses a significant market need for energy optimization in industrial facilities and data centers.
Risk Factors
- ● Lack of explicit domain expertise in building operations or CFD within the founding team.
- ● Demonstrating clear ROI and overcoming long sales cycles in industrial sectors.
- ● Scalability and defensibility of the AI models and platform beyond initial implementation.
Founders
Kabir Jain is the co-founder of Inviscid AI, a Y Combinator startup focused on AI-powered solutions. His background likely includes significant experience in artificial intelligence and software development, leading to the creation of innovative products at Inviscid AI.
Ziming Qiu is the co-founder of Inviscid AI, a Y Combinator startup focused on AI solutions. His background likely includes significant experience in artificial intelligence and software development, leading to the creation of innovative AI technologies. He is a graduate of the University of Waterloo.
Score Breakdown
Strong technical foundation with Ziming's Google experience and Waterloo M.Math in CS. Kabir's background is less detailed but assumed to be complementary. The team has clear AI and software development expertise, but lacks explicit domain expertise in building operations or CFD. No prior exits mentioned. [Boost +1: Founder from Google]
Large addressable market in industrial facilities and data centers, both of which are under increasing pressure to optimize energy consumption and operational efficiency. The timing is excellent with rising energy costs and sustainability mandates. Regulatory tailwinds for energy efficiency are strong, though adoption cycles in these industries can be slow.
The product shows promise by combining real-time IoT data with CFD for digital twins, offering a technically differentiated approach to operational optimization. The core innovation lies in the real-time physics-informed AI. However, defensibility beyond the initial AI model and integration complexity needs further validation. UX quality and platform potential are not yet fully demonstrated.
Early stage with positive press coverage indicating initial market validation and interest. No specific revenue or user numbers are provided, making it difficult to assess growth rate. Partnerships and investor interest are implied by the YC acceptance and press, but concrete details are missing. [Boost +2: Tier-1 VC: accel]
News
Inviscid AI has launched a new platform that leverages physics-informed AI to optimize energy usage in buildings.
Inviscid AI is developing physics-informed neural networks for real-time computational fluid dynamics (CFD) in data centers and buildings, offering significant speed improvements and potential for substantial energy savings.
Inviscid AI has launched, offering real-time physics simulations and digital twins for built environments, promising to optimize energy costs, design cycles, and live operations with simulations that are up to 1000x faster than traditional solvers.
Inviscid AI, a startup from Y Combinator's Winter 2026 batch, has launched a real-time building energy platform aimed at reducing energy waste.
Inviscid AI, backed by Y Combinator W26, provides autonomous building cooling through real-time physics simulation, promising over 30% less HVAC energy.
Inviscid AI is recognized as a high-signal company with strong founder credentials and a promising product in the massive market of building energy optimization and HVAC efficiency, having secured pilot projects and customer testimonials.
Inviscid AI, a Y Combinator-backed company, has introduced a new platform that leverages physics-based AI to optimize energy usage in buildings.
Inviscid AI develops physics-informed AI solutions for real-time simulation and optimization of buildings and data centers, aiming to reduce energy costs and improve operational efficiency.
Inviscid AI has launched a platform for real-time physics simulations and digital twins, designed to cut energy costs and accelerate design cycles.
Inviscid AI has launched a new platform that uses physics-informed AI to optimize building energy consumption.
Inviscid AI, a Y Combinator startup, is offering real-time building simulations to help reduce energy usage.
Quick Info
- Batch
- Winter 2026
- Team Size
- 2
- Location
- Remote
- Founders
- 2
- Scraped
- 4/10/2026