Inviscid AI

Inviscid AI

Real-time Building Simulations to Optimize Energy and Operational…

Winter 2026ActiveIndustrialsEnergyWarehouse Management TechIoTSustainabilityProptechSan Francisco, CA, USA; Remote
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.

Verdict

High Signal
Market Opportunity
Building energy optimization and HVAC efficiency is a massive market — commercial buildings account for ~40% of US energy consumption and the global building automation market is $100B+. ICP is reasonably clear: facilities managers at commercial buildings and data centers. Monetization path via SaaS + integration services is plausible, and data center cooling optimization is an especially hot subsegment given AI infrastructure buildout.
Low Signal
Founder Signal
No LinkedIn data available for either Kabir Jain or Ziming Qiu. YC bios are essentially placeholders ('I like physics and AI', 'I make simulations :)'). Without LinkedIn profiles, work history, prior roles, or any verifiable experience, founder quality cannot be assessed — this is a significant blind spot that defaults to low confidence.
Medium Signal
Competition
No competitor data was returned, but the space has real incumbents: Siemens Enlighted, Johnson Controls OpenBlue, Schneider Electric EcoStruxure, and startups like 75F and Willow. Physics-informed neural networks for CFD acceleration is a genuine technical differentiator (vs. pure ML), but this approach is also being pursued by players like Ansys and NVIDIA Modulus. Differentiation exists but needs to be defended.
Medium Signal
Product
Website shows case studies with specific metrics (240x faster CFD, 40% better flow, 600x faster storm surge forecasting) but no named customer logos, no pricing page, and no verifiable revenue or usage data. The case studies lack customer names or testimonials, making it hard to distinguish real deployments from internal demos or pilot work.
OverallC Tier

Inviscid AI is targeting a real, large market with a technically credible approach — physics-informed neural networks for building simulation is a legitimate niche with strong demand, especially for data centers. However, the founder profiles are essentially invisible: no LinkedIn data, no verifiable work history, and YC bios that say nothing substantive. The case studies on the site lack named customers, making it impossible to confirm real commercial traction versus demo work. The coastal infrastructure and storm surge cases suggest the team may be spreading focus too thin across verticals. Without visible founder credentials or confirmed paying customers, this is a technically interesting but unvalidated early-stage bet.

Active Founders

Kabir Jain
Kabir Jain
Founder

I like physics and AI

Ziming Qiu
Ziming Qiu
Founder

I make simulations :).

Inviscid AI
Inviscid AI
TierC Tier
BatchWinter 2026
Team Size2
StatusActive
LocationSan Francisco, CA, USA; Remote