The most expensive fault on an electrical grid is the one you don't see. It's the high-impedance event where a tree branch brushes a line, or a failing insulator arcs silently, building heat over hours or days. By the time a traditional protection relay trips, it's often too late. The fault has already become an outage, or worse, a fire. This is the quiet, expensive problem Nyquis wants to solve, not with more hardware, but with smarter listening.
Founded in 2024, the San Francisco-based startup is building a point-on-wave sensor system paired with a machine-learning model trained on over 100,000 real electrical faults [Nyquis, Unknown]. The bet is that by installing a fraction of the hardware required by legacy monitoring systems, utilities can get a real-time, AI-powered map of their entire distribution feeder, catching anomalies long before they escalate [Perplexity Sonar Pro Brief, Unknown].
A hardware wedge for a software problem
Nyquis's approach turns a classic grid problem on its head. Traditionally, getting high-resolution visibility into a distribution network meant installing sensors at a dense interval, a capital-intensive proposition that has limited adoption. Nyquis claims its architecture can cover an entire feeder with significantly fewer devices [Perplexity Sonar Pro Brief, Unknown]. The key is in the data captured by its point-on-wave sensors and the AI model that interprets it.
The founder's second act in infrastructure
Nyquis is the latest venture from serial entrepreneur David Gobaud, who is also a Senior Venture Partner at Pioneer Fund [Pioneer Fund, 2026]. His background is a mosaic of tech and law, with a computer science degree from Stanford and a JD from Harvard Law School [me.sh, 2026]. His track record includes founding or co-founding several companies, most notably the Y Combinator-backed mobile fuel startup Yoshi and Mobius Network, which raised $39 million [Forbes, 2018] [Wellfound, 2026].
The competitive landscape and the proof gap
Nyquis is entering a field with established players and well-funded newcomers, all chasing utility budgets that are swelling under regulatory pressure to harden grids against climate change and wildfire risk.
| Company | Primary Focus | Key Differentiator |
|---|---|---|
| Nyquis | Distribution feeder fault detection | AI model trained on 100k+ faults; claims feeder-wide coverage with minimal hardware |
| Utilidata | Grid-edge optimization & volt/VAR control | Deep software integration with utilities; partnership with NVIDIA |
| Whisker Labs (Ting) | Home fire prevention & grid monitoring | Consumer-facing product creates a distributed sensor network |
| Gridware | Grid monitoring for wildfire prevention | Focus on low-cost, ruggedized sensors |
The next twelve months
The path forward for Nyquis is a familiar one in climate tech: prove the unit economics of prevention. The next milestones will be less about the model's training accuracy and more about its performance in the field. Securing and publicly announcing a paid pilot with a regional utility would be a critical validation signal.