Rhizome provides an AI‑driven grid planning platform that combines high‑resolution climate projections, asset inventories, and equity metrics to produce asset‑level risk models for utilities. The web‑based tool continuously updates with new weather, grid, and load data, enabling utilities to pinpoint vulnerable components and prioritize resilient, equity‑focused investments while integrating with existing asset‑management systems.
Funding
$6.5M raised to dateRaised to date based on public sources. This may differ from the amount the company actually raised and is based only on what is publicly available on the internet.
6OFounders
Product
Problem
Utilities must plan decades‑long infrastructure investments using historical data that does not reflect accelerating climate threats, leading to under‑investment, higher outage risks, and regulatory challenges.
Solution
Rhizome offers an AI‑driven grid planning platform that integrates high‑resolution climate projections, asset inventories, and equity metrics to generate asset‑level risk models. The system continuously updates as new weather data, grid changes, and load patterns become available, allowing utilities to identify vulnerable components and prioritize investments that improve reliability and meet regulatory expectations. Results are delivered through a secure, web‑based interface that can be linked to existing asset‑management and investment‑planning tools, enabling data‑backed decision making for resilient, equitable grid upgrades.
Target Audience
Primary customers are electric utilities and grid operators responsible for long‑term infrastructure planning, as well as regulatory and policy teams that evaluate resilience investments.
Features
- Autonomous machine‑learning models that synthesize climate scenarios, weather extremes, and asset condition data to quantify failure risk for individual grid components
- Real‑time incorporation of new grid configurations, load growth, and electrification trends to keep risk assessments current
- Access to the highest‑resolution climate datasets across multiple Shared Socioeconomic Pathway (SSP) scenarios for robust hazard characterization
- Community‑level vulnerability analysis using public socioeconomic data to inform equity‑focused investment decisions
- Secure, infrastructure‑grade data protection and end‑to‑end encryption for sensitive utility information
- Seamless integration with existing asset‑management, enterprise resource planning, and investment‑planning systems via APIs
- Historical archive of extreme‑weather events and asset performance to support scenario analysis and model validation