VeriGrid
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Description
VeriGrid is an enterprise spatial AI software platform designed to solve the multi-year grid interconnection backlog for utility-scale clean energy developers and data center infrastructure operators.
Core Overview
* The Problem: Over $100B in solar, wind, battery storage, and data center projects are trapped in 3-to-5-year regional transmission queue delays (across ISOs like PJM, MISO, and ERCOT) due to slow, manual power-flow modeling.
* The Solution: VeriGrid provides an autonomous spatial intelligence platform powered by Physics-Informed Graph Neural Networks (PI-GNN). It delivers sub-second grid capacity simulations with ~98% benchmark accuracy compared to traditional load-flow solvers (like PSS/E), enabling developers to instantly screen capacity and assess queue risk before making multi-million-dollar filings.
* Business Model: B2B Enterprise SaaS ($5k–$10k/month paid developer pilot contracts).
* Security & Compliance: Built with single-tenant architecture ensuring strict Critical Energy Infrastructure Information (CEII) compliance and SOC 2 alignment.
Key Team Members
About Us
Executive Summary
* Market Opportunity: Over $100B in clean energy and data center projects are stuck in 3-to-5-year queue backlogs across major regional transmission organizations (PJM, MISO, ERCOT) due to legacy, manual load-flow modeling tools. Recent 2026 FERC queue reform regulations place urgent pressure on developers to assess capacity accurately before filing.
* Core Product & Moat: An enterprise SaaS platform powered by Physics-Informed Graph Neural Networks (PI-GNN). VeriGrid enforces Kirchhoff’s physics laws within its neural network layers to deliver sub-second grid capacity simulations at ~98% benchmark accuracy relative to traditional solvers (PSS/E), while maintaining strict CEII security compliance.
* Business Model & Traction: B2B Enterprise SaaS. Targeting 5–10 paid enterprise pilot design partnerships ($5k–$10k/month; $300k+ ARR target) to screen regional queue risk prior to project commitments.
* Capital & Execution: Raising a $1.5M Pre-Seed equity round ($10M post-money valuation) to fund 18 months of operational runway—allocating 60% toward key technical hires (Power Systems AI & Geospatial Engineers) and 30% toward GTM execution. Guided by advisory from Awesome Inc and Auburn University New Venture Accelerator EIRs.