Hi WFW VC Teardowns,
I’m reaching out to introduce GridForge — an autonomous orchestration layer for distributed energy systems.
As electrification accelerates globally, the electricity grid is becoming a real-time coordination problem. EV fleets, batteries, data centres, industrial loads, and renewables are all interacting dynamically — but grid operators still rely on legacy forecasting and static infrastructure upgrades.
GridForge builds the real-time decision engine that coordinates distributed energy assets as a unified, self-optimising system.
Instead of upgrading physical infrastructure, networks can increase effective capacity using software.
We’ve completed three paid pilots across New Zealand and Australia managing 42MW of flexible capacity and are now preparing for scaled deployments.
We’re raising a $6M USD seed to expand orchestration capability and deploy across our first 10 utility customers.
Best,
Samir Patel
Founder & CEO
GridForge
The Problem
The grid was designed for one-way power flow.
The future grid is:
- Bidirectional
- Distributed
- Electrified
- Software-dependent
Utilities face:
- EV charging spikes
- Renewable intermittency
- Data centre load concentration
- Political resistance to large infrastructure builds
Physical upgrades take 5–10 years. Electrification is happening now.
Without intelligent orchestration, grid constraints become the bottleneck to decarbonisation.
The Solution
GridForge is building the autonomous operating system for distributed energy.
The platform:
- Connects to batteries, EV fleets, solar farms, industrial load, and data centres
- Predicts local congestion 5–60 minutes ahead
- Dispatches distributed assets automatically
- Continuously learns optimal load-shifting behaviour
- Provides operators with override and visibility layers
Think of it as:
Air traffic control for electrons.
The long-term vision is a fully self-optimising distributed grid.
Why This Is Unique
Most grid software:
- Provides analytics dashboards
- Recommends actions
- Requires manual intervention
GridForge executes autonomously.
The core IP:
- Real-time multi-node congestion prediction engine
- Reinforcement learning dispatch model trained on live grid data
- Cross-asset optimisation (EV + battery + industrial load simultaneously)
The moat compounds with:
- Every additional connected asset
- Every congestion event
- Every market integrated
Over time, GridForge becomes the intelligence layer that new distributed assets plug into by default.
Early Traction
- 3 paid pilots
- 42MW under orchestration
- $420k ARR (early contracts converting to multi-year)
- Demonstrated 9% peak load reduction in pilot region
- 2 additional utilities in procurement process
Initial Wedge
Start with:
Mid-sized utilities facing EV adoption spikes but lacking capital approval for new substations.
Deliver:
- 5–10% peak demand reduction
- Deferral of $10M+ infrastructure upgrades
- Rapid ROI inside 12 months
Then expand to:
- Data centres
- Industrial energy users
- Virtual power plant marketplaces
- Cross-border grid markets
Market Opportunity
Global electricity grid investment is projected to exceed $20T by 2040.
If distributed orchestration becomes a standard layer:
This is infrastructure-scale software.
Even capturing a fraction of global utilities represents multi-billion revenue potential.
The upside case: GridForge becomes the default operating layer for distributed grids.
Competition / Risk
Incumbents:
- Siemens
- GE Grid
- Schneider Electric
They build hardware-heavy, enterprise contracts with slow innovation cycles.
Risk:
- Long utility sales cycles
- Regulatory dependency
- Utilities building internal tools
Counter:
If GridForge becomes the data-rich, learning system that improves with scale, incumbents become integration partners or acquirers.
Team
Samir Patel (CEO): Former grid systems engineer, worked on national transmission upgrade modelling
Mei Chen (CTO): PhD in distributed systems optimisation, ex–DeepMind energy optimisation team
Advisory: Former national grid operator CTO
The Ask
$6M USD Seed to:
- Expand engineering team (RL + grid modelling)
- Deploy first 10 scaled customers
- Achieve 150MW under orchestration
- Demonstrate autonomous dispatch across multi-asset environments
Target milestone for Series A:
- $2M+ ARR
- 150–200MW under management
- Clear repeatable sales motion
Quick Navigation:
Back to 
🚨 Disclaimers: All startup companies, business models, products and founders described in VC Teardowns are fictional and created solely for educational purposes. Any resemblance to actual companies, persons or events — past, present or future — is purely coincidental. The opinion of each participating VC reflects their individual perspective and does not represent their firm as a whole. A teardown should not be treated as a universal rulebook. Founders are encouraged to engage investors early and build relationships — early conversations are often exploratory, not evaluative.