Energy Infrastructure Intelligence
Early-stage energy projects are decided long before an engineering study exists — on partial information, under queue uncertainty, with capital already at risk. We are building an AI-powered decision intelligence system that evaluates project intent against real infrastructure data at that stage, turning fragmented inputs into predictive decision making.
PROJECT INTENT + ENERGY DATA → INFRASTRUCTURE & CAPITAL DECISIONS
Input — project intent
- LOAD
- Magnitude, profile, growth curve, coincidence
- LOCATION
- Siting envelope, land, grid proximity, permitting regime
- RELIABILITY
- Uptime target, redundancy class, islanding requirement
- ECONOMICS
- Capital envelope, cost-to-serve ceiling, offtake structure
- TIMELINE
- Energization date, tolerance for queue risk
- OPERATIONS
- Fuel access, emissions limits, staffing, maintainability
Evaluated against
Generation & storage
Technology cost curves, dispatch, degradation
Grid & interconnection
Capacity, queue position, upgrade exposure
Geography & land
Resource quality, terrain, environmental constraint
Market & policy
Pricing, incentive structures, tariff regimes
Output
Comparable configurations, with the assumptions visible.
Feasibility
Which configurations can actually be built here, on this timeline, under these constraints.
Cost & capital shape
Cost-to-serve, capital profile, and where the estimate is most fragile.
Risk & sensitivity
What breaks the project, and how far each input can move before it does.
Status
In active development with design partners in generation, data-center load, and industrial siting. We are looking for early-stage developers and capital teams willing to test the model against real projects.
There is no public demo yet. What we offer instead is a working session: we walk through the model against one of your real projects and share what the system already resolves.
Shared with Resactly
Nothing about a menu resembles an interconnection queue. What carries across is the architecture: intent captured as structure, fitted against domain data, resolved into an explainable recommendation, and corrected by the outcome that follows.
The architecture →