We help solar developers, utilities, and EPCs eliminate environmental surprises and accelerate work by deploying Physics-Informed AI Agents that automate all types of site work in minutes instead of months.
The AI Agent for Solar Energy
Orbyfy Earthflow is building the definitive AI agent for solar energy development, providing a complete picture of all site solar conditions through intelligent interaction across every lifecycle phase. Powered by Physics AI.
Two Exponential Curves. Simultaneously.
We are not riding one exponential curve. We are riding two. When two exponentials collide, you get Reed's Law β not just connected nodes, but explosive, combinatorial value.
3-5x increase in just 5 years
70% of projects face cost overruns
"The most powerful economic convergence in a generation."
The question every hyperscaler is losing sleep over right now:
Solar isn't just the energy transition. Solar is the power supply of artificial intelligence.
Profitable Solar Development is Broken
Environmental surprises β flooding, erosion, wildfire, soil failure β are killing projects after hundreds of thousands of dollars are already spent.
The world of assessment, planning, construction β the full lifecycle of solar β needs to change.
"The current solution is a consultant. Manual. Unscalable. And still failing."
The First Agentic AI Physics-Informed Platform for Solar
Trusted Agent for Solar Energy Development
Orbyfy Earthflow
Think about what a solar developer actually needs to know before committing capital:
Today, answering those questions takes months, costs a fortune, and still surprises you mid-construction.
Earthflow answers all of them in fifteen minutes.
Data Fabric
The industry's most complete environmental intelligence layer for solar.
- 1,000+ fields per site β soil, flood, erosion, seismic, fire, wind, hail
- 70+ sources: USDA, NOAA, NASA, FEMA, USGS
- 23 government APIs, 6 satellite systems
- No more data gaps that kill projects mid-construction
Structured-Entropy Physics
Real physics equations β not statistical guesses. Peer-reviewed, published on ResearchGate.
- 85-95% accuracy on erosion, flood, seismic, vegetation risk
- 4-7x more accurate than industry standard models
- 46x in geological transition regions
- Cannot be replicated by prompting a foundation model
Cirra β Autonomous AI Agent
Ask it a question in plain English. Get a bankable answer.
- Full lifecycle: screening β construction β operations
- Works where your team works β Web, Slack, Teams, email
- No blown budgets from environmental surprises
- Finds issues in minutes, before capital is committed
See Earthflow in Action
Structured-Entropy Physics: The Digital Twin Revolution
Every major simulation platformβANSYS, Siemens Simcenter, COMSOL, Cadence, NVIDIA PhysicsNeMo, and the entire EDA industryβuses 200-year-old Fourier-Navier-Stokes physics. We've proven it's fundamentally incomplete. Structured-Entropy captures what Fourier-Navier-Stokes misses, delivering 4-7x predictive accuracy on real experimental data, with peaks up to 46x in transition regions. We built the solution.
The Problem: Fourier's 1822 Assumption
Every major simulation tool assumes diffusivity ΞΊ is constant. Heat/mass spreads at the same rate regardless of local geometry or structure.
This forces simulation tools to add complex turbulence models (k-Ξ΅, k-Ο, LES), effective lengths, and dozens of tunable parametersβpatches for missing physics.
The Solution: Structured-Entropy Physics
Diffusivity D(S) adapts to local thermal structure. High-gradient regions naturally diffuse slower. The geometry itself participates in transport.
One parameter (Ξ²) replaces entire turbulence model zoo. Validated across Weber packed-bed, Sullivan-Thompson rod, and Ilmenau experiments.
This is Why ORBYFY Enables Better Digital Twins
Orbyfy aims to make Simulation OBSOLETE.
"Fourier and NavierβStokes are not wrong; they are incomplete in structured regimes. We've restored the missing geometryβand that changes everything for Physics AI digital twins."
Orbyfy Labs Research Publications
Foundational research driving the next generation of physics-informed AI
Physics AI Neural Network
Multi-modal data flows through Physics-Informed Neural Networks to power predictive environmental intelligence
MULTI-MODAL INPUTS
MODEL OUTPUTS
Platform Capabilities
Comprehensive environmental intelligence across the entire solar development lifecycle.
Grid Dashboard
Cell-by-cell site analysis with 50+ metrics per cell. Visual heatmaps for instant risk identification.
Vegetation Compliance
Multi-satellite vegetation detection at 10m resolution. AI-powered clearing strategy recommendations.
CIRRA AI
Natural language site intelligence with 3,890+ query patterns. Ask questions, get instant answers.
Portfolio Analytics
Multi-site comparison across 504+ sites. Portfolio-level risk scoring and prioritization.
Site Monitor
Real-time environmental tracking with 24/7 monitoring. Automated alerts for changing conditions.
Report Generator
Professional documentation with 5 report types. Export-ready for permits and stakeholders.
The Perfect Storm: AI Meets Solar Acceleration
Three forces are converging NOW to create an unprecedented opportunity.
AI Power Demand Explosion
Data centers need 165-280 GW by 2030 - a 3-5x increase. NVIDIA, Microsoft, and Google are all racing to secure solar capacity.
Solar: The Only Fast Solution
1-2 years to deploy vs 4-7 for gas/nuclear. IRA tax credits accelerating adoption. 20% CAGR projected through 2030.
AI Technology Now Capable
Physics-Informed Neural Networks mature enough for production. LLM costs dropped 100x enabling practical Agentic AI deployment.
"First mover advantage is critical in vertical SaaS. The B&V partnership window is open now, and enterprise customers are ready to buy today."
The First Physics-Informed Agentic AI for Solar
Why traditional AI won't work here - and why competitors can't catch up.
Physics-Informed Neural Networks
Accuracy vs 60% from generic ML. RUSLE erosion constrained by real physics equations - cannot be replicated by prompt engineering.
Agentic AI Architecture
Cheaper than LLM approaches ($0.0001/query vs $2.50). Specialized agents orchestrate physics models, not just generate text.
Environmental Data Fabric
Months to replicate. 1,000+ fields from 70+ sources, 23 government APIs. 2+ years and $500K+ investment to build.
Why Competitors Can't Catch Up
- OpenAI/Anthropic: No physics domain expertise
- Traditional Consultants: Can't scale, too slow
- GIS Companies: No AI/ML capabilities
- Time to replicate: 24-36 months minimum
Strategic Validation
Black & Veatch IgniteX
Selected from 78+ startups for strategic partnership. Direct access to 30% of US solar market.
Validated with Real Projects
B&V IgniteX Selection
Selected from 78+ startups for strategic partnership
Idea Conceived
Identified the $6B solar siting problem
Working Prototype
Full platform built in <2 months with Physics AI
First Contract Signed
First enterprise customer commitment secured
3 Beta Customers
Expanded to three enterprise beta customers
Beta Launch
Platform beta launch with paying customers
Agentic AI SaaS + API Revenue
Agentic AI Platform
Autonomous AI agents for site assessment, construction monitoring, and operational optimization. Per-agent subscription model.
Developer APIs
CIRRA AI queries, environmental data access, and GeoAI satellite analysis. Usage-based expansion revenue as customers scale.
Essentials
85% Gross Margin
Basic site assessment and reporting for small developers.
Professional
87% Gross Margin
Full platform with CIRRA AI and portfolio analytics.
Enterprise
90% Gross Margin
Unlimited access with dedicated support and custom integrations.
Path to $100M
Capital-efficient growth to $100M ARR by Year 5
A $2B+ Market Opportunity
TAM
Global renewable energy environmental intelligence (solar, wind, storage, grid)
SAM
North America solar, wind, storage, and grid infrastructure
SOM
US utility-scale solar + construction monitoring + operations
Market Segments
Prior Experience + Deep Domain Expertise
20+ years building energy technology platforms.
Hara Brar
Engineer. MIT, Stanford, UofT. 20+ years energy tech. Founded OpenAxis (sold to Deloitte). Top 40 under 40.
Greg Elliott
20+ years cleantech/fintech. CEO Dreamshare Learning. Energy & Utilities. Financial Services. Sales.
Mike Smith
MIT, Apple, Google. Founded Agentic AI Platform for Research.
Andrew Elliott
Physics AI specialization. Physics-constrained machine learning expertise.
Tyson Rose
Head of Development. Full-stack AI/ML engineering.
Ryan Tyler
Go-to-market execution. Enterprise sales experience.
Strategic Partners
Full Agentic AI Platform by January 2027
"In 12 months, Orbyfy will have autonomous AI agents managing every phase of solar development - from site selection to construction monitoring to operational optimization."
Environmental Assessment Agents
Automated site analysis, risk scoring, and compliance checking
Construction Monitoring Agents
Real-time satellite monitoring during construction phase
Operational Risk Agents
Ongoing environmental risk monitoring and optimization
Autonomous Site Selection Agents
AI-driven site identification and portfolio optimization
Full Agentic AI Platform
End-to-end autonomous agents for solar lifecycle
The Agentic AI Difference
- Not just chatbots - autonomous agents that execute workflows
- Agents coordinate physics models, satellite data, and regulatory databases
- Self-improving through operational feedback loops
- Human-in-the-loop for critical decisions, autonomous for routine analysis
Why Won't This Work? Risks We've Mitigated
Addressing objections head-on to build credibility.
Seed Raise
Target Q2 2026 β Capital-efficient path to profitability in 22 months
USE OF FUNDS
MILESTONES UNLOCKED
"The unstoppable force of AI demand has met the immovable object of broken solar development. We are the answer. We are live. We are ready."
Contact: hara@orbyfy.com
See Earthflow in Action
Watch a walkthrough of the platform's capabilities