Your AI Training Cluster Thirsty? Let's Talk Water.
We ran the numbers: A 10k H100 cluster can consume 2 million gallons of water a month. Here is the math and the engineering fix.
Chaos. Then insights.
Single-agent systems give you one perspective. That's fine for simple tasks. But real decisions about energy infrastructure require multiple lenses.
So we built a system where six specialist agents collaborate - and disagree.
Focuses on sanctions, policy changes, critical mineral supply chains, and sovereign risk. When there's a regime change in a lithium-producing country, this agent sounds the alarm.
Monitors load balancing, frequency deviation, and grid reliability. This is the agent that notices when ERCOT is running hot before it makes headlines.
Tracks PPAs, RECs, carbon pricing, and regulatory changes. When the EU ETS price moves or a new carbon border adjustment is proposed, this agent assesses portfolio exposure.
Handles predictive maintenance, dispatch optimization, and battery storage. This agent knows when a wind turbine needs service before it fails.
Runs due diligence, risk/return modeling, and portfolio benchmarking. When you're evaluating a new solar project, this agent calculates the risk-adjusted returns.
Forecasts energy costs, models weather impact, and identifies hidden cost drivers. This agent sees the price spike coming before your competitors.
When you ask a question, the orchestrator:
Here's what makes multi-agent systems valuable: agents have different priorities.
Example: You're evaluating a datacenter site in Arizona.
Both the Grid Stability Agent and Investment Intelligence Agent are "right." They're just looking through different lenses.
The orchestrator's job is synthesis, not consensus.
Real decisions need multiple perspectives. A single-agent system would either give you the optimistic view or the pessimistic view. Our system gives you both, with clear reasoning for each position.
The final output might be:
"Arizona site shows strong financial returns (IRR 12.5%) but elevated operational risk from grid constraints. Recommend conditional approval with power curtailment provisions in interconnection agreement and water mitigation plan for ESG disclosure."
Yes. Significantly.
You need:
Also yes.
In our testing, multi-agent responses were rated as "more comprehensive" by domain experts 78% of the time compared to single-agent responses. The key difference: multi-agent systems surface trade-offs that single-agent systems miss.
Better decisions through structured conflict.
That's the goal. That's why we built it this way.
Want to see our six agents in action? Request a demo and ask them a question about your portfolio.
We ran the numbers: A 10k H100 cluster can consume 2 million gallons of water a month. Here is the math and the engineering fix.
Traditional SaaS is too slow for energy markets. We pivoted to 'Autonomous Organization as a Service'—software that works while you sleep.
Giving an agent 30 tools costs $0.45 per run. We implemented a 'Code-First Skills' pattern to drop that to $0.003.
Grid interconnection is the #1 bottleneck for AI. Google X's Tapestry project is trying to virtualize the grid to fix it.
News tells you what happened yesterday. Markets tell you what will happen tomorrow. We built an agent to trade on the difference.
Starting August 2025, mandatory environmental reporting kicks in for AI models. Most CTOs are completely unprepared.
Installed capacity is a vanity metric. LCOE is the only number that levels the playing field between solar, gas, and nuclear.
Grid carbon intensity varies by 3x throughout the day. We built a scheduler that pauses AI training when the grid is dirty.
We didn't want to pay for a Bloomberg terminal, so we wrote a 950-line TypeScript scraper that builds our own intelligence feed.