‹ Plant and infrastructure operator agents

Invertix

Energy Superintelligence, AI that operates critical infrastructure

invertix.ai

2.8Score#404 of 999 agents#2 of 10 in Plant and infrastructure operator

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Why it ranks #404

Invertix, an AI plant and infrastructure operator from YC F26, ranks #404 of 999 AI agents and #2 of 10 in Plant and infrastructure operator, with a score of 2.8 of 10. Data as of 3 Oct 2026.

The score adds four parts, each 0 to 10, by weight. Every point comes from public evidence. How scoring works ·

Proof 0 × 30% 0.0
Scale 3 × 30% 0.9
Momentum 4 × 25% 1.0
Autonomy 6 × 15% 0.9
= 2.8

Proof
Do customers use it? 30% of the score
0
No public claims of customers or revenue yet.
Not found: revenue or growth (+4), named customers (+3), a customer result (+2), a customer count (+2), work done at scale (+2)
Scale
Is there a real company behind it? 30% of the score
3
8 people.
- +3
  8 people on its YC page5 to 9 people
Not found: funding round in its news (+3)
Momentum
Is it shipping and growing now? 25% of the score
4
Shows a recent launch and a new batch.
- +3
  Launched in the last 6 monthsInvertix: Energy SuperintelligenceLaunch YC post · 28 Sep 2026
- +1
  New: YC Fall 2026
Not found: open roles on YC (+2), new traction claim in the last 6 months (+2), funding news in the last year (+2)
Autonomy
How much of the job does it do on its own? 15% of the score
6
Supervised agent: takes actions in other systems; a person approves key steps.
- +6
  Level 2 of 4: Supervised agentTakes actions in other systems; a person approves key steps.
What the model read
- Acts in other systems: 78% likely
- Finishes whole tasks: 29% likely
- Calls itself an agent: 95% likely
Level 2: Takes actions in other systems; a person approves key steps.

About Invertix

The next constraint is not intelligence, it is the energy and operational cost of running it. We deploy custom AI models inside critical infrastructure companies that ingest SCADA data, work orders, human interventions, operational data no public model has seen, grounded in physics-informed neural networks and differentiable simulators. This creates a flywheel: more operator data → better process understanding → more efficient operations → cheaper energy → cheaper AI → more deployment → more data.

https://www.youtube.com/watch?v=0uDH6pMn5OE

AI compute doubles every 2.2 years. Data center demand grows from 415 to 945 TWh by 2030. Without near-zero energy cost, AI remains limited by human infrastructure. We are the layer that runs AI at zero energy cost. Energy is a $10 trillion market. The infrastructure layer doesn't exist yet. We are building it.