Globeholder AI

[ Research ]

A frontier AI lab building autonomous agents for engineering and critical infrastructure decisions.

Language models learned to reason about text. We build models that reason about the physical world: the ground, the grid, the weather and the economics of what gets built on them.

Request a demo

[ Benchmarks ]

Measured against the field.

We put Thinking Lab™ head to head with leading general-purpose AI models on real siting decisions, and score every recommendation against infrastructure that was actually built.

Solar siting · Bakersfield, California

Distance to the nearest operating solar installation

12.30 km

mean siting error for Thinking Lab™, against 35.04 km for the next-best general-purpose model

Mean absolute error, km · lower is better

Ten ranked sites per model, each scored by its distance to the nearest operating installation. Composite proximity score: 86.3 / 100 for Thinking Lab™, against 63.8 for the next-best model.

Source: Globeholder AI benchmark report — The ROI Impact of Solar Panel Site Selection Accuracy →

Wind siting · Central New Mexico

Predictions landing on a real, operating turbine

2.64 km

median siting error for Thinking Lab™, against 10.19 km and 23.27 km for the general-purpose models

Share of predictions within radius · higher is better

Within 5 km Within 10 km

120 candidate points per model, scored against 1,289 operating turbines. Thinking Lab™ has the highest hit-rate at 5, 10 and 20 km, and places 120 of 120 within 50 km.

Source: Globeholder AI benchmark report — The ROI Impact of Wind Turbine Site Selection Accuracy →

Business impact · illustrative

How site selection accuracy translates into ROI

1M–6M USD

estimated avoided feasibility and early engineering spend per development program

Most developers spend 50,000 to 200,000 USD to assess a single candidate site once it reaches detailed study. A more precise screen removes 20 to 30 of those dead-end investigations before money is committed. In the wind benchmark, Thinking Lab™ produced 63 decision-grade candidates per 100 locations screened, against 27 and 18.

Estimated avoided spend, USD · higher is better

At 50k USD per assessment At 200k USD per assessment

An illustrative model of the impact, not a measured outcome.

Source: Globeholder AI benchmark report — The ROI Impact of Wind Turbine Site Selection Accuracy →

Source: Globeholder AI benchmark report — The ROI Impact of Solar Panel Site Selection Accuracy →

General-purpose models are anonymised; the same letter marks the same model in both benchmarks. Full model-by-model results are in the linked reports.

[ Research areas ]

The science behind Thinking Lab.

Physical world models

Models that learn how terrain, subsurface, structures, weather and grids interact, and predict what happens when one changes.

How do we represent physical causality across scales, from a borehole to a basin?

Multimodal geospatial reasoning

Reasoning jointly over imagery, elevation models, geological surveys, network data and documents.

How should a model weigh sparse ground truth against abundant remote sensing?

Orchestration and planning

An intelligence layer that turns a question into a sequence of physical tests, and replans as evidence arrives.

What does an optimal investigation plan look like under uncertainty and time pressure?

Physics-grounded verification

Checking outputs against physical laws, engineering codes and real outcomes, so answers are correct and not just plausible.

How do we measure defensibility, not just accuracy?

Organizational memory

Learning from an organization's own decisions and outcomes, privately, so each project improves the next.

How does tacit field judgment become knowledge a model can reuse?

Decision-grade evaluation

Benchmarks built from real capital projects, where ground truth is what got built and what it cost.

How early can a model predict a cost overrun that humans missed?

[ How we work ]

Research principles.

Physics first

If an answer violates how the world works, it's wrong, however confident the model sounds.

Built alongside practitioners

Researchers work next to geologists, grid engineers and investors on live decisions.

Measured on real projects

Progress is judged by decisions made and outcomes delivered, not only by benchmarks.

Accountable by design

Every output is traceable to its evidence, so humans can check it and stand behind it.

[ Join the lab ]

Help build the model of the physical world.

We're hiring in machine learning, geoscience, power systems and infrastructure engineering.

See open roles

Bring us your hardest infrastructure decision.

We'll put the agents on it, live with your team, and show you a complete, defensible answer.

Request a demo