Welcome to TerraNavitas

Transforming the energy sector through the fusion of AI and physics

Vision

Abundance, responsibly unlocked.

AI × physics at planetary scale, turning noisy infrastructure into orchestrated systems.

Annual global energy investment is over $3.3 trillion and rising, yes trillion with a 'T'. The frontier is not just more capital, but smarter capital that is enabled by data, physics-based simulation, and AI.

From Constraint to Flywheel

Earth-scale data plus physics and AI models close the loop: better maps → smarter siting → de-risked financing → live telemetry → continuous optimization.

Data-to-Decisions

The living grid speaks in signals; humans need decisions. By coupling data, physics, and AI we project those signals onto decision surfaces—shared, auditable views of where to invest, how to operate, and when to adapt.

Every community empowered abundantly

We are not just building tools. We're creating a new category of planet-scale intelligence for energy transformation so that every community can be empowered by abundant, sustainable energy.

Our Method

Method Detail
Data → Harmonized Streams
Our data method builds a unified fabric from messy, multi-modal sources—regulators, sensors, satellites, documents, and simulations. We enforce consistent schemas, semantics, and lineage so every downstream tool sees clean, trustworthy tables instead of bespoke feeds and spreadsheets. A single fabric powers maps, dashboards, models, and agents without re-wrangling. Because everything is versioned, API-first, and self-describing, this backbone scales from a single basin to global portfolios and from one product line to entirely new domains.
Data → Harmonized Streams
Connect wells, cores, satellites, and simulators into one federated Earth-data fabric with lineage, semantics, and low-latency APIs. A single, continuously refreshed backbone that every product and model can trust, query, and extend.
Method Detail
AI → Cognitive Engines
Our AI method layers specialized models and agents on top of the data fabric. Time-series networks, graph models, and structured predictors transform raw signals into forecasts, rankings, and scenarios tuned to physical assets and markets. Tool-using agents orchestrate retrieval, modeling, and aggregation to answer complex questions in one workflow. Because models are modular, explainable, and wrapped in APIs, we can recombine them rapidly for new use cases, scaling from single-asset analysis to portfolio optimization and cross-domain planning.
AI → Cognitive Engines
On top of the data fabric we stack domain-tuned models and autonomous agents that forecast, rank, and recommend. From LSTM production forecasters and GNN prospectivity maps to EnergyGPT and GeoGPT copilots, AI turns raw signals into fast, explainable guidance for every team.
Method Detail
First-Principles Physics → Simulated Reality
Our physics method starts from high-fidelity, multiphysics simulation—governing equations for flow, heat, mechanics, and transport solved on modern compute. These first-principles cores define what is physically possible, setting guardrails for AI. We then compress simulation campaigns into fast surrogates and response surfaces, preserving essential dynamics with orders-of-magnitude speedups. This pattern—simulate, learn, distill—scales from single wells to regional systems and from one resource type to another, while keeping decisions anchored to the underlying physics.
First-Principles Physics → Simulated Reality
Beneath the AI, we run full multiphysics digital twins that obey the laws of flow, heat, rock mechanics, and chemistry. Built on national lab-developed simulators and proprietary mechanistic models, TerraNavitas can simulate geothermal, CO₂ storage, and unconventional reservoirs at field scale, then distill those dynamics into fast surrogates.
Method Detail
Decision Surfaces → Clear actions
Our insight method turns raw outputs into decision surfaces—maps, frontiers, and interactive plots where value, risk, and uncertainty are visible at a glance. Instead of static reports, teams explore how choices move outcomes by dragging sliders and switching scenarios. The same surfaces adapt to different roles, from technical staff to executives, without duplicating logic. Because they’re generated from shared data, models, and physics cores, these interfaces scale cleanly across assets, time horizons, and even entirely new problem domains.
Decision Surfaces → Clear actions
We turn simulations and models into decision surfaces—maps, dashboards, and trade-off frontiers where teams can see value, risk, and uncertainty at a glance. Landmen, geoscientists, engineers, and executives all work from the same interactive surfaces instead of brittle spreadsheets.