Technology

Modeling, AI and workflows, built in-house.

We run state-of-the-art weather and climate models, downscaled to street scale and asset level on high-performance computing, then apply machine learning and build the tools and workflows that act on the result.

The Tempra stack

From physics to the decision.

Four layers, each one ours: physical modeling, machine learning, operational context, and the workflows that deliver it to the people who act.

Server rack with active network cables and indicator lights

Public forecasts are a starting point. Our own model runs resolve weather where it actually lands: along a street, across a site, at a single asset.

Each layer turns raw physics into something a team can act on.

Weather and climate modeling

State-of-the-art weather and climate models, dynamically downscaled to street scale and asset level on high-performance computing.

AI and machine learning

Models that correct forecast bias, learn from what actually happened, and convert a hazard into its expected impact on people and assets.

Operational context

Sites, assets, work types, exposure and thresholds: what decides whether a given hazard matters to your organization.

Products and workflows

Alerts, approvals, records, APIs and integrations that put model output into daily operations, as ready products or built around your process.

Research foundation

From hazard signal to place-based understanding.

The useful layer is not only the forecast itself. It is the translation of changing conditions into where impacts may emerge and what decisions need attention.

GPU-driven flood modeling can turn complex hazard information into a place-based view of where consequences may emerge.

A simulation of flood hazard information moving toward site-level consequence.

Example DPI Climate Hub research/simulation visual used to illustrate spatial hazard intelligence. Read the DPI story.

Positioning

What technical buyers can expect from Tempra.

Rigorous underneath. Plain on the surface.

Forecasts, observations and models do the heavy lifting in the background. What reaches your team is a recommendation it can explain, question and defend.

The forecast is the input. The operational decision is the product.

Bring a hazard and a decision. We’ll bring the models.

Next: Use Cases
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