Tempo Labs

Reasoning over telemetry,
grounded in your asset's
own schemas and docs.

Time-series reasoning for industrial operations — built on RunnerTau, our family of vertical reasoning models, and TEMPO, the agent that puts them to work.

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Wind Turbine 7

"Why did output drop 15% this morning?"
Request a pilot → Not sure you're ready? Check your reasoning readiness →
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The gap

Your AI tools don't know what your assets can do.

Most tools reading your sensor data don't know anything about the asset producing it. They'll explain a vibration spike or extrapolate a forecast without ever checking whether the answer is physically possible for that specific machine.

We start from your asset's own documentation — power curves, fault tables, system topology — so every explanation and forecast is checked against what the asset can actually do. The question is always: how do you know you can trust this output?

Not there yet?

Most data estates aren't reasoning-ready — telemetry, documentation, and asset context usually live in separate systems. Start with a Reasoning Readiness Assessment and find out exactly what's missing.

Check your readiness →

How it works

From a question to a grounded answer — placed back on your asset or process schema.

01
Reads your asset's structure Power curves, fault tables, P&IDs, topology — ingested and mapped to your live telemetry channels.
02
Reasons over both together Ask a question. TEMPO selects which signals matter, which analytical tools apply, and how your asset's constraints bear on the answer.
03
Finds it and shows you where Root-cause findings are placed directly on your asset's own schema — a clogging filter, a degrading bearing — not buried in a log.
04
Checks before it tells you Forecasts and explanations are verified against real operating limits before they reach you.
05
Stays reliable when sensors fail When a sensor degrades, TEMPO falls back to related signals it understands — using the same asset structure to choose the right substitute.

Finding on asset schema

Heat pump schema with clogged filter finding System schematic showing a heat pump with outdoor unit, expansion valve, indoor unit, return line, circulation pump, and a water filter. The filter is highlighted in amber as the finding location, with a callout explaining the flow ratio has dropped 34% versus baseline, consistent with progressive clogging. HEAT PUMP — UNIT 3 Outdoor unit Expansion valve Indoor unit RETURN LINE Circ. pump Filter ⚠ clogging Finding: water filter Flow ratio −34% vs baseline. Consistent with progressive clogging.

Offerings

Where we bring reasoning to your operation.

Reasoning readiness isn't limited to time-series. The same discipline — grounding an AI system in the structure it's reasoning about, then verifying every output against it — applies to your codebase, your AI architecture, and how you run inference. These are the ways we work with you today.

Offering

AI Coding Adoption

From vibe coding to agentic engineering. We help you understand where you are and what it takes to reason over your codebase with precision.

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Offering

AI Architecture

Deep expertise designing AI systems that reason over your data with precision, at scale, and with a full audit trail.

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Offering

Private Inference

The architecture and infrastructure to put AI to work locally, at a reasonable cost, fully under your control.

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Offering

Reasoning Readiness

Is your operational data reasoning-ready? Our original assessment — for time-series, industrial data estates.

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The agent & the models

An execution layer. A family of reasoning models. One ask.

TEMPO

Industrial reasoning agent

TEMPO reads your asset's documentation, selects the right analytical tools, and reasons over telemetry and asset constraints together. Ask a question — get an answer grounded in what your asset can actually do.

In development

RunnerTau

Time-series reasoning models (TSRM)

Named, vertical reasoning models trained to reason over time-series — not just forecast it. Today they understand energy problems, including battery systems, down to the level of a domain expert.

RunnerTau 7B AVAILABLE
RunnerTau 4B AVAILABLE
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Industries

Starting in energy. Expanding by design.

RunnerTau's reasoning starts wherever the time-series data is richest and the failure modes are best understood. Energy — and battery systems specifically — is where we're proven today. Data centres and automotive are next.

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Energy LIVE

Battery systems, grid assets, and generation — RunnerTau reasons over charge, dispatch, and degradation today.

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Data centres NEXT

Power draw, cooling, and capacity planning for compute infrastructure — expanding RunnerTau's domain next.

Explore data centres →
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Automotive NEXT

Powertrain, battery, and fleet telemetry — reasoning that travels from stationary energy assets to moving ones.

Explore automotive →

Deployment

Put it to work. Locally.

TEMPO and RunnerTau run on your infrastructure, on your data, under your control. Nothing leaves your site unless you decide it should. They sit alongside the systems you already run — your historian, your SCADA, your digital twin — adding a reasoning layer rather than asking you to replace what's working.

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Data sovereignty

Your telemetry stays on-site. No cloud dependency, no third-party data exposure, no compliance risk.

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Works with your stack

Connects to your historian, SCADA, and digital twin investments. Adds reasoning on top — not a replacement.

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No lock-in

No dependency on a single model or vendor. Swap components as the landscape evolves.

Working on industrial time-series problems?

Design partner, early customer, or investor — we'd like to talk.

We respond to every message.