Tempo Labs

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

Agentic Temporal Intelligence for Industrial Operations

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Wind Turbine 7

"Why did output drop 15% this morning?"
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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?

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.

The agent

An execution layer. A reasoning engine. 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

Vertical reasoning models

Named, on-prem reasoning models built for industrial verticals — starting with Energy, then Chem. Where TEMPO orchestrates, RunnerTau understands natively, with asset knowledge built in.

In development

Deployment

Put it to work. Locally.

TEMPO runs on your infrastructure, on your data, under your control. Nothing leaves your site unless you decide it should. It sits 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.

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