Clean energy · Industrial IoT and agents

IoT connects the machine.Agents give its dataoperational intelligence.

We build the firmware, cloud and software layer that lets industrial energy equipment report, respond and be reasoned about, then put AI agents on top of it that can actually do something with what they see.

Talk about your equipment>See the agents

Sensor reads/Telemetry up/State recorded/Pattern spotted/Cause narrowed/Action proposed/Operator approves/Command down/Ack returned/Log written/

Sensor reads/Telemetry up/State recorded/Pattern spotted/Cause narrowed/Action proposed/Operator approves/Command down/Ack returned/Log written/

Great hardware is only half of an energy system.

A Canadian clean energy company came to us with a unit that turns industrial waste heat into electricity. The physics worked. What it did not have yet was a way to tell anyone what it was doing, take an instruction from a thousand miles away, or leave behind data an engineer could learn from.

We built that layer. Device to cloud messaging, live telemetry, remote commands with acknowledgement, structured logging and the production cloud underneath it. Then the interesting part: agents that read all of it.

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communication between the equipment and the platform

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of runs captured as structured, queryable field data

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actions taken without an operator approving them

Six agents sitting on top of the equipment, not beside it.

They watch the signals, explain what changed, and can reach the machine through the same controlled interfaces your engineers use.

Telemetry Agent

The equipment finally talks, and somebody is always listening.

Readings stream off the machine and land in one place at the rate the process actually moves, not whenever an engineer happens to plug in.

Command Agent

Instructions go down. Confirmations come back.

Every command carries an acknowledgement and a status, so nobody is standing in a plant guessing whether the unit heard it.

Anomaly Agent

It notices the drift before the shutdown does.

Signals get watched together instead of one gauge at a time, and the odd pattern gets raised while it is still a small problem.

Performance Agent

Output measured against what the unit should be doing today.

Conditions change hour to hour, so the baseline moves with them. A soft number gets flagged for what it is, not averaged away.

Maintenance Agent

A truck roll you can justify before you send it.

Cause gets narrowed from history and live state, so the crew arrives knowing which subsystem to open and what to bring.

Field Record Agent

Every run leaves a record you can actually analyze later.

Structured operating data, debug output and outcomes, kept in a form your engineers can query during development and long after.

What an agent actually does with a machine's data.

Observe

Telemetry and operating conditions get watched continuously, not sampled when someone remembers to check.

Understand

Abnormal patterns get correlated across signals, so a single strange reading is read in context.

Reason

Likely causes get narrowed and the next action gets proposed with the evidence attached.

Act

Approved commands go out through the same control layer your engineers already trust.

Learn

Historical runs and what actually fixed them sharpen the next recommendation.

From a bench unit to a connected, observable platform in weeks.

Weeks 1 to 2

Get the machine talking

Device to cloud messaging, telemetry ingest and command acknowledgement, wired to the hardware your team is already testing.

Weeks 3 to 5

Give engineering eyes

An operator view with live system state and debug controls, plus structured logging so field testing produces data worth keeping.

Week 6 onward

Put agents on top

Observation and diagnosis first, action second, always behind an approval your operators control.

What if your equipment could explain itself?

Connect the equipment.
Capture the run, not just the alarm.
Read the signals together.
Explain what is happening in plain language.
Propose the fix with the evidence attached.
And only act when an operator says go.

“We don't just build AI models. We build the infrastructure that lets AI touch real machines.”

Tell us what your equipment is doing today, and what you wish it could tell you.

Bring the hardware, the protocol mess and the questions your engineers keep answering by hand. We'll map the path from device to agent.

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