NORTH RIDGE / SITE OVERVIEW

Every signal. In view.

↳ Local runtime

Power output1.84 MWIllustrative reading

Signals observed128Processed on-site

Active advisory01Inverter temperature

Generation / last 12 hoursRecorded

06:0012:0018:00

Advisory recordedRead-only · Signed audit↗

PRE-SEED · PILOT STAGE

AI that learns on-site.
Data that never leaves it.

DOM is edge AI hardware and software that runs inference and retrains models directly at your facility, so your operational data never has to leave the premises.

Talk to us

SCROLL TO FOLLOW THE SIGNAL

Claude Partner Network
AWS

backed and supported by

01 / THE PROBLEMHow it works ↘

More data.
Still missing the picture.

ILLUSTRATIVE SCENARIOS

EQUIPMENT SIGNAL

A moment that matters.

A brief deviation

EquipmentSite average

More telemetry. Less of the detail.

SITE ACOASTAL

Same equipment.

Salt air · variable wind

SITE BINLAND

Different reality.

Heat · seasonal dust

A shared model isn’t a shared reality.

Your facilityStill generating signals

Cloud modelConnection unavailable

Insight waiting on the network

Your operation can’t wait for a connection.

Scroll to explore

Intelligence needs to live closer to the operation.

02 / HOW IT WORKS

From plant signals.
To local insight.

01 / CONNECT

Read the signals
you already have.

Connect to existing equipment through read-only access. Raw operational data stays at your facility.

Your signals, captured locally.

02 / LEARN

Learn the way
your site runs.

Run inference and retrain locally. Evaluate each candidate against the current model before it can take over.

A learning loop grounded in your site.

03 / REVIEW

Give operators
the evidence.

Review advisories with their model version and recorded history. Your team decides what happens next.

Insight for people. Control stays with them.

LOCAL DATA · READ-ONLY ACCESS

INSIDE YOUR FACILITYILLUSTRATION

Equipment Sensors Site data

ON-SITE INTELLIGENCERAIN

NODE + RUNTIME

MODEL EVALUATIONCandidate → evaluate → promote

↗

Advisory ready for reviewModel version · recorded history

LOCAL

READ-ONLY INPUT · LOCAL STORAGE

Explore the hardware + software

03 / WHERE WE ARERAIN · PRE-SEED

An ambitious system.
A focused first step.

We’re building on-site intelligence for facilities where raw operational data needs to stay local.

Renewable energy is our starting point. A battery-storage simulator is the current foundation; a scoped field pilot is the next validation milestone.

RAIN is part of NVIDIA Inception, NVIDIA’s program for AI startups.

TODAY / SIMULATOR
A working local loop.
Telemetry capture, local inference, retraining, model evaluation and audit history implemented against a simulated battery plant.
NEXT / FIELD VALIDATION
Prove it at one site.
Validate hardware, signal quality, alert usefulness and operating stability with a design partner. No field deployment is claimed.
THEN / REPEATABILITY
Make the next site easier.
Turn pilot learning into a repeatable installation and support process. Extend to additional asset types as adapters are validated.

What we need to prove

Useful insights for operators. Reliable operation on-site. A repeatable deployment process. Commercial value established with customers.

Talk to us

04 / THE SYSTEMAT YOUR FACILITY

Hardware and software.
One local system.

The RAIN Node hosts the runtime and your view of the site. Your operators decide. Your control systems remain separate.

ON-SITE INFRASTRUCTURE

The local compute layer

Compute, storage and connectivity at your facility.

Conceptual architecture

WITHIN YOUR FACILITY

ConnectionRead-only input

ComputeRun & learn locally

StorageData stays on-site

HARDWARE CONFIGURATION TO BE DEFINED

RUNS ON THE NODELOCAL

RAIN Runtime

Local models. Evaluated updates. Recorded history.

Inference & learningON-SITE
Model evaluationGATED
Signed historyRECORDED

Local console

ILLUSTRATIVE

Review advisories, model versions and system health.

SITE SIGNAL

Advisory for reviewModel version + recorded history

READ-ONLY VIEWOperator decides ↗

Working against a simulated plant. Preparing for a first field pilot.

05 / USE CASESSTARTING WITH RENEWABLE ENERGY

Start with a real
operational question.

For asset owners, operators and maintenance teams. Start with one asset and agree what useful insight would look like.

SOLAR / PROPOSED APPLICATION

Which inverter is behaving differently?

Learn from inverter and tracker signals to surface changing behavior that can disappear inside aggregated performance reports.

Inverter telemetryTracker behavior

Explore this use case

06 / BUILT FOR OPERATIONAL TRUST

Local intelligence.
Clear operational boundaries.

Raw signals remain local.

The inference and learning loop uses local data. Raw telemetry is kept on-site; production uplink transport is not yet implemented.

Advice, with no actuation.

The system observes and advises. Operators retain authority over equipment, control systems and any action taken.

Decisions have a history.

A signed, linked history records inferences and model changes, including rejected updates. It is evidence to inspect, not a claim of certification.

07 / TALK TO US

Bring a real problem.
Help shape what comes next.

For operators exploring a pilot and investors evaluating the next stage.

FOR OPERATORS & DESIGN PARTNERSTalk to us

Define the asset, the question and what success should look like.

FOR INVESTORSTalk to us

Understand the product thesis, current stage and next milestones.

dhwanilm@rainlab.ai · Operators and investors alike.

Every signal. In view.

AI that learns on-site.
Data that never leaves it.

backed and supported by

More data.
Still missing the picture.

A moment that matters.

Same equipment.

Different reality.

From plant signals.
To local insight.

Read the signals
you already have.

Learn the way
your site runs.

Give operators
the evidence.

An ambitious system.
A focused first step.

A working local loop.

Prove it at one site.

Make the next site easier.

What we need to prove

Hardware and software.
One local system.

The local compute layer

RAIN Runtime

Local console

Your systems.
One intelligence.

Start with a real
operational question.

Which inverter is behaving differently?

What changed in this turbine’s pattern?

Why is this module running warmer?

Which air handler is working harder?

Local intelligence.
Clear operational boundaries.

Raw signals remain local.

Advice, with no actuation.

Decisions have a history.

Before we
step on-site.

Bring a real problem.
Help shape what comes next.

Your pilot brief.

Every signal. In view.

AI that learns on-site.Data that never leaves it.

backed and supported by

More data.Still missing the picture.

A moment that matters.

Same equipment.

Different reality.

From plant signals.To local insight.

Read the signals you already have.

Learn the way your site runs.

Give operators the evidence.

An ambitious system.A focused first step.

A working local loop.

Prove it at one site.

Make the next site easier.

What we need to prove

Hardware and software.One local system.

The local compute layer

RAIN Runtime

Local console

Your systems.One intelligence.

01 Connect your systems

02 Build site context

03 Run local intelligence

04 Explore the evidence

Start with a realoperational question.

Which inverter is behaving differently?

What changed in this turbine’s pattern?

Why is this module running warmer?

Which air handler is working harder?

Local intelligence.Clear operational boundaries.

Raw signals remain local.

Advice, with no actuation.

Decisions have a history.

Before we step on-site.

Bring a real problem.Help shape what comes next.

AI that learns on-site.
Data that never leaves it.

More data.
Still missing the picture.

From plant signals.
To local insight.

Read the signals
you already have.

Learn the way
your site runs.

Give operators
the evidence.

An ambitious system.
A focused first step.

Hardware and software.
One local system.

Your systems.
One intelligence.

Start with a real
operational question.

Local intelligence.
Clear operational boundaries.

Before we
step on-site.

Bring a real problem.
Help shape what comes next.