Predictive Maintenance

Listen to the condition of your assets. Turn machine signals into maintenance decisions your teams can understand, prioritise and act on.

Explore the solution Discuss your challenge

APPLIED INTELLIGENCEBIN / PM

Designed around your operation

01 / THE OPPORTUNITY

A clearer view of what your equipment is telling you

Intelligence starts with context

Combine vibration, temperature, operating history and production context for a more complete view of asset health
Interpret signal changes in context, so normal shifts in load, speed or environment are not mistaken for faults
Connect live sensor data with maintenance records and asset history
Distinguish genuine deterioration from the normal rhythm of production
Reduce false alarms and unnecessary maintenance activity
Help maintenance teams focus on the changes most likely to require attention

02 / WHAT BECOMES POSSIBLE

Purpose-built capabilities

Sense the change

Establish operating baselines and detect condition changes across different loads and production states.

Understand the risk

Combine anomaly evidence with asset criticality and maintenance history to support investigation.

Plan the response

Route reviewable findings into maintenance workflows with supporting signals and a clear next action.

03 / FROM SIGNAL TO ACTION

A connected working cycle

Instrument

Review available sensors, sampling and asset context.

Baseline

Learn representative operating states and known failure patterns.

Assess

Evaluate changes with maintenance specialists and documented thresholds.

Act

Connect reviewed alerts to inspections, planning and feedback.

04 / DESIGNED FOR YOUR OPERATION

The foundation for a useful system

DATA & CONTEXT

What we start with

Time-aligned condition signals and operating context
Asset hierarchy, criticality and maintenance records
Known events and access to maintenance specialists

DEPLOYMENT & INTEGRATION

How it connects

Edge or central inference based on latency and connectivity
Historian, monitoring and work-order integrations
Human review and escalation before critical maintenance action

05 / EVIDENCE BEFORE EXPANSION

Useful alerts earn operational trust

VALIDATION CRITERIA

Agree what good looks like

Alert precision and false alarms per asset over time
Warning lead time for documented events where data permits
Coverage across operating states, loads and changing conditions
Consistency of predictions across similar assets and equipment classes
Ability to distinguish genuine deterioration from normal operational variation
Sufficient confidence and supporting evidence for maintenance teams to review an alert

06 / YOUR FIRST STEP

Start focused, learn deliberately

A SHARED PLAN, WITH CLEAR DECISION POINTS

A practical pilot

Select an asset family with usable signals and documented history
Review historical events and run the model in shadow mode
Agree alert ownership, escalation rules and the maintenance feedback loop

A CONTROLLED START, WITH MEASURABLE OUTCOMES

What the pilot delivers

A clear view of current asset behaviour, historical failure patterns and data quality
Alerts assessed against real operating conditions before they influence maintenance decisions
Clear ownership, review responsibilities, escalation paths and response expectations

THE TECHNOLOGY FOUNDATION

Built with the right tools

A stack shaped around your data, operating environment and delivery requirements.

PyTorch

ONNX

InfluxDB

MQTT

A CLOSER LOOK

From possibility to everyday value

Perspectives on bringing predictive maintenance into your operation.

01 / The context observatory

Understand your setting

Connect people, data and constraints around a clearly defined operational problem.

02 / The decision instrument

Make the decision tangible

Define the evidence, review criteria and actions that make the intelligence useful.

03 / The operational relay

Keep the system useful

Plan deployment, ownership and feedback so the solution evolves with your operation.

Intelligence that works

Start a conversation

BINARY SOLUTIONS

Intelligence that works

Preparing your next perspective

BINARY SOLUTIONS / SOLUTIONS

Predictive Maintenance

Listen to the condition of your assets. Turn machine signals into maintenance decisions your teams can understand, prioritise and act on.

Explore the solution Discuss your challenge

APPLIED INTELLIGENCEBIN / PM

Designed around your operation

01 / THE OPPORTUNITY

A clearer view of what your equipment is telling you

Intelligence starts with context

Combine vibration, temperature, operating history and production context for a more complete view of asset health
Interpret signal changes in context, so normal shifts in load, speed or environment are not mistaken for faults
Connect live sensor data with maintenance records and asset history
Distinguish genuine deterioration from the normal rhythm of production
Reduce false alarms and unnecessary maintenance activity
Help maintenance teams focus on the changes most likely to require attention

02 / WHAT BECOMES POSSIBLE

Purpose-built capabilities

Sense the change

Establish operating baselines and detect condition changes across different loads and production states.

Understand the risk

Combine anomaly evidence with asset criticality and maintenance history to support investigation.

Plan the response

Route reviewable findings into maintenance workflows with supporting signals and a clear next action.

03 / FROM SIGNAL TO ACTION

A connected working cycle

Instrument

Review available sensors, sampling and asset context.

Baseline

Learn representative operating states and known failure patterns.

Assess

Evaluate changes with maintenance specialists and documented thresholds.

Act

Connect reviewed alerts to inspections, planning and feedback.

04 / DESIGNED FOR YOUR OPERATION

The foundation for a useful system

DATA & CONTEXT

What we start with

Time-aligned condition signals and operating context
Asset hierarchy, criticality and maintenance records
Known events and access to maintenance specialists

DEPLOYMENT & INTEGRATION

How it connects

Edge or central inference based on latency and connectivity
Historian, monitoring and work-order integrations
Human review and escalation before critical maintenance action

05 / EVIDENCE BEFORE EXPANSION

Useful alerts earn operational trust

VALIDATION CRITERIA

Agree what good looks like

Alert precision and false alarms per asset over time
Warning lead time for documented events where data permits
Coverage across operating states, loads and changing conditions
Consistency of predictions across similar assets and equipment classes
Ability to distinguish genuine deterioration from normal operational variation
Sufficient confidence and supporting evidence for maintenance teams to review an alert

06 / YOUR FIRST STEP

Start focused, learn deliberately

A SHARED PLAN, WITH CLEAR DECISION POINTS

A practical pilot

Select an asset family with usable signals and documented history
Review historical events and run the model in shadow mode
Agree alert ownership, escalation rules and the maintenance feedback loop

A CONTROLLED START, WITH MEASURABLE OUTCOMES

What the pilot delivers

A clear view of current asset behaviour, historical failure patterns and data quality
Alerts assessed against real operating conditions before they influence maintenance decisions
Clear ownership, review responsibilities, escalation paths and response expectations

THE TECHNOLOGY FOUNDATION

Built with the right tools

A stack shaped around your data, operating environment and delivery requirements.

PyTorch

ONNX

InfluxDB

MQTT

A CLOSER LOOK

From possibility to everyday value

Perspectives on bringing predictive maintenance into your operation.

01 / The context observatory

Understand your setting

Connect people, data and constraints around a clearly defined operational problem.

02 / The decision instrument

Make the decision tangible

Define the evidence, review criteria and actions that make the intelligence useful.

03 / The operational relay

Keep the system useful

Plan deployment, ownership and feedback so the solution evolves with your operation.

Intelligence that works

Start a conversation

Predictive Maintenance

A clearer view of what your equipment is telling you

Intelligence starts with context

Purpose-built capabilities

Sense the change

Understand the risk

Plan the response

A connected working cycle

Instrument

Baseline

Assess

Act

The foundation for a useful system

What we start with

How it connects

Useful alerts earn operational trust

Agree what good looks like

Start focused, learn deliberately

A practical pilot

What the pilot delivers

Built with the right tools

From possibility to everyday value

Understand your setting

Make the decision tangible

Keep the system useful

Ready to putAI to work?

Predictive Maintenance

A clearer view of what your equipment is telling you

Intelligence starts with context

Purpose-built capabilities

Sense the change

Understand the risk

Plan the response

A connected working cycle

Instrument

Baseline

Assess

Act

The foundation for a useful system

What we start with

How it connects

Useful alerts earn operational trust

Agree what good looks like

Start focused, learn deliberately

A practical pilot

What the pilot delivers

Built with the right tools

From possibility to everyday value

Understand your setting

Make the decision tangible

Keep the system useful

Ready to putAI to work?

Ready to put
AI to work?

Ready to put
AI to work?