Manufacturing
Investigate changing machine conditions alongside operating data and maintenance history.
Predictive maintenance / Hardware & machine learning
Toronto, CanadaPredictive maintenance.
Built around
your equipment.
VertoAeris combines telemetry hardware or existing sensor data with proprietary machine learning, tailored to your equipment and maintenance priorities.
Collaborated with
- Beacon Utility Contractors
- Malion New Materials Co., Ltd.
The operational problem
The reading is only
part of the story.
Machines produce valuable readings. Noise and changing operating conditions can make them difficult to interpret, especially when maintenance records sit elsewhere. Understanding what changed starts with connecting the data to how the equipment works.
What is the machine doing?Operating context
What is noise?Measurement context
What needs investigation?Meaningful change
Our capabilities
New hardware.
Existing data.
Collect the readings you need, or put existing sensor data to work. Both routes start with your equipment and maintenance priorities.
Your starting point
Start at the equipment. Build the data connection.
For equipment without the readings your team needs, we start with what to measure and how to collect it.
Our engineering focus
Telemetry units
We provide telemetry units to collect equipment readings and connect them with our monitoring and machine-learning software.
Discuss this workflowYour starting point
Your machinery may already hold useful signals.
Existing sensor readings can hold useful information that has yet to inform maintenance decisions.
Our engineering focus
Work with the data you have
We integrate available readings with our proprietary ML framework, informed by operating context and your team’s priorities.
Discuss this workflowYour starting point
Interpret the signal in the context of the machine.
Physical behaviour, operating conditions, and measurement noise all affect what a reading means.
Our engineering focus
Tailored to the application
We tailor anomaly-detection and predictive research to the equipment, with application-specific validation before performance claims.
Discuss this workflowData access, equipment compatibility, and scope are assessed for each application.
How we approach the work
Understand the machine.
Then interpret the signal.
We start with the equipment’s physics, operating conditions, and your maintenance priorities. That context shapes our proprietary ML framework and how we develop anomaly-detection and predictive methods for the application.
The value of equipment context
Our aim is to make noisy data more useful and reduce misleading alerts. Equipment context guides how we account for measurement noise and distinguish routine variation from changes worth investigating.
Predictive methods are a research direction. Detection and prediction performance require validation for each application.
Potential applications
Across industries.
Built for the application.
Explore where equipment monitoring and predictive-maintenance research could support your team.
Energy & utilities
Understand how changing loads and environments affect equipment readings.
Buildings & facilities
Connect readings from pumps, motors, and building-service equipment with maintenance follow-up.
Specialist equipment
Assess unusual operating conditions and sensor data that need equipment-specific interpretation.
Suitability depends on equipment context, available data, and application-specific validation.
The company
Built with an
engineering mindset.
VertoAeris is a predictive-maintenance company developing hardware, software, and equipment-specific machine learning to support better maintenance decisions.
About VertoAeris
Akhilesh Menon
Founder & CEO
Engineering Science · Aerospace Engineering
University of Toronto
Based in Toronto, Canada.
Let’s talk about predictive maintenance
Explore what your equipment
could tell you.
Tell us about your equipment, available data, and the maintenance decisions you want to improve.
admin@vertoaeris.dev