Turning images and videos of your assets into insights for predictive maintenance
Predictive maintenance is a data-driven strategy to optimize and accelerate large-scale production, with skyrocketing adoption across industries like oil & gas, utilities, energy, telecom, etc.
A McKinsey report suggests that predictive asset maintenance can help reduce asset and machine downtime by as much as 50% and increase the life of assets by almost 40%.
iMerit excels at image and video annotation to help you assess the future state of your assets and the scale of maintenance work to support your predictive maintenance strategy.
With high-quality training datasets curated by expert annotators from our team, we help you improve the efficiency and accuracy of predictive models to reduce the risk of asset failure.
iMerit has 10+ years of experience working with businesses across numerous industries on their predictive maintenance models. We leverage industry best practices and best-in-class tools to help curate training datasets at scale. Our team will
Projects completed ahead of time
Efficiency gain throughout project
Accuracy achieved across data types
Enel Group Partners with iMerit to Analyze their Electrical Distribution Network
With 1.5 million miles of electrical distribution network, Enel Group, a leading European manufacturer and distributor of electricity and gas, was looking to automate manual analysis of the network, as it was error-prone and cumbersome.
The company is working with iMerit to set up and scale data annotation and labeling across multiple data types and scenarios.
Listen to Mario Larcher, Head of Computer Vision at Enel Group, speak about how we helped accelerate their deep learning initiatives.
We can help enhance and maintain your Digital Twin to optimize your assets for better safety of your people on the job and a longer life for your machines.
Our team can capture and detect 2D corrosion, fractures, cracks, and oil/ water presence at the pixel level across multiple degradation labels. We help our clients predict potential failures by:
Identify and classify worker safety infractions for actionable intervention and prevent future incidents. With video tracking and interpolation of workers and dangerous scenarios, we can