Querri uses AI to help automate labor-intensive data cleaning and analysis processes, and makes optimizing maintenance strategies easier through predictive modeling.

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Implementing RCM with Querri

Implementing Reliability Centered Maintenance requires significant data collection, cleaning, and analysis. The collection process is often very manual, resulting in a messy data set.

Querri can:

  • ✓ Take this messy data, clean it up
  • ✓ Automate the process of creating an RCM framework
  • ✓ And develop PdM models
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Failure Mode and Effects Analysis (FMEA)

Querri's Natural Language Process and data processing capabilities can help to clean up human entered data such as closing comments and fill in missing data in intelligent ways.

This makes it easier to analyze an asset's past failures and potential failure modes, causes of those modes, and the component, asset, and plant-level impact of those failures.

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Optimizing Maintenance Strategies with Predictive Modeling

Querri can use its data exploration and visualization tools to uncover patterns and relationships between operating conditions, failure modes, and their effects.

It uses machine learning techniques to determine optimal scheduling for time-directed or condition-based maintenance activities.

Querri can also examine element failures and look for the most important causes, improving forecasting potential failures and their modes based on historical data.
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Why Querri?

Simple ways to do hard


Talk to your data through a chat interface and watch it transform in a spreadsheet view.

Reliable, repeatable data


Clean, merge, and analyze once. Then set up your data workflows to run on your schedule.

Designed for humans, not


It’s not a black box. See an explanation of the data workflows behind every Querri.

Get talking with your data now