Use the right data science and engineering models to drive informed reliability decisions

Today, our data systems are heavily reliant on subject matter expertise. While these resources add significant value, they can be better leveraged in situations where their expertise is uniquely required. Additionally, when reliant on human expertise, our models can become too conservative, resulting in wasted time and spending. We have the opportunity to better leverage our data to learn from previous patterns and quickly scale our programs. With data-driven reliability, you have the ability to dynamically adjust your plans, quickly make decisions based on live data, and holistically approach complex problems.

We create holistic reliability models that bridge the gap between data science and human expertise.

Data-driven intelligence improves your facility’s reliability performance through a combination of engineering, data science, and hybrid reliability models. Removing the data silos from your reliability models will give you the clarity and confidence needed to make reliability decisions today.
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Featured Video and eBook

The Challenge with Our Current Risk Models
Listen to CEO, Ryan Sitton, and VP of R&D, Lynne Kaley, discuss how capturing risk accurately is key for a facility management is to understand their true risks as well as how to deploy limited resources most effectively.

Data-Driven Intelligence Solutions

Make impactful reliability decisions with Quantitative Reliability Optimization (QRO)

QRO combines elements of traditional reliability models and links every data point to the overall system. By implementing QRO, you can confidently forecast and quantify uncertainty to maximize your investments and sustain long-term reliability performance.
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Data Intelligence

Leverage the appropriate intelligence models to drive successful decisions and prevent loss of containment and function.

  • We drive the future of reliability modeling by combining traditional models with data science to create hybrid models such as Quantitative Reliability Optimization (QRO).

  • We incorporate data science models such as Bayesian modeling, spaces analysis, and anomaly detection when data is in abundance.

  • We leverage subject matter expertise and engineering models such as Risk Based Inspection (RBI), Reliability Centered Maintenance (RCM), and Root Cause Analysis (RCA) when data is limited.

People & Process

Build and manage your intelligence models with a proven process and an empowered workforce.

  • We ensure your work processes support your facility by integrating them into selected models and software.

  • We help you secure buy-in from facility and corporate stakeholders on your data and models through key data gate reviews, implementation and evergreening.

  • We empower your team to make intelligent decisions by ensuring they can properly use models post deployment through focused trainings.


Implement and properly manage technology to drive the right intelligence models.

  • We work with many of the recognized software packages in the industry and are able to leverage the unique value of each software.

  • We understand how to use these software packages to best fit your designated work processes.

  • We integrate the appropriate software package into your system to ensure all correct data is captured and organized.

How QRO's Holistic Approach to Reliability Helped a Refiner Forecast Availability with Limited Data

A refinery recently implemented RBI, RCM, and real-time monitoring, but remained uncertain that they were investing in the correct places. Learn how we helped this facility ensure its reliability investments were properly focused to maximize availability and minimize risk with QRO

Pinnacle Process to Getting Started

30 Minutes
10 Days
100 Days

Ready to see how data-driven reliability will impact your facility?