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AI-Fit

Optimizing Drilling with AI-Enhanced Motor Inspection Reports

We collaborated with a startup to design and implement AI-enhanced motor inspection reports, significantly improving accuracy and consistency in the drilling process.

The Challenge

Motor inspection reports traditionally rely on theoretical ‘best case’ scenarios, often disregarding actual drilling conditions. This discrepancy leads to inaccurate and unreliable reports, which can hinder the decision-making process and affect the overall efficiency of drilling operations. Moreover, the current process involves a significant amount of manual data entry, prone to human error and inconsistencies. The challenge was to create a solution that could integrate real drilling conditions into performance reports, reduce manual input errors, and enhance the overall accuracy and reliability of the data. This required a comprehensive understanding of the existing workflow, the pain points associated with current practices, and the potential for leveraging AI to streamline and improve the reporting process.

The Challenge

Motor inspection reports traditionally rely on theoretical ‘best case’ scenarios, often disregarding actual drilling conditions. This discrepancy leads to inaccurate and unreliable reports, which can hinder the decision-making process and affect the overall efficiency of drilling operations. Moreover, the current process involves a significant amount of manual data entry, prone to human error and inconsistencies. The challenge was to create a solution that could integrate real drilling conditions into performance reports, reduce manual input errors, and enhance the overall accuracy and reliability of the data. This required a comprehensive understanding of the existing workflow, the pain points associated with current practices, and the potential for leveraging AI to streamline and improve the reporting process.

Project breakdown

Team

Product Manager & Facilitator UX/UI Designer Back-End expert Front-End expert ML Expert

Duration

8 weeks for MVP 12 weeks refining

Delivery

Style Guide, UI Mockups, Progressive Web App

Tech Stack

Ionic, React, Recharts, Sass, Cognito, S3, CloudFront, Aurora, AWS, Document DB

Project breakdown

Team

Product Manager & Facilitator UX/UI Designer Back-End expert Front-End expert ML Expert

Delivery

Style Guide, UI Mockups, Progressive Web App

Duration

8 weeks for MVP 12 weeks refining

Tech Stack

Ionic, Capacitor, React, Workbox, PWA NestJS, TypeScript, Firebase

Understanding Motor Inspection and Reports

Through interviews with SMEs, desk research, and a thorough documentation process, our product team began understanding the problem. We studied drilling motor inspections, current workflows, documentation processes, and identified main pain points and needs.

To lay a solid groundwork for the project, we also examined selected topics from drilling, physics, math, hydraulic motors and laser measurements that were relevant for the ML model.

ML pipelines documentation

The team already had an ML model, but it lacked documentation and failed to consider relevant input details, impacting the recommendations. We meticulously reviewed the datasets and ML workflows, documented the selected features, and integrated new pipelines to include feedback from the app as model input. This process also refined the model by removing unnecessary features and optimizing workflows.

When we work with an ML-Based product, we have special Discovery processes that combine expertise from Design and AI to produce much more user-centered AI Products.

Check how our AI Design Sprint helps to organize this

Check how our AI Design Sprint helps to organize this

Enhancing manual measurements with data coming from laser-measurements

Predominantly, inspection activities involve manual measurements, with only 5% using laser equipment that offers significantly improved accuracy.

Through and ancillary ML Model we helped to define and implement, we enhanced the manual measurements with additional data from the laser measurements datasets we had (over ten years of science-backed field data on 10,000+ motor runs with 21 million data points).

This contributed to increase accuracy for the 95% of cases in which the measurements are manually taken.

Adding Visual clarity

We designed custom icons representing each of the possible rotor—stator combinations.

This was a proposal from our team, as we detected it could make each report easier to recognize at a glance. It also helped through the whole report generation process, making the input screens clearer for inspectors.


Input reliability improved through an enhanced UX

Data from the inspection needs to be collected with precision, as it's the main input the ML Model will use to generate its recommendations.

We went through this screen several times, refining it and getting to a proposal that was intuitive for inspectors and easy to fill up.

A shared library of UI components

AI-Fit is part of a suite of products, and we find an opportunity to keep consistency and reduce errors by making a shareable Library of UI Components.

We used Storybook to isolate and document each shared UI component, then included the library on both projects. This helped the products to be consistent, adding intuitiveness for users having access to both and reducing UI mantainability.

Summary

Summary

Summary

How we helped

How we helped

How we helped

Our ML experts and backend engineers made a significant contribution. Their initial mission was to deploy an existing ML model, but we went beyond that by documenting the pipelines related to the model and implementing necessary changes to ensure the model produced accurate recommendations based on different types of user input.

Our product team streamlined the process for motor inspectors, adding guiding details to help them input more reliable data. Visual clarity and iterative design were key to developing an effective UI.

The frontend team created a high-performance UI, integrating existing chart libraries with custom components to deliver the necessary user experience.

The outcome

The outcome

The outcome

AI-Fit is a living product that is making its way into the market. You can take a look at the live project by visiting its landing page.