Align
AI-Fit™ was created to modernize motor inspection reporting, helping drilling companies make better decisions based on data that reflects real drilling conditions.
While the system already had a functioning ML model, missing documentation and inconsistent input data were limiting its reliability.
We began with an in-depth discovery workshop, bringing together AI, product, and design teams to understand how inspectors worked in the field and how inspection data directly influenced model outputs.
Prioritize
Through discovery, we identified two critical challenges:
Model reliability: inspection data lacked structure, documentation, and consistent inputs.
Inspector experience: the reporting interface made it easy to introduce errors and hard to understand results at a glance.
Improving accuracy required addressing both the AI pipeline and the human workflows feeding it.
95%
reliable inspection recommendations
through refined ML models and richer inputs
Consistency across inspections
by standardizing data capture and reporting
Prove
Our ML experts refined and optimized the existing model by reviewing datasets, documenting selected features, and improving ML workflows.
New pipelines were introduced to incorporate feedback from the inspection app as model input.
At the same time, our product and design teams reworked the inspection flow. Guiding details were added to help inspectors enter more reliable data, and the UI was redesigned for clarity and speed.
The result was a refined ML model that combines manual measurements with data backed by over a decade of scientific field research — delivering accurate recommendations in 95% of cases.
Integrate
We integrated these improvements into a working inspection platform used in real drilling contexts.
A high-performance UI was built by combining existing chart libraries with custom components, making inspection reports easier to read and interpret.
To further reduce errors, the design team introduced custom icons representing each possible rotor-stator combination, allowing inspectors to quickly recognize configurations and validate inputs.
Inspector efficiency
by standardizing data capture and reporting
Scale
AI-Fit™ is part of a broader product suite. To support scale and long-term consistency, Arionkoder created a shared UI component library using Storybook.
This reduced UI maintenance, ensured consistency across products, and made it easier to evolve the platform over time.
With a clearer data foundation, a stronger model, and a scalable UI system, AI-Fit™ is positioned to continue improving inspection accuracy as usage grows.
