AI and Automation
Models that sharpen decisions you already make
Machine learning for problems buried in more data than any team can review by hand, built into your operation instead of sitting beside it.
When the answer is in the data
Some problems cannot be solved with a rule, because nobody can write the rule down. Which sound means a pump is about to fail? Which images need a person to look at them? Which pattern in last year's orders predicts next quarter's? When the answer lives in a large amount of data, the task repeats, and a person doing it by hand is the bottleneck, machine learning earns its keep.
Oilfield equipment monitoring used to mean a technician checking each site every 24 hours. For WellChecked, we built a system that listens to the equipment instead, using sound and deep learning to flag potential problems in seconds, and getting more accurate as it learns which sounds signal which failures.
Models are software, not magic
A model that ships once and is never retrained gets worse as the world it learned from changes. We treat models as software that needs maintaining: we build the data pipeline that feeds them, measure them against real outcomes, and set up the path to retrain and improve them as the work evolves.
When a problem is not actually a machine learning problem, we say so. If the rules are simple and fixed, a plain script is cheaper and more reliable.
How the work runs
1Define the decision
We start from the business outcome and confirm machine learning is the right tool before anything is built.
2Prepare the data
We get the data into a usable, validated shape, and tell you early whether it can support the model you want.
3Train and test
We build models for the specific task and measure them against real outcomes, not against a demo.
4Integrate
Models are wired into the applications and workflows your people already use.
5Monitor and retrain
We track performance in production and build the pipeline to improve models as the data changes.
What you leave with
A use-case assessment tied to a business decision
A validated training dataset and the pipeline that maintains it
Trained models measured against real outcomes
Integration into your applications and workflow
Monitoring and a retraining plan
Honest answers
How do we know if our problem is a machine learning problem?
If the answer lives in a large amount of data, the task repeats, and a person doing it by hand is the bottleneck, it might be. If the rules are simple and fixed, a plain script is cheaper and more reliable. We will tell you which one you have before you spend on the harder path.
Do we need a giant, perfectly labeled dataset first?
Not always, but data quality matters more than data size. Part of our job is getting your data into a usable, validated shape, and being honest early about whether what you have can support the model you want.
How do you know a model is actually working?
We measure it against what really happened, not against impressions. Where we can, we test a model on historical data it has never seen and score its predictions against the known outcome before anyone relies on it.
Put your data to work
Tell us about the decision that takes too long or the review nobody has time for.