Industrial AI & Machine Learning Consulting
Machine learning that respects physics, built by engineers who have run the machines your data comes from.
Most industrial AI projects fail the same way: a model that looked great in a notebook meets a plant, a test cell, or a flight program, and quietly stops being used. The model was fit to data but blind to physics, so the first time conditions drift outside the training set, operators stop trusting it.
We build models the other way around. Start from the governing physics of the machine, use data to capture what the equations cannot, and keep every prediction explainable to the engineer who has to act on it. That approach comes from experience: our team has deployed analytics and automation at Blue Origin, Baker Hughes, Halliburton, and GE, on systems where a wrong prediction costs real money or worse.
The same team designs the test infrastructure and the combustion systems that generate the data, which means we know what the sensors are actually measuring, where they lie, and which features matter.
Capabilities
Physics-informed machine learning grounded in the governing equations of your system
Computer vision for automated inspection and defect detection
Predictive maintenance and reliability models built on real failure physics
Anomaly detection in high-frequency sensor and test data
Bayesian methods for decisions under sparse, expensive data
Digital twins for performance monitoring and emissions optimization
Test automation that closes the loop between experiment and model
Production-grade data pipelines and decision systems, not throwaway notebooks
Deployed, not demoed
Common questions
How are you different from a generic data science firm?
Our AI lead, Dr. Ammar Abdilghanie, holds a PhD in mechanical engineering and has deployed industrial AI at Blue Origin, Baker Hughes, and Halliburton. We understand the physics of the machines generating your data, so our models stay consistent with how the hardware actually behaves, and engineers can interrogate why a model made a prediction.
Our data is messy and scattered. Is that a problem?
It is normal. Most industrial datasets we inherit live in historians, CSV exports, and test logs with inconsistent naming. Building the pipeline that turns that into something trustworthy is usually the first third of the project, and we scope it that way from the start.
Do you hand over production systems or just studies?
Production systems. Several of our engagements have replaced legacy data acquisition and test execution tooling that operators now use daily. We deliver deployed software with documentation and training, and your team owns it.
What does a sensible first project look like?
Small and measurable: one machine, one failure mode, one test stand. A few weeks to a working prototype against your real data, then a decision point on whether to scale. We would rather earn the larger engagement than sell it up front.
Have data you suspect is worth more?
Bring one problem and a sample of the data. We will tell you what is achievable and what it would take.