Engineering
I build the path data takes from the machine it comes off to the decision someone makes with it.
Sense
The data exists, but it is trapped in the machine.
What I build
- Sensor selection and placement for the measurement you actually need
- Signal conditioning and acquisition on industrial hardware
- Calibration against a known reference, so the numbers mean something
Control & instrumentation
Move
It is on the machine, in a vendor portal, and in a PDF emailed every Monday.
What I build
- Telemetry paths off the plant floor
- Vendor export ingestion, scheduled and monitored rather than remembered
- LLM-assisted extraction from the documents nobody wants to retype
Industrial data acquisition — edge to cloud
Shape
Twelve sources, twelve schemas, no shared key, and nobody trusts the total.
What I build
- Normalisation into one queryable model
- Validation and reconciliation, with the mismatches surfaced instead of dropped
- Provenance you can audit back to the source row
Data quality, sensor fusion
Decide
Someone still opens four dashboards and makes the call from memory.
What I build
- Scheduled reporting that lands before the meeting, not after
- Thresholds and alerting on the numbers that actually move
- Monitoring surfaces and digital-twin views of the running system
Digital twins, Industry 4.0, closed-loop control
Then the decision goes back to the machine
It changes what the instrument reads, which changes the next decision. Studying that loop is the PhD.
Stack
- Sense
- Sensors & DAQ
- LabVIEW
- Keil uVision
- Altium
- Move
- Python
- C++
- REST APIs
- LLM orchestration
- Shape
- Pandas
- NumPy
- SQL
- PostgreSQL
- Decide
- MATLAB & Simulink
- ETAP
- Matplotlib
- Scheduled jobs
Case studies
Working with me
Engagements start at any stage. Most start at Shape, because that is where the pain is loudest.
A first piece of work is usually two to four weeks, scoped to one measurable outcome we agree on before it starts.