How it works
Sheet 03 · Rev A
The foundation first. Then the software.
Get the data out of every system, build the foundation under it, then put software on top that your own people run.
The whole chain
The whole chain, not one link of it.
We are engineers from the buildings we sell into. Both founders work every engagement.


Armaan Waels
Co-Founder · Forward-deployed engineering
- Payment infrastructure at a Y Combinator-backed fintech.
- Event-driven pipelines behind 100,000 transactions a day.
Georgia Tech · CS, Machine Learning


Aymane Arfaoui
Co-Founder · Data platform and ML
- Three years of ML infrastructure for aerospace manufacturing: millions of telemetry records a day, statistical process control and anomaly detection on live production lines.
- Patent pending on uncertainty-aware prediction.
Georgia Tech · CS, Machine Learning
Part 01
Ensure the data exists.
Exports from the ERP and logs straight from the machine. If a record only exists on paper, we digitize it. Wherever it lives, we go and get it.
Sources so far: ERP, MES, PMS, WMS, and EHR exports · machine logs · scanned paper · spreadsheets
- ERP exportCSV
- PLC / machine logTEXT
- Quality systemSQL
- Inspection formSCANNED PDF
- SpreadsheetXLSX ×5
- NotebookPHOTOGRAPHED
Ingest
Any format · anywhere it lives
Part 02
Build the foundation.
Pipelines and a data model built around your operation, down to your part numbers and your exceptions. Three stages, called medallion architecture.
Select a tier
Structured tables built around the questions you ask.
scrap_by_alloy_machine_shift · tool_change_windows · example
Running AutoML on scattered data is a Ferrari with no road.
Most companies sell the car. We build the road first.
Part 03
Software on top.
It runs on a machine inside your building. Offline, air-gap capable. Your people run it, no code and no data scientist, in four steps.
Upload · Profile · Train · Predict
It trains competing models and promotes the winner. The answer comes back ranked, with a confidence score, and every model traces to a hashed, re-runnable plan.
Build the road
Bronze centralizes everything as-is. Silver cleans and versions it. Gold holds the tables your questions run against.
Build the road
Bronze centralizes everything as-is. Silver cleans and versions it. Gold holds the tables your questions run against.
The run, end to end · example data
What you own
You own the pipelines.
You own the data model.
You own the structured data.
You own the models.
A consultant
leaves a report.
A data scientist
leaves in 18 months and takes the capability along.
Cogentic
leaves a working function that your own engineer re-runs.
Next
See it on your own data.
The exploratory phase runs one of your datasets through the pipeline and trains one model on your history. $12,500, 30 to 45 days.