Company
An engineering team that takes machine learning models into industrial operation instead of stopping at a demo.
What we do
We work on problems where a model error costs money or safety: production sites, warehouses, transport hubs. That imposes requirements research projects do not have — predictable latency, reproducible results and a clear rollback procedure. We design around those constraints from day one instead of retrofitting a prototype for production at the end.
How the work is organised
Four stages, the same across all services.
- 1
Data reconnaissance
We look at what data already exists, what state it is in and whether the required quality is achievable at all. At this stage some tasks are honestly closed as unsolvable with the available data.
- 2
Pilot on real data
We collect a sample on site, train a baseline model and fix metrics on a holdout. The pilot takes four to six weeks and ends with numbers, not a presentation.
- 3
Integration
We embed the model into the client environment: data sources, storage, alerting, access control. This is also where the inference scheme is chosen for the available hardware.
- 4
Operation
We set up quality and drift monitoring, a retraining policy and a rollback procedure. Code, weights and documentation are handed to the client team.
Team
Machine learning engineers, data specialists and infrastructure developers. Most of the team came from industrial software development, so operational questions are discussed before model training starts rather than after.
Principles
- Metrics are fixed before the work starts and verified on client data
- Source code, model weights and documentation are handed over to the client
- Data does not leave the client perimeter without separate approval
- A negative pilot result is reported immediately rather than disguised
Next step
Have a data or video problem?
Describe it in your own words. We will tell you whether it is solvable with the data you have and propose a pilot format.