What we solve
For manufacturing, logistics, retail, and other projects with distinctive images, signals, or datasets. Before promising a custom model, we check whether enough data exists and whether the cost is justified compared with an existing solution.
What you get
The first stage produces a validated problem definition, a prepared dataset, and a prototype with a measured quality metric. If the hypothesis holds, we plan a production implementation.
What is included
- Analysis of the task, data, and quality criteria
- Selection of a specialist ML team
- Preparation and labeling of a reference dataset
- A model prototype with reproducible measurements
- A plan for integration, monitoring, and retraining
How we work
- Review the problem and available alternatives.
- Agree roles and responsibilities with the specialist team.
- Run an experiment on a limited dataset.
- Decide whether a production system is justified.
Optional additions
- A pipeline for collecting new data
- Integration with a web app or production system
- Monitoring for quality drift
- Documentation for the internal team
Important considerations
The team is selected after the initial review. Model quality cannot be guaranteed before examining the data; target metrics, data rights, and each party’s responsibilities are agreed before the experiment.