AI strategy & machine learning
We identify where a model will change a real decision, then build it on data you can defend. The work includes the evaluation, the limits, and the handover.
When a board or a director is about to fund a model and needs a decision, not a demonstration.
What the work covers
- Use-case selection against cost, risk and data quality
- Predictive, classification and forecasting models
- Evaluation your team can repeat
- Documentation written for operators, not only data scientists
What you leave with
- A short list of uses worth funding, and the ones to refuse
- An evaluation your team can run again
- Operating notes written for the people who stay