We apply machine learning where it removes a real bottleneck: classification, retrieval, forecasting, and assisted workflows. The work includes the unglamorous parts — data quality, evaluation, and a path for a person to override the model.
We do not recommend a model until the baseline process is understood and the cost of a wrong answer is named.
Outcomes
- A measured improvement on a defined task
- Auditability for sensitive decisions
- An operating plan, not a demo
Process
How an engagement runs
01
Baseline
Measure how the work is done today and what a useful improvement would be.
02
Prove
Evaluate on your data before committing to a production design.
03
Operate
Monitor drift, cost, and the cases people still have to catch.