Machine Learning
Find useful signals in the data you already create. SAI can help teams turn historical events and live data into forecasts, risk scores or recommendations that fit the way work gets done.
The business problem
A model can score thousands of records quickly, but a high score alone is not a business result. Teams need representative data, a fair comparison with existing practice, a way to act on the prediction and controls when the model becomes less reliable.
How SAI Technologies approaches the work
Frame the decision: choose what to predict, how far ahead and what action a person or system will take.
Inspect the data for missing records, leakage, imbalance, seasonal effects and fields that should not be used.
Build a repeatable training and evaluation pipeline. Compare the model with a simple baseline and test performance on later periods or new groups.
Expose the prediction through an API, dashboard or workflow. Show uncertainty and allow overrides when appropriate.
Monitor production data and outcomes for drift, error and cost. Define retraining and rollback conditions with the client.
Connected process
Illustrative delivery story
A distribution business needs to predict which shipments may miss an important delivery window. SAI could combine route history, scan events and operating conditions to flag exceptions early, then deliver an alert to the team that can intervene.
Measures to agree before work begins
Forecast error against the current method; useful alert rate; false alerts; time available to intervene; missed delivery rate; labor hours spent on manual review.
