AI Development
Use business data to build forecasts, recommendations and classification tools with clear evaluation criteria. We assess whether the available information supports the decision or process you want to improve.
A predictive model is useful when it can be evaluated against a relevant baseline and used in an operational decision. We examine historical data, missing values, timing and the outcome being predicted before selecting a machine-learning or deep-learning approach.
Review coverage, quality, permissions and whether the target outcome can be measured.
Develop forecasting, classification or recommendation approaches suited to the defined task.
Separate development and evaluation data and compare performance with an agreed baseline.
Deliver results through dashboards, APIs or application workflows and plan monitoring after launch.
Possible applications include demand forecasting, operational prioritization and relevant product recommendations. The project should explain how the output will be used, who will review it and what happens when confidence or data quality is insufficient.
Provide representative historical records and a definition of the business outcome. We assess the amount and consistency of data before confirming feasibility, an evaluation method and a delivery estimate.
There is no single minimum for every task. The required coverage depends on the prediction, frequency, variation and quality of the available records.
An appropriate target is agreed after assessing the data and baseline. Results must be measured on representative evaluation data rather than promised before discovery.
An update and monitoring process can be included in scope, with defined criteria for reviewing data changes and model performance.
Tell us about the workflow and outcome you want to improve so we can define a practical next step and a clear project scope.
