AI Data Audit — Quality, Completeness & ML Readiness Assessment

I will audit your dataset and deliver a detailed AI data readiness report covering completeness, consistency, accuracy, duplicate rates, class distribution, feature correlations, and an honest assessment of whether your data is ready for machine learning or AI model training — and if not, exactly what needs to change before it will be. Organisations routinely underestimate data quality problems until they are weeks into a model training project and wondering why their accuracy is poor; a data audit before the build phase prevents expensive rework.

The report covers field-level completeness analysis, data type validation, outlier detection, duplicate record identification, class imbalance assessment for classification tasks, correlation heatmap, and a data readiness score per ML task type (classification, regression, NLP, computer vision). Each finding is accompanied by a specific remediation recommendation and effort estimate.

This service is ideal for data teams, data scientists, and business analysts preparing a dataset for an AI project, evaluating inherited data from a legacy system, or responding to poor model performance that may be rooted in data quality issues.