AI Validation Engine – Intelligent QC

The AI Validation Engine is a framework for checking data quality in environments where simple rules are not enough. It combines traditional rule-based validation with AI reasoning to catch issues that would otherwise slip through — particularly the subtle, relational inconsistencies that rigid checks miss.

Traditional validation checks whether a field is present or within a range. But complex engineering and operational data has consistency requirements that span multiple fields, tables, and relationships. A value can be individually valid but contextually wrong. These kinds of errors are hard to encode as simple rules, invisible to basic QA tools, and expensive to find during downstream reviews.

The engine applies validation in layers: mandatory field checks and range validations run first as a fast baseline. Then relational checks identify conflicting values across related records and logical inconsistencies in attribute combinations. Finally, AI-based reasoning flags outliers, anomalies, and patterns that suggest data entry or modelling errors. Each layer escalates issues with clear context so users can act quickly and confidently.

Defined the rule hierarchy and escalation logic across validation layers. Worked with domain experts to encode their knowledge into reusable checks — translating unwritten engineering judgement into structured, maintainable rules. Designed how validation results are reported: what context is shown, how urgency is communicated, and how users move from error to resolution.

Higher confidence in critical data before it reaches downstream systems. Less time spent on repeated manual reviews. A reusable validation foundation that can be applied across multiple products and data environments.