Let AI Handle the Tagging. Your Team Keeps Control.

Every document uploaded to an enterprise system needs to be classified, tagged, and filed correctly. In practice, this means staff spend hours manually selecting document types, entering property values, and assigning categories. Mistakes are common — a misfiled contract, a missing client reference, or an incorrect department tag can make critical documents impossible to find when they matter most.

Dockria's AI-Powered Metadata Extraction changes this by analysing each document as it enters the system and suggesting the most appropriate classification, property values, and tags. The system examines document content, structure, and context to generate intelligent suggestions — each accompanied by a confidence score so your team knows exactly how certain the system is about each recommendation.

The human-in-the-loop design is central to how this feature works. AI suggestions are never applied automatically without oversight. Instead, they appear in a structured review queue where designated reviewers can approve suggestions with a single click, edit values before confirming, or override the AI's recommendation entirely. For high-confidence suggestions that consistently prove accurate, administrators can configure auto-confirm thresholds — but even then, every auto-confirmed action is logged and auditable.

Bulk review capabilities let teams process large batches of incoming documents efficiently. Filter suggestions by confidence level, document class, or date range, then approve entire batches at once or review items individually. Over time, the system's accuracy improves as your organisation's document patterns become clearer, reducing the review workload while maintaining the quality and consistency of your metadata.