AI-Powered Metadata Extraction & Classification
Let AI suggest document details, such as a category or reference. Your team can review suggestions or set rules to accept them automatically. This needs an enabled processing service and suitable document settings.
Existing interface image for orientation only. Framework names, status labels and signature stamps do not establish certification, legal conformity or a tested deployment. Use the qualified feature descriptions below.
How it works and what to check
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.
Configured auto-confirm settings, confidence and permitted class selection determine whether results remain proposals or are applied. Review routes support confirmation, rejection and selective confirmation. Not every suggestion requires manual review.
The dossier reports isolated tests of decision logic, not a deployed AI integration, universal event logging or a guarantee of accuracy. Evaluate representative inputs and failures. AI vision metadata does not establish full-text transcription of image-only scans.
Key Benefits
Confidence-Scored Suggestions
Every AI suggestion includes a confidence score so reviewers can prioritise their attention on uncertain classifications and fast-track high-confidence items.
Review Proposals
Authorised reviewers can confirm, reject or selectively confirm proposed metadata.
Configured Auto-Confirm
Auto-confirm settings, confidence and permitted class selection affect application. Verify outcomes and available records in your deployment.
Edit Before Confirming
Reviewers can correct any field on a suggestion before confirming, keeping the values the AI got right and fixing only the parts that need adjustment, no need to start from scratch.
Bulk Processing
Review and approve metadata suggestions in batches, filtering by confidence level, document class, or date range for efficient high-volume processing.
Industry Use Cases
Law firms use AI extraction to automatically suggest matter numbers, client references, and document types for incoming correspondence, reducing filing time and improving retrieval accuracy.
Banks leverage AI classification to tag incoming loan documents, financial statements, and regulatory filings with the correct categories and reference numbers.
Hospitals use intelligent extraction to classify patient documents by record type, department, and clinical category, ensuring accurate filing across high-volume admissions.
Government agencies use AI suggestions to classify incoming citizen applications, correspondence, and regulatory submissions into the correct file plan categories.
Related Features
See How This Feature Applies to Your Industry
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