Document & Knowledge Automation Malta

We catalogue the document types your organisation processes, measures volumes and processing times, identifies data extraction requirements for each type, and assesses current quality levels. This establishes a clear picture of the automation opportunity and its business value.

We collect representative samples of each document type to be automated. For extraction tasks, we annotate a training set that teaches the AI model where to find each data field in the document layout. Sample quality and diversity directly determines extraction accuracy.

We train document processing models on your annotated samples and test extraction accuracy against held-out documents. We measure accuracy by field and document type, iterate on model architecture and training data until accuracy targets are met before any production deployment.

We design the downstream workflows triggered by document processing — routing rules, approval steps, system integrations, and exception queues. This connects document processing to your actual business processes rather than delivering extracted data that still requires manual action.

We build the integrations between your document sources (email, portals, scanners, APIs), the AI processing layer, and your downstream systems (ERP, CRM, DMS, compliance platforms). All integrations include error handling, retry logic, and monitoring.

Before cutover, we run the automation in parallel with manual processing, comparing outputs to validate accuracy in production conditions. Any discrepancies are investigated and addressed. We only recommend full cutover when accuracy consistently meets agreed targets.

We monitor extraction accuracy in production, review exceptions, and use correction data to improve model performance over time. As new document variants are encountered, we update training data and retrain models to maintain high accuracy.