Context & Business Challenges
Documentation workflows in the health sector depend on a constant flow of incoming files, reports, forms, and records written in multiple languages, each requiring careful sorting, naming, and filing before they can be used. As volumes grow, this process becomes a bottleneck: time-intensive, error-prone, and difficult to scale across teams. Agatha identified this as a structural problem. The challenge wasn't just reducing manual effort, it was building a system that teams could trust every day, one that would bring consistency from the moment a document arrives, without adding complexity to the workflow.
High volume of incoming documents in multiple languages and formatsManual sorting and filing process becoming a bottleneck at scaleNeed for a reliable, low-friction solution that fits daily team workflows
Multilingual document classification engine
The system automatically identifies document types regardless of language or format. Each incoming file is categorized, named, and routed through the right workflow, reducing the need for manual interpretation at intake.
Lightweight, reliable architecture
Rather than defaulting to heavy, state-of-the-art models, we chose an approach grounded in the actual data and real user decisions. The result is a system that is robust, maintainable, and aligned with how documentation work actually happens.
Continuous fine-tuning pipeline
As new document types appear, the model adapts. A structured fine-tuning process integrates new examples without disrupting past performance, keeping the system accurate as the volume and variety of documents grow.
Operational outcomes
- Average workload reduction of 55%, reaching 80% in high-volume cases
- Classification accuracy consistently above 95%
- Reliable date extraction across all document types
- Teams reported stronger confidence and less friction in daily documentation work