Document Classification Service
Containerized microservices on Azure handling document extraction, automated categorization, and structured storage — where consistent labeling mattered as much as the manual effort saved.
- Architecture
- Containerized microservices
- Gain
- Effort and consistency
The problem
Documents arriving continuously and needing to be categorised before anything else could happen to them. Done by hand it is slow — and, less obviously, inconsistent. Human taggers drift over time and disagree with each other, so a taxonomy applied manually is never quite the same taxonomy twice. That inconsistency is the more expensive problem, because it is invisible: nothing downstream can tell a mislabelled document from a correct one.
The approach
An end-to-end document processing service on Azure:
- Extraction, pulling content out of documents as they arrived and in whatever shape they arrived in.
- ML classification, applying the taxonomy automatically rather than prompting someone to choose from a dropdown.
- Structured storage, so classified output landed somewhere queryable instead of back in a folder with a better name.
- Containerized microservices behind a REST API, sized for scalable intake rather than for a fixed expected volume, so a spike in arrivals was a scaling event rather than a backlog someone had to work through afterwards.
The outcome
Manual tagging effort dropped substantially, and labeling became consistent — a model applies the same taxonomy the same way every time, which is the guarantee a human process cannot make no matter how careful the humans are.