CASE Study

The solution combined OCR/ML-assisted document processing with human review, while translating complex accounting workflows into a usable digital system.

Note: Selected workflow screens, recreated for this case study from the original interaction design. Implementation demo available on request.

Context and problem

A UK accounting firm wanted to reduce manual invoice and receipt processing and lower the risk of human error, without disrupting the accounting practices staff already relied on.

Project goal

Design a workflow system to reduce manual document processing and human error, while preserving familiar accounting practices and supporting future automation.

Constraints

Variable document formats
Invoices and receipts used inconsistent layouts, so OCR results had to remain easy to review and correct.

Established accounting model
Existing financial periods and folder structures needed to be preserved rather than reinvented.

Operational reliability
Large record volumes and sensitive financial data required fast navigation, clear system states, and editable extracted values.

I worked directly with the accounting company owner to understand their existing process, data structure, and expectations for automation.

We started with a Blue Sky version of the solution, then narrowed the scope around budget, technical feasibility, and day-to-day workflows.

Two assumptions changed during discovery. The product was initially planned for local deployment, but remote work required shared access, so we moved toward a cloud-based solution. OCR/ML also could not reliably determine every value, so human review became part of the core workflow.

The client’s existing folder structure and spreadsheet specification helped anchor the information architecture in a model staff already understood.

Tradeoff: lower upfront cost vs. staff effort

To stay within budget, the client chose to train the OCR/ML system through day-to-day use rather than outsource data preparation. This meant staff had to review uncertain results and correct fields, creating more manual work initially in exchange for lower upfront cost and the potential for increased automation over time.