The Business Challenge
The business situation the organisation faced before the project began.
The source documents were not clean. PDF text could merge numbers and labels, statement layouts changed across periods, and manual spreadsheet work created room for errors. The users also needed a Windows-friendly tool, because the workflow had to be usable by finance staff without Python or terminal access.
How the Solution Was Delivered
Discovery, design, development, testing and rollout — the journey, not the tooling.
- 1Mapped the manual reconciliation and reporting workflow
- 2Identified the fields, balances, classifications, and validation checks that mattered
- 3Built parsing and normalization logic in Python
- 4Generated Excel outputs with consistent structure
- 5Packaged the tool as a Windows executable
- 6Documented the workflow for users and maintainers
Business Outcomes & Impact
The measurable outcomes recorded on this AI Case Study — with the story behind them.
Reduced supported reconciliation and reporting batches from days or months of manual effort to minutes while producing validated Excel outputs for non-technical finance users.
Business Outcome Categories
The story behind the numbers
The workflow reduced supported reconciliation and reporting batches from days or months of manual work to minutes. It also made the output more consistent because the same parsing, validation, and workbook generation rules run each time.
Lessons Learned
What surprised the team, what worked well, and what would be done differently.
- Start from the real workflow, not the tool choice
- Make failures explainable so users know what needs attention
- Package automation in the format users can actually run
- Documentation matters when finance teams need to trust the output