Process automation · client work
Financial Data Processing Automation — From ERP Export to Structured Business Data
Desktop software that processes Excel and PDF exports from ERP systems: it validates, standardizes, and turns those files into ready-to-use output instead of leaving accounting staff to clean and reshape each file by hand.
- Developed by
- TankDev
- Project type
- Process automation · client work
- Field
- Accounting · financial operations · data processing
- Input
- Excel · PDF · scanned documents
- Core capabilities
- Data extraction · OCR · validation · transformation
- Output
- Standard Excel · structured data · database
- Platform
- Desktop application
- Technologies
- Python · openpyxl · OCR · SQL / database
- Status
- Working operational software
Verifiable scope
These items describe scope, not performance. No verified processing-time benchmark has been published, so none is stated.
Problem and operational context
Excel or PDF exports from an ERP system rarely arrive in the layout an accounting operation can use as-is. Staff then strip unused fields, reorder columns, check records, map source fields onto the target structure, and rekey PDF content when the file will not yield it directly. They prepare the target Excel template and move the information into other records. In some workflows that preparation can take up to about an hour for a single file. The time depends on the file, the source format, and missing fields. It is not a fixed duration for every job.
Project constraints
Replacing the source ERP system was not a precondition. The application works from the files that system already produces. File layout is not guaranteed to stay identical. Some sources are Excel, others PDF. Some PDFs need OCR because they have no text layer. Fields can be missing or unexpected. The output still has to fit the existing accounting operation, and the transformation has to keep that operation’s business rules. Moving from manual checking to automated processing is only useful if the data stays intact.
What TankDev built
TankDev built a desktop data-processing application for an accounting and finance operation. The user loads an Excel or PDF file. The application reads the file type and structure, and uses OCR when a document has no text layer. It normalizes the data, maps and transforms fields according to the business rules, then runs the required checks. The result is a target Excel file ready for use. Structured data can be written to a database when that record is needed, and the processed information is shown back to the user in a consistent view. Client-specific mapping rules and exception logic are not published.
Data-processing flow
- ERP / source system
- Excel / PDF / scanned document
- File processing
- OCR (when required)
- Data extraction
- Normalization
- Validation + business rules
- Data transformation
Outputs
- Ready Excel file
- Database
- Structured data view
Before and after
Before · manual preparation
- 01ERP or PDF export
- 02Manual review
- 03Column and field editing
- 04Copying and reformatting
- 05Preparing the Excel template
- 06Checking the data again
- 07Ready-to-use output
After · application flow
- 01ERP / PDF export
- 02Load the file into the application
- 03Automatic data extraction
- 04OCR (when required)
- 05Validation and business rules
- 06Automatic transformation
- 07Ready Excel file + structured data
Measurable effect
No processing-time comparison has been published. The note that some manual preparation can take up to about an hour is operational context, not a claim that automation finishes every file in a fixed time. What can be stated is the scope: repeated data-preparation steps run in the application, output is a standard Excel layout, manual rekeying is reduced, the same business rules can be applied again, and processed data can be stored as structured records.
Why this is not a simple Excel macro
The application does not merely reorder cells. It takes operational data from different sources, parses it, reads documents with OCR when a text layer is missing, normalizes and validates it, applies the operation’s business rules, and maps it onto the target data model. When a durable record is required, it writes that record to the data layer. The job is not file formatting. It is putting heterogeneous operational data through one repeatable workflow.
Technologies
The desktop application is written in Python. Excel files are read with openpyxl, and the target workbook is produced in that layer. Documents without a text layer are read with OCR. Structured records can be written to a SQL database. OCR is the document-reading step; the system is not positioned as an AI product.
Related capabilities
Running a similar manual operation?
Repeated data preparation between Excel, PDF, and other business systems can be standardized with custom software and process automation.
Tell us about the projectProcess Automation