PDF-to-Data Extraction Pipeline

Placeholder case study — replace with your real project details.

The problem

A recurring business process depended on information locked inside PDF reports: inconsistent layouts, tables that spanned pages, and key figures buried in free text. The existing workflow was manual copy-paste into spreadsheets — slow, error-prone, and impossible to audit.

The approach

• Profiled a sample of documents to identify the distinct layout families.
• Built a layout-aware extraction layer with pdfplumber, using positional rules for tables and regex patterns for inline figures.
• Normalized everything into typed pandas DataFrames with validation rules (ranges, formats, cross-field consistency) that flag suspect records instead of silently accepting them.
• Persisted results to a queryable store with full provenance: every value traces back to its source document and page.

The outcome

• ~95% of documents processed with no human touch; the rest routed to a review queue with the specific validation failure attached.
• Turnaround for a full batch dropped from days to minutes.
• The structured dataset unlocked downstream analysis that was previously impossible — trends across years of historical reports.