Smart Document Automation: How We Built It and What It Delivered

The Business Problem

The client processed hundreds of vendor invoices and supplier contracts every month, each requiring manual data entry into their ERP system. The work was tedious, time-consuming, and prone to transcription errors that caused downstream accounting discrepancies. With a growing vendor base, the team faced a choice: hire more data entry staff or find a smarter way to handle the volume. They needed a solution that could handle varied document layouts — not just one fixed template — and flag anything that looked suspicious before it hit their books.

What We Built

We built a document automation pipeline that accepts uploaded PDFs and images, runs layout-aware OCR to extract raw text with positional context, and then uses an LLM extraction layer to identify and structure key fields: vendor name, invoice number, line items, totals, due dates, and contract clauses.

Extracted data passes through a configurable validation engine that checks field format, range, and cross-field consistency (e.g., line item totals must sum to the invoice total). Documents that pass validation are pushed directly to the ERP via API. Documents that fail are flagged in a review queue with the specific rule violation highlighted — so the human reviewer knows exactly what to check.

Key Features

• Layout-aware OCR: Handles varied document formats — multi-column invoices, scanned contracts, handwritten notes — without template configuration per vendor.
• LLM field extraction: Contextual understanding means fields like "net 30" or abbreviated payment terms are correctly normalized to structured date values.
• Configurable validation rules: Business logic is defined in a YAML config — no code changes needed to add new checks as accounting requirements evolve.
• Bulk processing pipeline: Drag-and-drop upload of up to 500 documents at once, with real-time progress tracking and a downloadable results CSV.

Results Delivered