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Invoice-to-Inventory Workflow Automation

A liquidation company was losing time and money because invoices couldn’t be read, translated, or matched to inventory correctly. We built a structured, AI-assisted workflow using OCR, translation, and data extraction to turn messy invoices into clean inventory data that could be imported into their POS system.

Quick Snapshot

Client Type: Liquidation & resale business


Workflow Area: Invoices → pricing → inventory intake


Challenge: Foreign-language invoices + inconsistent invoice tracking


Solution: OCR + translation + AI extraction + structured import workflow


Result: Faster processing, improved pricing accuracy, less revenue leakage

The Problem

In liquidation businesses, inventory moves fast—and pricing speed directly impacts revenue. This company received a shipment of goods, but the intake process broke down at a critical point: the invoices were difficult to process.

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Many invoices were in a foreign language, inconsistent in formatting, and not clearly tied to the correct pallets. That made it nearly impossible to:

  • accurately price goods

  • properly track inventory

  • move product onto the floor or online quickly

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When invoices can’t be read and inventory can’t be priced, goods sit longer than they should—and that delay quietly costs money.

This wasn’t a technology issue. It was a workflow issue.

The Approach: A Workflow Built for Real Operations

Instead of relying on someone to manually translate and retype invoice data, we built a repeatable workflow that turns invoices into structured inventory information—while keeping a human review step in place for accuracy.

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The goal was simple:

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Make invoices usable, matchable, and import-ready.

What We Built

1) A “Raw Intake” invoice system (zero friction)

All invoices were uploaded into a single folder:

01_Raw_Invoices
No renaming, no sorting, no manual cleanup required.

This removed the biggest intake failure point: inconsistent file handling.

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2) OCR conversion to extract readable text

Invoices were converted into usable text using Google Docs OCR.

If OCR struggled due to scan quality or formatting, invoices were run through ChatGPT for text extraction. This created a reliable fallback so the workflow didn’t stop when a file was messy.

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3) Translation for foreign-language invoices

Foreign-language invoices were translated using Google Docs translation first.

If anything came through unclear, ChatGPT was used as a secondary step to interpret wording, pricing terms, and product details.

This ensured invoice content could be understood before pricing decisions were made.

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4) AI extraction into structured data

Once the text was readable and translated, a custom GPT extracted key fields into a clean format, including the details needed for inventory processing and pricing decisions.

At this stage, the output stopped being “invoice text” and became structured data.

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5) Human approval + clean POS import

Before anything touched the POS system:

  • extracted data was reviewed and approved

  • clean information was exported to CSV

  • finalized CSV was imported into OctoPOS

This preserved data quality and prevented bad imports.

Results

While the original process created delays and confusion, the improved workflow made invoice processing predictable and repeatable.

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Key improvements included:

  • Faster pricing decisions

  • Improved inventory visibility

  • Reduced processing time and manual rework

  • Less revenue loss from intake delays and missed pricing opportunities

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This turned a stressful, error-prone task into an operational system that could scale with shipment volume.

Want Help Fixing a Workflow Like This?

If you have a process that feels manual, inconsistent, or hard to scale, we can help you map it, improve it, and build a workflow that actually works in day-to-day operations.

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We’ll review one workflow and send you clear recommendations you can implement. Spots are limited weekly.

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