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Enterprise SoftwareOverview

Global Shop Solutions

AI-powered document ingestion for ERP manufacturing workflows

TL;DR

01

Built a computer vision-powered API that digitizes manually aggregated invoices, vendor quotes, and documents into structured data within Global Shop Solutions' ERP platform, achieving 95% ingestion and transcription accuracy

02

Delivered the project two weeks ahead of schedule with a model 15% more accurate than originally projected, exceeding the client's expectations on both timeline and performance

03

Automated the full document processing pipeline using GPT-4V and custom LLM field extraction, reducing overhead costs by 90% and freeing manufacturing teams to focus on higher-value work

The Challenge

Manufacturing operations generate a constant volume of paper-based and scanned documents. Invoices from suppliers, vendor quotes, purchase orders, and shipping records arrive in inconsistent formats, from handwritten notes to multi-page PDFs from dozens of different vendors. Extracting usable data from these documents requires manual entry, which is slow, error-prone, and expensive at scale.

Global Shop Solutions builds ERP software used by manufacturers around the world to manage inventory, scheduling, accounting, supply chain, and customer relationships. Their customers needed a way to get structured, actionable data from incoming documents directly into the ERP without manual transcription. The existing process required staff to open documents, read values, and key them into the system field by field. As document volumes grew, so did the overhead.

The core technical challenge was building a system that could handle the real-world messiness of manufacturing documents. Vendors do not use standardized invoice templates. Dates, gross totals, vendor identifiers, and line items appear in different locations, in different formats, and with different labels depending on the source. A reliable ingestion system needed to understand document structure at a level of nuance that simple OCR and template matching cannot provide.

Global Shop Solutions needed a custom AI model that could ingest documents regardless of format, extract the right fields with high accuracy, match vendor nomenclature to ERP records, and return structured data through an API that integrated cleanly with their existing platform.

Key Results

01

95% ingestion and transcription accuracy across real-world invoice and vendor document formats

02

90% reduction in overhead costs associated with manual document processing

03

Delivered two weeks ahead of the projected six to seven week timeline

04

Model accuracy 15% higher than originally projected at project kickoff

05

Smooth engineering handover enabling Global Shop Solutions team to maintain and extend the system independently

The Solution

01

Computer Vision Pipeline Built on GPT-4V

AE Studio built a document ingestion pipeline powered by GPT-4V, OpenAI's vision-capable model, which was specifically suited to the task of understanding scanned and photographed documents in the way a human reader would. Unlike traditional OCR, which extracts raw text without semantic understanding, GPT-4V interprets the structure and meaning of document content.

The pipeline triggers automatically when a document enters the queue. Scanned PDFs and images are passed to the model, which identifies the document type, locates relevant fields, and extracts both required and optional data points.

02

LLM Field Extraction for Required and Optional Data

Once text is extracted from the document, a second LLM layer handles structured field parsing. Required fields such as gross totals, invoice dates, vendor identifiers, and line item details are extracted with priority. Optional fields are identified and captured when present, ensuring the system surfaces all available information without failing when non-standard fields are absent.

This two-stage extraction approach separates the problem of reading documents from the problem of understanding them. Vision handles layout and content recognition; LLM parsing handles semantic extraction and field mapping. Separating these layers makes each stage easier to tune and validate independently.

03

Nomenclature Matching for ERP Integration

Vendor documents use vendor-specific terminology that does not always align with how items are named inside the ERP. A part described as 'Hex Bolt M8x1.25' in a vendor invoice may be catalogued differently in Global Shop Solutions' system.

AE Studio built a nomenclature matching layer that maps extracted vendor terms to ERP records, using string similarity and contextual signals to resolve ambiguities. This layer ensures that extracted data populates the correct fields in the ERP without requiring manual correction for naming inconsistencies.

04

API Infrastructure and Authentication

The ingestion pipeline is exposed through a secure API that integrates directly with Global Shop Solutions' ERP platform. The API handles authentication, accepts document submissions, processes them through the vision and extraction pipeline, and returns a structured key-value response containing all identified fields.

This architecture allows Global Shop Solutions to embed document ingestion directly into their product as a native capability. ERP users can submit documents through their existing interface and receive structured data without leaving the platform or switching to a separate tool.

05

Validation and Quality Assurance

Accuracy in document processing is non-negotiable in financial and procurement contexts. An incorrect gross total or mismatched vendor record can propagate errors through inventory, accounting, and payment workflows.

AE Studio built validation logic into the pipeline to flag low-confidence extractions for review rather than silently passing them through. The system distinguishes between fields extracted with high confidence and fields where the model is uncertain, giving ERP users visibility into which values may need verification before they are committed to the system.

06

Delivered Ahead of Schedule with Performance Exceeding Projections

The project was scoped at approximately six to seven weeks with a small team of one data scientist and a part-time technical project manager. AE Studio delivered the completed system two weeks ahead of schedule.

The accuracy of the final model came in 15% higher than the accuracy target projected at the start of the engagement. Global Shop Solutions' engineering team was closely involved throughout the handover process, ensuring their team understood the architecture and could maintain and extend the system after AE's engagement concluded.

Results

Key Metrics

95% ingestion and transcription accuracy across real-world invoice and vendor document formats

90% reduction in overhead costs associated with manual document processing

Delivered two weeks ahead of the projected six to seven week timeline

Model accuracy 15% higher than originally projected at project kickoff

Smooth engineering handover enabling Global Shop Solutions team to maintain and extend the system independently

The Full Story

AE Studio delivered a production-ready document ingestion API for Global Shop Solutions that met and exceeded every target set at the start of the engagement. The system achieves 95% ingestion and transcription accuracy across the diverse range of invoice and vendor document formats that manufacturing companies receive in the real world.

Overhead costs associated with manual document processing dropped by 90%. Staff who previously spent hours keying invoice data into the ERP can now focus on higher-value tasks, while the pipeline handles routine document digitization automatically.

The project was delivered two weeks ahead of schedule, and the final model accuracy came in 15% higher than the original projection. Close collaboration with the Global Shop Solutions engineering team throughout the engagement ensured a smooth handover, with their team fully equipped to maintain and extend the system going forward.

The result is a native ERP capability that transforms how Global Shop Solutions' manufacturing customers handle incoming documents, from a manual, error-prone process to an automated, accurate, and scalable data pipeline.

Conclusion

Global Shop Solutions needed to eliminate the manual bottleneck of document data entry inside their ERP platform. AE Studio built a computer vision pipeline that handles the full range of invoice and vendor document formats, extracting structured data with 95% accuracy and reducing overhead costs by 90%.

The engagement was completed two weeks ahead of schedule with model performance that exceeded initial projections. The system is now a native capability within Global Shop Solutions' ERP, giving their manufacturing customers automated, accurate document digitization at scale. For an industry where operational efficiency is measured in fractions of a percent, a 90% reduction in document processing overhead represents a meaningful and durable advantage.

Key Insights

1

Vision-capable models unlock document understanding that OCR cannot. GPT-4V interprets document structure and meaning rather than just extracting raw text, making it far more effective on the inconsistent, real-world documents that manufacturing companies receive from dozens of different vendors.

2

Separating vision and LLM extraction into distinct pipeline stages improves tunability. Handling layout recognition and semantic field parsing as separate problems makes each easier to validate and optimize without introducing regressions in the other.

3

Nomenclature matching is essential for ERP integration. Vendor terminology never maps cleanly to internal catalog records. A matching layer that resolves naming inconsistencies is what allows extracted data to populate the right ERP fields without manual correction.

4

Confidence-based flagging preserves data integrity. Rather than passing all extractions through silently, surfacing low-confidence fields for review prevents errors from propagating through financial and procurement workflows where data accuracy matters.

5

Small, focused teams can move fast on well-scoped AI projects. A single data scientist and part-time TPM delivered a production system two weeks early and 15% above accuracy targets, demonstrating what a tight scope and clear problem definition enables.

Frequently Asked Questions

The pipeline uses GPT-4V, a vision-capable large language model, to interpret document structure the way a human reader would rather than relying on fixed templates. This allows the system to locate and extract fields like gross totals, dates, and vendor identifiers regardless of where they appear on the page or how they are labeled. The LLM extraction layer then maps identified values to the correct ERP fields, with a nomenclature matching step that reconciles vendor-specific terminology against internal catalog records.
The pipeline includes confidence-based validation that distinguishes between high-confidence extractions and fields where the model is uncertain. Low-confidence fields are flagged for human review rather than passed through automatically. This prevents inaccurate data from propagating into accounting, inventory, or payment workflows, where a single incorrect value can have cascading effects.
The API accepts document submissions, processes them through the vision and extraction pipeline, and returns a structured key-value response containing all identified fields. This response maps directly to ERP data fields, allowing Global Shop Solutions to surface the extracted data to users within their existing interface without requiring a separate tool or manual re-entry.
The pipeline is designed to handle invoices, vendor quotes, purchase orders, and other business documents that manufacturing companies receive from suppliers. These arrive as scanned PDFs, photographed documents, and digital files in a variety of layouts and formats. The system is not template-dependent, so it can handle new vendor formats without requiring configuration changes.
AE Studio worked closely with Global Shop Solutions' engineering team throughout the project, not just at the end. This collaborative approach ensured that the GSS team understood the architecture, the pipeline logic, and the integration points before the engagement concluded. The goal was a handover where their team could maintain, extend, and build on the system independently without requiring ongoing AE involvement.
OverviewEnterprise Softwareintermediate7 min readComputer VisionDocument IngestionERP IntegrationAI AutomationManufacturingGPT-4VData ExtractionOCR

Published: Jan 2025 ยท Last updated: Feb 2026

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AI Document Ingestion for ERP: 95% Accuracy, 90% Less Overhead