Overview

Northvale Components, a U.S. based industrial parts manufacturer, relied on its sales team to manually enter every purchase order into SAP. Their customers each sent orders in their own way, some as email text, others as PDF attachments, scanned faxes, or spreadsheets, and no two layouts looked alike. As nothing arrived in a fixed shape, the desk had to open each order, read it, and retype it line by line. This absorbed hours a day that the team could have spent on customers and accounts. To address this concern, Bacancy built a generative AI-based order processing software that reads any incoming format and posts the order into SAP on its own.

Technologies Used

PostgreSQL
Python 3.10
SAP
langgraph
Gemini
Jira

Project Highlights

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Automated order processing across emails, PDFs, scanned faxes, and spreadsheets

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AI-powered mapping of customer part numbers to the correct internal SKUs instantly

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Direct SAP integration with idempotent, fully auditable order transactions

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Human review queue for validating low-confidence orders before SAP submission

The Challenges

1

The sales operations team handled more than 1,200 purchase orders every day, manually entering information from emails, PDFs, scanned documents, and spreadsheets into SAP.

2

Every customer submitted purchase orders in a different layout. The existing template-based OCR solution frequently failed whenever document structures changed, requiring constant manual corrections.

3

Customer part numbers rarely aligned with Northvale's internal SKU catalog. Mismatches were discovered only after shipment, leading to returns, credit notes, and operational costs.

4

Any automation solution had to integrate with a live SAP environment while preventing duplicate orders and preserving existing pricing, inventory, and credit validation rules.

Solutions by Bacancy

1

Before building the solution, our team analyzed the order processing workflow. Document extraction wasn’t the biggest challenge. Most of the team’s time was spent identifying correct product SKUs and resolving incomplete orders. That insight shaped the solution around resolution accuracy rather than OCR speed.

2

Our generative AI developers built a two-layer extraction pipeline. Standard OCR handled structured documents, while a vision-language model processed scanned documents, complex purchase orders, and poor-quality files. Every extracted field was validated and converted into structured JSON before downstream processing.

3

To eliminate manual catalog searches, our RAG development team indexed the product catalog using vector embeddings. Semantic search combined with AI-based reranking mapped customer part numbers and free-text descriptions to Northvale’s internal SKUs, assigning confidence scores for downstream validation.

4

Instead of relying on AI for business decisions, pricing, inventory availability, minimum order quantities, and credit validations continued to run through deterministic business rules. Every SAP transaction was protected using confidence thresholds and idempotency controls, ensuring only fully verified orders were posted.

Core Features

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Multi-format order processing across emails, PDFs, scans, and spreadsheets

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Two-stage document extraction with schema-validated JSON

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AI-powered SKU matching with confidence scoring on every line

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Human-in-the-loop review workflow with complete audit trails per order

No. of Resource

05

No. of Resource

Time Frame

Feb 2026 - May 2026

Time Frame

Project Snapshot

Bacancy Built Generative AI-based Order Processing Software for Northvale Snapshot

Outcomes

Around 78% of purchase orders now post directly to SAP without any manual intervention.

Average order entry time dropped from approximately 11 minutes to under 40 seconds.

Manual order entry errors decreased by nearly 90% across all incoming order formats.

Returns and credit notes from SKU mismatches were reduced by more than 80%.

The sales team shifted from routine data entry to exception handling and customer support activities.

Seasonal spikes in order volume were managed without hiring any additional temporary staff.

Technical Stack

Programming Language Python
Workflow Orchestration LangGraphTemporal
Document AI Specialized OCR Layer
Vision Models Gemini Vision-Language Models
Retrieval PostgreSQL (pgvector)Embedding SearchAI Reranking
Validation PydanticRule-Based Validation Engine
Database PostgreSQLRedis
ERP Integration SAP OData APIs with Idempotent Writes
Review Application React
Observability LangfuseField-Level Evaluation Harness
Deployment DockerKubernetesTerraform
Project Management JiraConfluence

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