MXTechies developed an AI-powered purchase order validation platform for a leading FMCG manufacturer supplying products to quick-commerce and retail partners such as Blinkit, Zepto, Swiggy, and Reliance Retail.
The solution, called QCP (Quick Commerce Processing), automates purchase order ingestion, validation, correction, and ERP integration. Built using the Mendix low-code platform, the system processes large volumes of POs from multiple partners and validates them against master datasets to ensure accuracy before invoicing.
The client is a major FMCG manufacturer distributing consumer goods across multiple retail and quick-commerce partners.
Purchase Orders are generated by partner platforms based on the company's product catalog and material master data. However, frequent changes such as new product launches, packaging changes, SKU updates, and price revisions often create inconsistencies between partner systems and the FMCG company's internal data.
These inconsistencies required manual validation before orders could be processed.
The company receives daily purchase orders through APIs and email-based PDF documents.
EAN Checks
MRP Mismatch
Case Size
Grammage
SKU Mapping
Refs Sync
Previously, a manual validation team spent 4–5 hours validating each PO against internal master datasets.
Some line items had to be removed
Some POs were delayed
Some orders were dropped completely
MXTechies implemented QCP (Quick Commerce Processing), an AI-powered platform that automates the entire purchase order validation workflow.
POs are received via REST APIs or extracted from PDF documents using OCR technology. The system then validates the order against multiple master datasets including material master, stock master, OTR data, and PIPO sequence.
Using AI-based validation logic, incorrect values such as EAN, MRP, case size, or grammage are automatically corrected.
Once validated, the PO is sent back to the customer for approval and then automatically pushed into SAP ERP for invoicing.
Multi-channel PO ingestion through API and email
AI-powered OCR extraction for PDF purchase orders
Intelligent SKU validation engine
Automated correction of PO discrepancies
Customer-specific SKU mapping
Master data validation across multiple datasets
Automated customer approval workflow
Integration with SAP ERP for invoicing
Monitoring dashboard for PO validation status
The platform was built using the following technologies
Mendix for rapid application development
Azure AI Document Intelligence for OCR document extraction
Agentic AI validation logic for automated corrections
Microsoft Azure cloud platform
REST API integrations for partner connectivity
SAP ERP integration for order invoicing
MXTechies followed a structured implementation approach:
Understanding PO workflows and validation requirements.
Setting up integrations for PO ingestion from customer systems.
Extracting PO data from PDF documents received via email.
Connecting to datasets including material master, stock master, OTR data, and PIPO sequence.
Implementing automated validation and correction logic.
Creating approval workflows for customer confirmation.
Automatically pushing validated orders to SAP ERP for invoicing.
Creating approval workflows for customer confirmation.
Automatically pushing validated orders to SAP ERP for invoicing.
The implementation delivered measurable improvements in order processing efficiency
PO validation time reduced from 4–5 hours to under a minute
Manual effort reduced by more than 85%
Faster order-to-invoice processing
Improved SKU and invoice accuracy
Higher order processing capacity
POs previously dropped due to time limitations are now processed automatically
The automation platform significantly improved operational efficiency by eliminating manual validation processes.The system now processes large volumes of purchase orders simultaneously, enabling the FMCG company to scale operations across multiple quick-commerce partners.
Reduced operational workload
Faster order processing cycles
Improved collaboration with customers
Increased order fulfillment
Higher revenue realization from previously missed orders
IMPACT
reduction in manual PO validation effort
FASTER
Faster purchase order validation time
INCREASED
increase in order processing capacity
ACHIEVED
Significant reduction in dropped PO
GROWTH
Increased revenue realization
The platform will continue to evolve with additional AI-driven capabilities
Predictive PO correction using advanced AI models
Real-time master data synchronization with customers
Customer self-service portal for PO validation
AI-based anomaly detection for order patterns
Expansion to additional retail and distribution partners