AI-Powered Order Management

For U.S. e-commerce and distribution companies, AI has rapidly shifted from experimental to essential. CapitalOne Shopping Research shows that nearly four in five online retailers now deploy AI in their operations, and a similar 81% of B2B wholesalers have at least piloted AI solutions. These companies leverage AI across sales, fulfillment, and customer service, and are already reaping tangible benefits. AI-powered chatbots and personalization engines are boosting online conversion rates by up to 67% and increasing revenues by as much as 40%, as highlighted in the afore-mentioned research from April 2025.

Our Client: A US-based leader in the distribution of electric and electronic components, serving both corporate buyers and end consumers. With a vast and constantly updated inventory, the company ensures product availability, rapid fulfillment, and high-quality customer relationships. 

Overview 

Speed, precision, and adaptability are essential to winning and retaining customers. Our client, a prominent US-based supplier serving both B2B and B2C markets, manages an extensive catalog with hundreds of thousands of items in stock. While their product range positioned them as a trusted source for customers, the variety and volume of incoming orders created a significant operational burden. Leveraging advancements in AI, the AROBS team helped the client transform their order management process into a streamlined, automated, and intelligent system. 

The Challenge 

Customers requested quotes and placed orders through an array of disconnected channels: 

• Free-text messages via SMS 

• Documents in varying formats (Word, Excel, PDF, image scans) sent by email or uploaded to the platform 

• Phone calls and voicemail messages 

While these options offered convenience to customers, they created a highly fragmented input stream for the sales team. Processing these diverse formats required substantial manual effort: matching products, validating customer details, checking availability, and confirming pricing. This labor-intensive approach slowed response times, increased resources and the risk of errors. 

The Solution 

We designed and deployed a robust AI-powered order processing solution, centered on an 

Enterprise Retrieval-Augmented Generation (RAG) architecture. The system was engineered to: 

Process Multi-Channel Data 

• Capture and interpret customer communications from SMS, email attachments, phone call transcripts, and uploaded files. 

Map to Internal Data Structures 

• Automatically match extracted order details to the internal product catalog, CRM records, and customer contact profiles. 

Integrate with ERP Systems 

• Synchronize pricing, stock availability, and order data in real time. Once approved by a sales representative, orders were automatically placed in the ERP system. 

Accelerate Sales Operations 

• Reduce order processing time from hours or even days to minutes, while maintaining accuracy. 

Technologies 

Integration Models

• Hybrid: Transparent, Orchestrated, and Augmented 

Approaches 

• Reinforcement Learning for continuous performance improvement 

• Model Fine-Tuning for industry-specific terminology and product categories 

• Foundational Platform ensuring scalability and adaptability 

• Enterprise RAG for intelligent, context-aware retrieval and processing 

Results & Benefits 

• 70% reduction in human effort for processing quotes and orders 

• Significantly shorter customer response times, improving service levels, and customer satisfaction 

• Scalable automation, enabling the sales team to handle higher volumes without additional staffing 

• Greater data accuracy, reducing costly errors, and improving order fulfillment reliability 

Conclusion 

Employing AI-based solutions to tackle expensive business processes can result in reduced costs and better service to customers. 

AROBS has proven to be a reliable partner to its customers by supporting them to leverage the latest AI technologies for their business.