End-to-end Autonomous O2C: A Case Study In Agentic AI And IDP

In this case study, we study how a Swiss exporter streamlined its Order-to-Cash (O2C) Cycle with AI Automation and multi-agent AI systems.

The company's goal is to accelerate Purchase Order (PO) processing and reduce manual work for its employees, thereby increasing productivity and scaling operations without increasing headcount.

The presented O2C cycle automation falls under the category of intelligent document processing (IDP). Modern AI models enable companies to develop IDP systems with strong generalization and flexibility.

But to ensure accuracy and consistency, it's critical to have human supervision in IDP systems. Therefore, in this case study, we discuss how to implement an effective Human-in-the-Loop (HITL). Enforcing human supervision via HITL is an essential part of every real-world AI Automation.

The O2C cycle for this specific case was implemented in three steps. Each step is carried out by a dedicated AI agent.

Here is a three-step O2C process:

1. Register Purchase Order: POs are received in different formats, such as emails, Excel sheets, PDFs, or images. Regardless of format, they must be processed and registered in the internal PO processing software.
2. Deliver Note Processing: Once suppliers have provided the delivery notes, the company must ensure that all the delivery notes fulfill the entire original PO. Next, they will apply pricing policy, convert currency, and issue an invoice in their accounting software.
3. Invoice Reconciliation: Finally, a PDF version of the invoice will be sent to the client along with a personalized email. The invoice payment will be monitored against specified deadlines; if not paid, reminder emails will be sent.

This process is illustrated in the following image.

This process is implemented with three AI agents. These AI agents collaborate to ensure that the O2C cycle is performed systematically and to reduce manual tasks, such as data entry.

While manual data entry and Delivery note processing take a lot of time, an AI agent can perform this in seconds! Therefore, it saves employees significant time and increases bandwidth for more strategic tasks.

Importantly, POs and Delivery Notes are provided in different formats (PDF and image), layouts, and languages. While traditional automation focused on OCR, we found that modern AI models can easily outperform the OCR in terms of speed and accuracy.

Interestingly, modern AI systems such as Gemini and LlamaExtract can readily generalize the process beyond the document formats and languages, enabling rapid delivery of agentic solutions for intelligent document processing.

The following figure depicts the three AI agents that orchestrate the implementation of the entire PO reconciliation process.

For the rest of this case study, we focus on the Delivery Notes Processing. This provides a great case study for how AI systems can process a diverse set of documents in different formats and languages.

The delivery notes are provided by different suppliers. As a result, each delivery note contains brand-specific formatting and information unique to each supplier. Moreover, each supplier may be located in a different region or city, and the delivery note may be provided in the local language (FR, DE, IT, EN).

In our case, the delivery notes were provided in German, Italian and French. Last, each supplier has its own format (PDF, PNG, JPEG, and Excel) with its own unique layout. Even suppliers may use different terms to describe the same concept. For example, "Maximum Shelf Life", "Expiration Date", and "MHD". They all describe the same thing!

Given the complexity of the task, using traditional OCR technology would have been infeasible. Therefore, we tested various AI models and evaluated their performance on a gold-standard dataset. Interestingly, modern AI models like Gemini 3.0 excel at the task and can easily generalize languages, formats, and layouts. The use of different keywords to describe the same concept was identified in the prompt as part of the prompt engineering step.

Once the key information, such as item, amount, and shelf-life, was extracted from all the delivery notes, it's time to apply a pricing strategy. This is where the AI agent uses its knowledge layer to understand what price it should apply to each item for the respective client. This was simply provided by an Excel sheet, which can be updated with a new version via a user-friendly UI.

Finally, in the last step, we are providing all the information to the user for human control. As mentioned above, HITL is an essential component for business automation. In this case, we want to avoid issuing a wrong invoice, as it can cost our client's reputation.

Human-In-The-Loop

Human-in-the-loop (HITL) for O2C automation is a verification layer where human experts validate AI-extracted data to ensure 100% financial accuracy.

To bridge the gap between AI-driven efficiency and enterprise-grade reliability, we implemented a Human-in-the-Loop (HITL) validation layer.

In the context of the Swiss exporter’s O2C cycle, HITL acts as a quality gate that ensures 100% accuracy before any financial document is finalized or dispatched.

The HITL workflow is structured around three core principles:

• Exception-Based Review: The system is designed to flag "low-confidence" extractions. If the AI agent encounters a delivery note with significant handwriting legibility issues or a completely new document layout, it routes the task to a human supervisor for manual verification.
• The "One-Click" Validation UI: Rather than having employees cross-reference multiple windows, we developed a side-by-side comparison interface. On the left, the user sees the original document (PDF/Image); on the right, they see the AI’s extracted data fields (Item, Quantity, MHD, Price). The user simply hits "Approve" or makes quick edits, turning a 10-minute manual entry task into a 10-second verification task.
• Feedback Loops for Continuous Learning: Every correction made by the human supervisor is logged. This data is used to refine the agent's prompts and knowledge layer, ensuring that the system becomes more autonomous over time as it "learns" the nuances of specific supplier formats.
  

Business Impact

The transition from a manual, fragmented process to an AI-agent-driven O2C cycle yielded immediate and measurable results for the exporter:

• 90% Reduction in Processing Time: The time required to process a single Purchase Order. From receipt to invoice generation, the AI automation helped reduce what takes hours, if not days, into minutes.
• Operational Scalability: The company successfully increased its order volume during peak season without hiring additional administrative staff.

Overall, the AI workflow helped the business increase productivity while staying lean and agile. They're essentially achieving a lot more as a team and the manual work is not the bottleneck in scaling operations.

Conclusion: The Future of Autonomous Operations

The Swiss exporter's transition to a multi-agent AI system marks a shift from traditional "rigid" automation to intelligent, adaptive workflows.

By moving away from the OCR technology and toward generative AI models like Gemini 3.0, ELIYA helped the company to solve a decades-old problem in the logistics industry: the inability to process diverse, multilingual, and unstructured documents at scale.

The integration of Human-in-the-Loop was the final piece, ensuring human expertise remained the ultimate authority while AI handled data extraction, currency conversions, and cross-referencing. This synergy reduces costs and creates a more resilient business model.

Ready to Automate Your Business?

Whether you are dealing with a deluge of invoices, complex logistics, or fragmented data across different languages, ELIYA specializes in building the agentic workflows that turn operational bottlenecks into competitive advantages.

Don't let manual data entry and repetitive tasks cap your company’s growth. Let our team of AI experts help you design a system where AI agents do the heavy lifting, and your team stays in control where it matters most.

Book a free strategic consulting call to analyze your current workflows and identify the highest-impact automation opportunities for your business.

Frequently Asked Questions (FAQ)

To help you navigate the rapidly evolving landscape of automation, we’ve compiled the most common questions regarding intelligent document processing (IDP) and its role in modern business operations.

What is the difference between IDP vs OCR?

Traditional Optical Character Recognition (OCR) is a foundational technology that converts images into text. However, it is often "blind" to context. Intelligent document processing (IDP) represents the next generation; it doesn't just read the text—it understands it. While OCR might capture the characters "$500.00," IDP identifies it specifically as a "Total Amount Due" and can cross-reference it against a corresponding purchase order.

How does automated data capture handle different document formats?

Unlike legacy systems that require rigid templates, modern intelligent document processing tools use automated data capture powered by Generative AI. This allows the system to handle unstructured data, such as emails, handwritten delivery notes, or varying Excel layouts, without needing a pre-defined template for every new supplier.

What is the role of AI agents for compliance?

AI agents for compliance act as autonomous monitors. They can perform AI document verification by checking every field against regulatory standards or internal company policies. If a delivery note is missing a mandatory "Shelf Life" (MHD) date or a signature, the agent flags it immediately, ensuring that your Agentic AI process documentation remains audit-ready and legally compliant.

Can IDP be used for HR document automation?

Absolutely. Beyond finance, HR document automation is one of the fastest-growing use cases for IDP. Organizations use these tools to automate the screening of resumes, process employee onboarding paperwork, and manage sensitive employee records with higher precision and privacy than manual handling.

What is the future of intelligent document processing?

The future of intelligent document processing lies in "Agentic" systems. We are moving away from simple "extraction tools" toward document workflow automation where AI agents can reason and make decisions. In the coming years, these systems will not only extract data but also autonomously negotiate discrepancies with suppliers or suggest optimal pricing strategies based on historical data.

How does Automated Invoice Processing reduce costs?

Automated Invoice Processing slashes the "cost per invoice" by reducing the hours spent on manual data entry and error correction. By implementing a seamless workflow from receipt to reconciliation, companies can capture early-payment discounts and avoid late fees, turning the accounts payable department from a cost center into a strategic asset.