Manually rewriting orders from emails, paper notes, or chat messages into an ERP system is time lost - time that slows down sales. Instead of selling, account managers become data entry operators. That work can be automated with AI. The Power Center team at ANEGIS approached this challenge differently. Rather than introducing yet another rigid form and forcing customers to change their habits, they built a tool that handles diverse inputs from multiple channels: AGA – ANEGIS Generative Assistant.
The daily reality of a sales department
A sales representative opens their inbox. Fourteen new messages. One order written directly in the email body. Another attached as a PDF. A third sent as a photo of handwritten notes. Then an Excel file, a CSV, a web form submission. Each order looks different. Each arrives through a different channel. Each requires manual processing.
This is standard practice in many sales teams: multiple channels, multiple formats, one person expected to handle them all.
Copy, paste, repeat
Receiving the order is only the beginning. The sales rep must:
- Read and interpret what the customer is ordering and in what quantities.
- Check whether the customer exists in the ERP system—and create a profile if not.
- Search for products, since customers rarely use official system names.
- Compare, match, and validate.
- Verify stock levels and delivery dates.
All of this happens before data entry even begins. The work is repetitive, time-consuming, and error-prone. A mistyped SKU. An incorrect quantity. A wrong delivery date. Errors are statistically inevitable - especially with the fiftieth order of the day.
There is also a scalability issue. If order volume doubles, either:
- Salespeople spend twice as much time on administration instead of selling, or
- Response times increase significantly.
Both scenarios carry direct costs.
Why response time creates competitive advantage
Customers sending RFQs rarely contact only one supplier. They typically reach out to several at once and often choose from the first complete responses they receive. A company responding within two hours has an advantage over one replying after two days. Not necessarily because it is better - but because it was first.
There is also operational cost. Every inquiry consumes human time, regardless of whether the deal closes. Errors and delays directly affect brand perception and customer retention.
Don’t ask “if AI,” ask “where and how”
Sales order processing is a natural candidate for AI - if implemented deliberately. The goal is not for AI to make commercial decisions. The goal is to remove repetitive, error-prone tasks that consume hours each day: extracting order data, identifying customers, matching products, validating ERP data.
This is precisely the role of AGA – ANEGIS Generative Assistant.
What AGA actually is
AGA is not a chatbot and not an off-the-shelf product. It is a solution framework tailored to each company’s processes and systems.
AGA activates the moment an order arrives. The source does not matter: email, form, chatbot, SMS. The format is secondary as well. The system processes: email text, PDFs, Excel files, CSV files, even images of handwritten notes.
Using AI, AGA analyzes content and extracts key data: customer details, products, quantities, and commercial terms.
The system is designed under the assumption that real-world input data is heterogeneous, incomplete, and often inconsistent - because that is how real orders look.
ERP integration - not another browser window
The key difference between AGA and simple OCR tools is deep integration with ERP and CRM systems - particularly with Microsoft Dynamics 365, though the architecture supports virtually any system with an API.
AGA identifies customers in the database based on email address, signature details, tax ID, or company name. If the customer does not exist, the system can suggest creating a new account.
When customers use informal product names (“that red connector” instead of SKU X-123), AGA applies semantic search and vector databases. Instead of matching exact strings, it compares meaning and context, mapping colloquial descriptions to inventory indices.
The result: AGA works directly on real system data, executing repetitive tasks faster and with fewer errors.
What changes in practice
Salespeople who previously spent hours retyping orders regain time for relationship building and advisory work. Orders are processed faster. Customers receive offers or confirmations before competitors have even opened their inboxes. Scalability is no longer tied to linear headcount growth in the back office.
This is not about removing people from the process. It is about shifting them toward activities that create value: negotiation, consulting, trust-building. Rewriting data from a PDF into Dynamics is not where a strong salesperson creates impact. That is what AGA does.
Webinar: AGA in action
If you want to see how AGA processes orders from multiple channels and formats, join our free webinar on March 19 at 10:30 AM. During the live session, we will demonstrate how the system:
- Processes multi-format orders
- Uses semantic search
- Creates ready-to-confirm orders in Microsoft Dynamics 365
Register here.
FAQ
Does AGA work only with Microsoft Dynamics 365?
No. AGA is optimized for Microsoft environments but can integrate with virtually any ERP or CRM system that provides an API. The processing logic remains system-agnostic.
How does AGA handle errors in customer orders?
The system is built on the assumption that inconsistencies will occur. When data is ambiguous or incomplete, AGA does not guess. Instead, it flags the order for review and clearly indicates what requires attention - for example, “ambiguous product code.” This ensures process safety while maintaining automation for clean cases.
Does implementing AGA require process changes?
AGA adapts to existing communication channels, so customers do not need to change how they place orders. Internally, the process becomes simpler: instead of manual data entry, employees focus on verifying and approving pre-processed orders in the ERP system.
