There is a version of B2B order processing that most inside sales teams know well. A customer email arrives with a PDF attachment. Someone opens it, reads through the line items, opens the ERP, creates a new order, types in the customer number, adds each product one by one, checks the pricing, saves the record, and sends a confirmation. Multiply that by 50 orders a day and you have a team spending most of their working hours on data transcription.
The AI-powered alternative is not a distant vision. It is running in production at manufacturers and distributors right now. This page walks through exactly how it works, step by step, from the moment a customer email lands in your inbox to the moment a completed order sits in your ERP, typically in under two minutes.
<2 min
from inbox to confirmed ERP order
vs. 10–20 minutes per order with manual processing
The problem
Before walking through the automated workflow, it helps to understand why manual order processing fails at scale.
Each manual step introduces delay and an opportunity for error. Mis-typing a product code, transposing a quantity, or missing a delivery instruction all cause downstream problems: wrong product shipped, billing dispute, customer complaint, return logistics.
The deeper problem is scale. Manual order processing does not scale gracefully. Double the order volume and you roughly need to double the headcount. During volume spikes, quality drops as the team rushes. The only sustainable answer is to change what the team spends its time doing.
The real cost per order
€20
per order in labour, error correction, and rework: manual processing baseline
€2k
daily processing cost at 100 orders before counting a single mistake
2×
headcount needed when you double order volume. Manual does not scale
The automated workflow
Here is how turian's AI agent handles a B2B sales order from arrival to ERP entry.
The email arrives and gets classified
A customer sends an order. It might be a free-text email in German, a PDF attachment, an Excel file with 40 line items, or a scanned document. The AI agent monitors the shared sales inbox and reads every incoming message. Its first task is classification: is this a new order, an RFQ, a complaint, a general inquiry, or something else?
This step alone saves significant time in high-volume inboxes where different message types arrive mixed together. The AI routes each one to the correct workflow without anyone manually triaging.
Data extraction using large language models
Once an order is identified, the AI extracts all relevant fields. This is where modern AI differs fundamentally from older OCR tools. A traditional OCR system reads text but does not understand it. It requires a fixed template and breaks the moment a customer uses a slightly different layout.
An LLM-based system understands meaning, not just structure. If a customer writes "please send us 200 of the flange connectors we ordered last quarter, urgent delivery to the Hamburg warehouse," the AI interprets that: it identifies the product reference, the quantity, the urgency flag, and the delivery location, even without a formal order form.
Validation and matching against master data
After extraction, the AI validates the data. It cross-checks the product description against your catalogue to confirm the correct SKU. It verifies whether the customer's pricing terms match what is on file in the ERP. It checks for duplicate orders.
ERP entry via API integration
For orders that pass validation cleanly, the AI creates the sales order directly in the ERP system via API. It fills in the order header, adds each line item, applies the correct pricing, and sets the delivery details. It does in seconds what a person would take 15 to 30 minutes to do manually.
Customer acknowledgement
Once the order is created in the ERP, the AI can draft an order acknowledgement to the customer. This can be sent automatically for fully processed orders, or routed to a team member for a quick review before sending. Either way, the customer gets a response in minutes rather than hours.
At a glance
The key distinction from older automation tools is that this flow handles free-text emails, PDFs, and Excel files in any format and any language, without templates and without a setup phase for each new customer format.
In practice
The clearest illustration of this workflow in action comes from Unigloves UK, a leading manufacturer and distributor of disposable gloves serving medical, industrial, and consumer markets across 50 countries. During the COVID-19 pandemic, Unigloves faced an order volume that increased exponentially. A customer service team of four people was responsible for processing all incoming orders, many of which contained hundreds of line items. A single complex order could take an hour or more to enter manually.
Unigloves UK: Results
Measurable results, fast implementation
79%
reduction in processing time for complex orders
84h
of manual work saved per month, more than two full working weeks
85%
of orders processed fully autonomously, no human intervention required
"The support from turian's technical team was something I had not seen from any software company I had previously worked with. The solution was tailored to our specific product catalogue, ERP configuration, and order formats."
Tom Vandersteen — IT Manager, Unigloves UK
The 85% straight-through processing rate means the team's attention shifted almost entirely from data entry to exception handling and customer interaction. Instead of spending the day typing, they monitored AI's progress, resolved the minority of cases that needed human judgment, and had more bandwidth to respond to customer queries. Service levels were maintained during peaks, including periods when team members were absent.
Technology comparison
Two older automation approaches are worth addressing directly because many companies have tried them before and found them insufficient.
Template-based OCR
Can extract text from documents but requires a fixed layout for each customer's order format. The moment a customer sends an order slightly differently, or adds a new column to their Excel, the OCR fails or misreads the data. OCR sees text but does not understand it, logical checks still fall to humans.
RPA
Follows fixed scripts: click here, copy this, paste there. It breaks when something unexpected happens. In sales order processing, almost everything is slightly unexpected. Customers have different formats, different terminology, different levels of order completeness. RPA is suitable for processes that never change.
AI Agents
Handles variability by understanding meaning. It does not need a template because it reads intent. It does not break when formats change because it reasons about the content rather than matching it to a fixed pattern. This is the fundamental shift that makes the two-minute order possible.
The shift
Implementing this workflow does not remove the inside sales team. It changes what they spend their time on.
Before AI Agents
Most of the day consumed by data transcription.
After AI Agents
Customer-focused work, not data entry.
