How we would have an AI agent read orders arriving by email and PDF, while a person always confirms them.
SOLUTION SCENARIO
This is a solution scenario: it describes how we would approach the case, not a delivered project. The company is a typical example. Timelines and phases are indicative, to be confirmed after the feasibility analysis.
AT A GLANCE
A B2B distributor receiving 40 to 80 orders a day by email and PDF can have an AI agent read them and turn them into draft orders in the ERP. A person reviews and confirms each one. The agent does not invent product codes: it checks every line against master data, flags exceptions and logs what it did.
THE TYPICAL COMPANY
A distributor of technical supplies takes orders from resellers. Some write the order in the email body, others attach a PDF from their own system, others send a photo of a paper form. Two people in the sales office spend much of the morning retyping them.
The errors are the usual ones: a wrong item code, a misread quantity, an order left in the inbox of a colleague on holiday.
Why manual order entry does not scale.
Data entry grows with order volume and adds no value.
Every customer uses a different format, so a fixed rule is not enough.
A typing error becomes a wrong shipment and a return.
There is no single place to see orders that have arrived but are not yet entered.
Four phases. Durations are indicative, to be confirmed after the feasibility analysis.
A sample of recent orders, anonymised where needed, to learn the formats, the hard cases and the names customers use for items.
Dedicated mailbox, text extraction including scanned PDFs, recognition of customer and lines, matching against item master and price lists, then a draft order or the exceptions queue.
The agent never confirms an order. The operator sees the draft next to the original document, corrects it if needed and confirms.
For a few weeks the agent works alongside manual entry. We compare results and decide together when to let it prepare the first draft.
STACK
| Component | Choice |
|---|---|
| Input | Dedicated mailbox read via IMAP or the provider’s API |
| Document reading | Text extraction from PDFs and OCR for scans |
| Language model | Model via API under a data processing agreement, chosen for language and cost |
| ERP | Integration via API, for example Odoo, or via import files |
| Log | Every step stored: document, extraction, matching, operator decision |
Orders contain personal and commercial data. Before starting we would agree where they are processed, how long they stay in the logs and which model provider may process them.
Human review and the activity log make every decision traceable and help answer a customer who disputes an order. They are also in line with the transparency and human oversight principles of the EU AI Act.
We do not promise a percentage of automated orders: it depends on how clean customers’ documents are. We would expect less time on data entry, fewer code errors and a single queue of orders to process. We would track average entry time, the share of drafts confirmed without changes and errors found at shipping.
This scenario is part of our business process automation and custom AI agents services, connected to ERP systems.
What companies ask us about AI agents and order entry.
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