AI Order Processing vs OCR and Templates for ERP Order Entry
OCR reads characters and templates say where to find them. AI reads the document like a person does. Why that changes order entry, and why reading is only half the job in Epicor Kinetic.
Gonzalo Nuñez
Chief Technology Officer
OCR turns an image into text. Templates tell software where on a page each field sits. Together they automate documents that always look the same, and break on everything else. AI order processing reads a purchase order the way a person does, understanding which number is the quantity and which is the part, in any layout, with no template per customer. That removes the setup that made OCR projects stall. It does not finish the job: an order is only right in Epicor Kinetic once the customer, parts, units and prices have been resolved against your own data.
What OCR does
Optical character recognition converts pixels into characters. On a clean, typed page it is very accurate. It has no idea what the characters mean: "4100" could be a part number, a quantity, a price or a postcode. On its own, OCR produces a block of text that still has to be interpreted.
Templates and zonal capture
To make OCR useful for orders, capture tools add templates: for this customer's form, the PO number is in the top right box and the lines start below the table header. This works while the layout holds.
- One template per layout. Every customer, and every customer who changes their form, needs setup.
- Fragile. A new column, a second page or a scanned copy that is slightly skewed sends values to the wrong field.
- The long tail is never worth it. Customers who order a few times a year never get a template, so their orders stay manual.
This is why many manufacturers tried document capture years ago and went back to typing.
What changes with AI reading
Language and vision models read the whole document and understand it. They find the PO number whether it is labelled "PO", "Order ref" or "Our reference", follow a table across two pages, read the order typed into an email body, and cope with photos and handwriting. There is no template to build or maintain, so the first order from a new customer is read like the hundredth.
Reading is half the job
A perfectly read purchase order can still be a wrong sales order. The PO says the customer's part number, in the customer's unit, at the price the customer remembers, shipped to an address that may not be on file. Turning that into an order Epicor accepts, and that your team would have entered, means resolving it:
- Customer and ship-to matched to the right customer and address in Epicor.
- Parts mapped from the customer's own part numbers to yours.
- Units of measure taken from the document and checked against the part, never assumed.
- Prices compared with the price Epicor holds for that customer.
- Duplicates caught when the same PO number already exists on an order.
Whatever cannot be resolved should be named on the line, for a person to decide.
Comparison
| OCR with templates | AI extraction only | AI agent resolving against Epicor | |
|---|---|---|---|
| Setup per customer | A template per layout | None | None |
| Handles new layouts | No, until a template is built | Yes | Yes |
| Photos, handwriting, email bodies | Poorly | Yes | Yes |
| Output | Text in fields | Fields as written on the PO | A draft order with Epicor customers, parts, units and prices |
| Customer part numbers | Left to a person | Left to a person | Mapped against live Epicor data |
| What a reviewer checks | Every field | Every value against Epicor | Only the warnings on each line |
What to test in a trial
- A new customer the tool has never seen.
- A customer who uses their own part numbers.
- An order in a unit you do not stock in.
- A PO whose prices differ from yours.
- The same PO sent twice.
- A photo, a spreadsheet and an order typed into the email body.
Measure lines resolved without correction and time spent reviewing each order. Character accuracy tells you little; what matters is whether the order your team approves is right.
How Fluent handles it
Fluent's Order Entry agent reads whatever arrives (PDF, scan, photo, spreadsheet, email body, XML or EDI), resolves the customer, ship-to, parts, units and prices against live Epicor data, and stages a draft with every difference as a warning on its line. Behavior is set with written instructions, and corrections become instructions. Read about order entry automation, compare the best IDP and document automation software, or book a demo.
Frequently Asked Questions
OCR converts an image into characters and relies on templates to know which characters belong in which field. AI order processing reads and understands the whole document, so it finds the right values in any layout without a template per customer.
No. Template-free tools read each document as it comes. Customer-specific handling, such as a supplier whose part numbers carry a revision suffix, is set with written instructions on tools like Fluent.
As a step, yes: text extraction is part of how many AI tools read scanned pages. As the whole solution, rarely. OCR with templates only pays off for a few high-volume customers whose layout never changes.
Yes. Current vision models read photos, skewed scans and handwriting well. Anything the agent is unsure of should still be shown to a person on the line it affects.
Measure it on your own orders, by lines resolved against your ERP without correction and by review time per order. On Fluent, the median review across 25,359 documents in the 90 days to 1 September 2026 was 55 seconds.
A good tool names it. Fluent shows an unresolved part, a unit that is not valid for the part or a price that differs from Epicor as a warning on that line of the draft, and nothing is written to Epicor until a person approves it.
