Industry
AI for manufacturing and wholesale
Manufacturers and wholesalers still receive a large share of their orders as PDFs and emails, then pay someone to type them into an ERP. AI automation reads the purchase order, matches the line items to your catalogue, validates pricing against the contract and creates the sales order, with exceptions routed to a person.
We integrate with
- MYOB Advanced
- Netsuite
- Pronto
- Cin7
- Unleashed
- Xero
- Microsoft 365
- EDI
- and more
The processes we automate in manufacturing and wholesale
Purchase order to sales order
A PDF purchase order arrives in the orders inbox. Line items, part numbers, quantities, delivery address and requested date are extracted, matched against your catalogue including the customer's own part numbering, checked against contract pricing, and the sales order is created in the ERP. Confirmation goes back to the buyer. Nothing is re-keyed.
Quote requests and stock checks
Requests read and priced against the correct tier, with a live stock and lead time check, and the quote drafted for approval. The customer gets an answer the same day rather than after someone works through the queue.
Three-way matching to the ledger
Supplier invoice, purchase order and delivery docket compared, quantities and pricing validated, GST checked, matched invoices posted and scheduled for payment. Only genuine variances reach a person, with the discrepancy already identified.
Supplier documents and certificates
Certificates of analysis, compliance declarations, material safety data sheets and test certificates filed against the batch or product they belong to, with expiry and revision tracked. Findable in seconds when a customer or auditor asks.
Dispatch and freight paperwork
Packing lists, consignment notes, dangerous goods documentation and customer-specific delivery paperwork generated from the order, in whatever format each major customer insists on.
Customer service on order status
The perpetual stream of 'where is my order' emails answered from live ERP and freight data, with anything involving a genuine problem escalated to a person along with the full history.
Re-keying is the single most automatable task in the sector
Order entry is a peculiar job. It requires accuracy and product knowledge, it directly affects customer satisfaction and margin, and it consists almost entirely of reading one document and typing its contents into another system. It is skilled work applied to an unskilled task.
It is also where a specific and expensive class of error lives. A transposed quantity ships the wrong volume. A mismatched part number ships the wrong item. A missed contract price erodes margin silently across every order until someone runs a report. These errors are not caused by carelessness; they are caused by a human doing repetitive transcription at volume, which is a task humans are reliably bad at.
The people doing it are usually your most product-knowledgeable staff, which is precisely why they were given the job, and precisely why it is the wrong use of them.
The hard part is not reading the PDF
Extracting text from a purchase order has been possible for years. The reason automated order entry historically failed is everything after that: the customer who uses their own part numbers, the one whose PDF layout changes without warning, the one who puts the delivery instruction in the email body rather than the document, and the one whose quantities are in cartons where your system expects units.
Language models handle this variation in a way that template-based extraction never could, because they read the document rather than matching its coordinates. But handling variation is not the same as handling it correctly, so the design has to assume ambiguity.
- Customer part number mapping maintained as data you can inspect and correct, not inferred each time
- Unit-of-measure conversion made explicit and checked, because this is where quiet errors hide
- Contract pricing validated against the agreement, with any deviation flagged rather than accepted
- Confidence thresholds per field: an ambiguous quantity stops the order, an ambiguous delivery note does not
- Every exception routed to a person with the source document and the specific uncertainty highlighted
Nothing ships on a guess
Below the confidence threshold the system does not proceed. An order held for thirty seconds of human confirmation costs nothing; an order shipped wrong costs the freight, the restock, the credit and the customer's patience.
For the customers who cannot send EDI
If your major customers are on EDI, that channel is already handled. The problem is the long tail: the customers whose volume does not justify an EDI project, who will keep emailing PDFs indefinitely, and who collectively account for a large share of the order entry workload.
Document automation is effectively EDI for the tail, giving the same structured outcome without requiring anything of the customer. In practice this often matters more than the EDI channel, because the tail is where the manual work is.
Frequently asked questions
How accurate is automated order entry?
Accurate enough to be useful only if it is designed to know when it is not. The important number is not raw extraction accuracy but how reliably the system identifies its own uncertainty and stops. We build to per-field confidence thresholds and measure against a set of your real historical orders during the build, so you see the actual figures on your own documents before it goes live.
Will this integrate with our ERP?
Most mid-market ERPs (MYOB Advanced, Netsuite, Pronto, Cin7, Unleashed) provide APIs we can create orders through. Older or heavily customised systems sometimes need a different approach, which we assess during discovery and tell you about before you commit.
What about customers who use their own part numbers?
Handled through a mapping you own and can inspect. The system learns from the corrections your team makes, so mappings improve over time, but they remain visible data rather than something inferred invisibly on each order, which matters when a mapping is wrong and you need to find out why.
Do we still need order entry staff?
You need fewer people typing and the same people applying product knowledge. In practice the role shifts to handling exceptions, managing the customer relationships behind them, and the work that was always being deferred because order entry consumed the day.
More questions answered on the full FAQ.
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