How to automate email in Infor SyteLine
Short answer
Read the shared mailboxes with Microsoft Graph or IMAP, classify each message, extract fields with a model, validate against SyteLine master data through the IDO REST service, and create draft transactions for a person to approve. Confident cases can be auto-posted later, after you have measured accuracy.
A large share of SyteLine order entry still starts as an email with a PDF. Customers send purchase orders to a sales inbox, vendors send acknowledgments and date changes to a purchasing inbox, and someone retypes the contents into the Customer Orders or Purchase Order Lines forms. It is slow, it is error-prone, and the knowledge of which mailbox matters lives in a few people's heads.
SyteLine's customer order entry is rich: ship-to, terms, tax codes, price lists, unit conversions, and item cross-references like the customer's own part number. That richness is why simple 'email to order' tools stall, because they cannot map a customer's wording to your item master and pricing rules.
This page lays out how to build an email pipeline that respects those rules, starts with review-first handling, and expands to straight-through processing only where the data proves it is safe.
What teams use it for
Inbound customer POs to draft customer orders
A customer's PDF PO is parsed, customer part numbers are mapped through the customer item cross-reference, and a draft order is created for a CSR to confirm.
RFQ intake and quote drafts
Quote requests are extracted into estimate or quote lines with unmatched items flagged, so the estimator starts from a structured draft.
Vendor PO acknowledgments
Acknowledgment emails are matched to PO numbers, promise dates are compared with the PO line due date, and differences are queued for the buyer.
Shipping and ASN notices
Advance ship notices from suppliers update expected receipt information and alert receiving to inbound freight.
Order change and cancel requests
Emails asking to change quantity or date are linked to the open order line and presented to the CSR with the current values side by side.
Past-due and collections correspondence
Replies to dunning notices are routed to AR with invoice numbers resolved and promise-to-pay dates captured as notes.
Infor SyteLine (CloudSuite Industrial) integration surface
- IDO REST service: create and update customer orders, quotes and POs by invoking the standard IDO methods, so SyteLine's own validation, pricing and numbering run exactly as in the forms.
- Master data IDOs for customers, ship-to addresses, items, customer item cross-references, vendors and terms, used to validate every extracted value before anything is written.
- Document attachment capabilities, such as Infor Document Management or SyteLine's attachment features, depending on what you license, to store the original email and PDF against the created record.
- Application Event System (AES): optional handlers to notify a reviewer when a draft is ready, or to stamp the source of an order as 'email' for reporting.
- Staging tables or custom user-defined fields to hold draft status, extraction confidence and email message ID, so audit and reprocessing are straightforward.
How it works
- 1
Connect to the mailboxes
Use Microsoft Graph for Microsoft 365 or IMAP for others, with a service identity limited to the shared mailboxes. Keep the original message ID as your idempotency key.
- 2
Classify before extracting
Decide whether a message is a PO, RFQ, acknowledgment, change request, invoice or noise. Route by type, since each has different fields and risks.
- 3
Extract structured fields
Pull customer, PO number, line items, quantities, dates and ship-to from the body and attachments using a language model or document parser, with a confidence value per field.
- 4
Resolve against SyteLine master data
Match customer by email domain and PO history, items via customer cross-reference and fuzzy matching, and check units of measure and pricing. Anything unresolved is flagged, never guessed.
- 5
Create a draft, not a posting
Invoke IDO methods to create the order in a pending or hold status, or write to a staging table, and attach the source email. A CSR reviews with extracted values highlighted.
- 6
Measure and loosen gradually
Track per-customer accuracy. When a customer's POs have been correct many times in a row, allow auto-confirmation for that customer with a spot-check sample.
Example flows
Customer PO PDF becomes a draft order
Trigger: An email with a PDF arrives in the orders mailbox.
- Classifier tags it as a customer PO and extracts header and lines.
- Customer is matched, and each customer part number is resolved through the item cross-reference.
- Price and requested date are compared with SyteLine pricing and lead times.
- A draft order is created through IDO methods and the email is attached.
- The CSR gets a review item listing mismatches such as price differences or unknown items.
Result: Data entry drops to review and exceptions, and every order carries its source document.
Vendor date change on a PO
Trigger: A supplier replies that a shipment will be a week late.
- Message is matched to the PO number or vendor reference in the subject or body.
- The new date is compared with the PO line due date and the demand it supports.
- If the delay affects a customer order, the buyer and planner are notified with the impact.
- On approval, the due date is updated through the PO line IDO.
Result: Supplier changes are visible to planning on the day they arrive, not when someone remembers.
Keeping data on-prem
Email content often includes customer drawings and terms, so many SyteLine manufacturers want extraction to stay in-house. Run the pipeline on a server inside your network, call the IDO REST service internally, and use an open-weight model served locally with vLLM or Ollama for classification and extraction. For Microsoft 365 mail, Graph access is cloud-based but only retrieves messages, and the processing and storage of extracted data can stay on your hardware. Keep the original emails encrypted at rest and subject to your retention policy.
Security checklist
- Treat email bodies and attachments as untrusted input, and keep model output away from direct execution or free-form SQL.
- Validate sender domain and SPF or DKIM results before auto-processing, because spoofed POs and bank-change requests are common fraud.
- Never auto-process remittance or bank detail changes, always route to a person with call-back verification.
- Use a service account with narrow IDO permissions to create drafts only, not post, ship or invoice.
- Retain the original message and extraction result with a link to the resulting SyteLine record for audit.
Ways to get it done
Rules plus templates for a few large customers
Effort: Weeks per layout, brittle when formats change.
A handful of customers with consistent PDF layouts.
Build in-house with a model and the IDO REST service
Effort: A pilot in several weeks, accuracy tuning ongoing.
Teams with development capacity and clear review roles.
Partner build with Netray
Effort: Scoped on a sample of your real inbound mail.
When you need a private model, customer cross-reference matching and review queues delivered together.
Pitfalls
- !Skipping customer part number cross-references, so correct POs fail to match your items.
- !Auto-creating orders with no review in the first months, and discovering unit-of-measure errors after shipment.
- !Ignoring thread context, so a later 'ok, go ahead' reply is treated as a new order.
- !Letting the pipeline reprocess the same email after a retry and creating duplicate orders.
- !Parsing only the PDF and missing instructions in the email body, such as 'ship by air'.
Frequently asked questions
Can SyteLine create orders from email natively?
Not in the sense of reading a PDF PO and mapping it to your items and pricing. Infor offers EDI and ION-based document exchange for trading partners that support it, but unstructured email needs an extraction layer in front of the IDO methods.
Is it safe to let AI post orders automatically?
Start with drafts and review. Move a customer to auto-confirmation only after a measured run of correct orders, keep price and quantity tolerances, and sample a percentage for ongoing checks.
How are customer part numbers handled?
Through the customer item cross-reference data in SyteLine, with fuzzy matching as a fallback that flags uncertain matches. The pipeline should never invent an item, unmatched lines go to a person.
Does this work with on-prem Exchange or IMAP?
Yes. Any mailbox you can read via Graph, EWS or IMAP works. Processing, the model and the SyteLine calls can all run inside your network.
What about EDI customers?
Use EDI or ION document exchange where the customer supports it, since structured data beats extraction. Email automation is for the long tail of customers who will never adopt EDI.
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