How to add an AI chatbot to Infor SyteLine
Short answer
Add a chatbot by putting a retrieval and tool layer between SyteLine and a language model: it answers from IDO and database lookups plus selected documents, applies the asker's permissions, and shows its sources. It can be embedded in a web portal or intranet and run entirely on-prem with an open-weight model.
SyteLine users spend a surprising amount of time asking each other questions: what is the status of this order, which warehouse has the stock, who approved this change, what does this field mean. A chatbot is useful when it answers those questions from live data and policy documents, with a record number the user can open in SyteLine to check.
There are two audiences with different needs. Internal staff want breadth: orders, inventory, jobs, purchasing, and how-to help. Customers and suppliers want a narrow, safe slice, such as their own order status and shipment tracking, with no way to see anyone else's data.
Be honest about scope. A chatbot that only answers FAQ from a help file is easy; one that answers account-specific questions needs identity, authorization, and tool design, and that is where most of the effort goes.
What teams use it for
Customer order status portal chat
Customers sign in to a portal and ask about their own orders and shipments; the bot calls order tools filtered by their customer code.
Internal help desk for SyteLine how-to
Retrieval over your internal procedures and training notes answers 'how do I post a misc receipt' with the steps and the form name your company actually uses.
Supplier PO confirmation questions
Suppliers ask about open POs and due dates; the bot returns only their lines and can capture a revised date as a request for the buyer.
Shop floor supervisor assistant
Supervisors ask which jobs are behind at a work center and what material is missing, from a terminal or tablet.
New hire onboarding
New planners and CSRs ask questions about codes, item types, and your own conventions, with answers pointing to the source document.
Infor SyteLine (CloudSuite Industrial) integration surface
- IDO web service (REST) for live lookups of orders, items, jobs, and POs, called as chatbot tools, with the caller's role determining allowed customers, vendors, and sites.
- Read-only SQL Server replica for aggregate or historical questions, with fixed parameterized queries rather than model-written SQL. Tables use the _mst pattern and a site_ref column.
- SyteLine's own authentication and user setup: internal chat can use single sign-on through your identity provider mapped to a SyteLine user; external portals need a separate customer or vendor identity mapped to a customer or vendor number.
- Document stores for retrieval: Infor Document Management (IDM) attachments, SharePoint, or file shares holding SOPs, price books, and training material.
- Embedding options: an intranet page, a customer portal, or a custom form in SyteLine that hosts a web view, depending on version and customization.
How it works
- 1
Pick the audience and boundary
Decide whether this is internal, customer-facing, or both. Customer-facing chat should have a deliberately small tool set and strict identity checks.
- 2
Build the tool and retrieval layer
Create tools for the five or six most common lookups and a retrieval index for approved documents. Return concise data with record keys.
- 3
Implement identity and authorization
Resolve the user to a role and a customer, vendor, or site scope before any tool runs. Never rely on the prompt to enforce data separation.
- 4
Ground and cite
Instruct the bot to answer only from tool results and retrieved text, to quote the order or document number, and to say when it does not know.
- 5
Add a handoff path
When confidence is low or the request needs a decision, create a case or email for a CSR or planner with the conversation attached.
- 6
Pilot with a small group
Start with one department, review transcripts weekly, and correct tools and documents before opening to more users.
Example flows
Customer asks about a shipment
Trigger: Signed-in customer types, where is my order 100388?
- Bot resolves the user to customer number and checks the order belongs to them.
- Tool loads the order lines and shipment records.
- Bot summarizes shipped and open quantities with dates and carrier reference.
- If the date is later than promised, it offers to notify the CSR.
Result: Routine status calls drop, and the CSR only sees the exceptions.
Internal how-to question
Trigger: A new CSR asks how to enter a blanket order release.
- Retrieval finds the relevant SOP section and training note.
- Bot returns the steps with the form names in your setup.
- It links the source document and notes the date it was last updated.
Result: The CSR gets a company-specific answer without interrupting a colleague.
Keeping data on-prem
An internal SyteLine chatbot can run entirely on-prem: the chat web app, the retrieval index, the tool service, and an open-weight model served with vLLM or Ollama all sit inside your network, with SSO to your directory. This is the right design when customer names, part numbers, and pricing cannot go to a cloud model. Customer-facing chat is harder because it must be reachable from outside; the usual approach is to expose only a web front end and API gateway in a DMZ, keep the model and ERP internal, and allow only whitelisted tool calls. Size GPU capacity for peak concurrent users, and fall back gracefully when it is saturated.
Security checklist
- Enforce authorization in the tool layer by customer, vendor, and site, never in the prompt alone.
- Use SSO for staff and verified identities for external users, with session timeouts.
- Redact or omit cost, margin, and unrelated customer data from tool results.
- Store transcripts with access controls and a retention period agreed with legal.
- Test for prompt injection: free text in order notes and emails should not be able to change the bot's rules.
Ways to get it done
Infor-provided assistants or portal features
Effort: Days to configure if licensed.
Worth checking first for your version and edition; they may cover basic lookups without custom work.
Build with an open chat framework plus an IDO tool layer
Effort: Six to twelve weeks for an internal pilot.
Good for teams with web developers who want control over experience and data.
Netray ERPray private chat with open models
Effort: Pilot over several weeks, then phased rollout.
Best when answers must stay on-prem and you want a SyteLine-aware tool set.
Pitfalls
- !Launching customer-facing chat before authorization is proven; one leaked order destroys trust.
- !Indexing every document without cleanup, so the bot quotes outdated procedures.
- !No way for users to report a bad answer, which hides accuracy problems.
- !Expecting the bot to know custom fields and local codes without documenting them for retrieval.
- !Underestimating concurrency: a GPU that handles a demo may not handle month-end.
Frequently asked questions
Can the chatbot answer questions from live SyteLine data?
Yes, through tools that call IDOs or reviewed queries on a read-only replica. The model should answer from what those tools return and cite the record numbers, not from memory, so users can verify in SyteLine.
Can customers use it safely?
It can be, with strict identity, a limited tool set, and authorization enforced by customer in code. Start internally, review transcripts, and then open a narrow slice such as order and shipment status to customers.
Does it need an internet connection?
Not if you use an open-weight model on your own hardware. Internal use can be fully offline from the internet. A customer-facing portal needs a public front end, but the model and ERP can stay internal.
How is this different from Infor's built-in help?
Built-in help explains product features generally. A custom chatbot can combine your procedures with live order, inventory, and job data, but it also costs more to build and maintain. Use the simpler option if it answers your questions.
What accuracy should I expect?
It varies by question type. Structured lookups through tools are typically reliable; open-ended questions and policy interpretation are less so. Measure it on a set of real questions from your users and decide where to restrict or add human review.
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