How to use ChatGPT with Infor LN
Short answer
You can use ChatGPT with Infor LN either by exporting data into it or by giving a custom GPT or app a controlled action over ION API. Exports are simple but stale and risky; live access needs an authenticated, read-only gateway you operate. For regulated or export-controlled data, a private model is usually the safer route.
Most Infor LN users first meet ChatGPT as a copy and paste tool: someone pulls an item list or a purchase order report into a spreadsheet, pastes it into a chat window, and asks for a summary. It works for a quick analysis, and it also puts company data into a tool the IT team may not have approved.
The more useful pattern is controlled, live access: ChatGPT asks a question, a gateway you run turns it into an authorised request against LN data, and only the needed answer goes back. That keeps the ERP behind your own authentication and lets you decide what leaves the building.
This page lays out the three levels, from export to custom actions to a private alternative, with the LN specifics that decide which is realistic for your site.
What teams use it for
Explain a planning message in plain language
Paste or fetch a planned order with its inputs and ask why the system suggested it, with the model describing lead time, safety stock and demand sources in readable terms.
Draft supplier communication
Give it a late order line and supplier history from an approved extract and have it draft a firm but polite chase message for a buyer to edit.
Summarise a project status
Feed in a project's cost and activity extract and ask for a short narrative for the weekly review, with variances called out.
Translate and clean item descriptions
Normalise inconsistent item descriptions or translate them for a multi-language site, writing results to a review sheet, not straight into the item master.
Help new planners learn LN sessions
Use it as a tutor for navigating sessions and terms, using non-sensitive screenshots or documentation instead of live records.
Infor LN integration surface
- Session exports and reports: LN sessions can export grids to spreadsheet or text, and report outputs can be saved to files. This is the lowest-effort path and the one that produces stale snapshots.
- ION API gateway: authenticated REST access to LN services under OAuth-style client credentials issued in Infor OS. This is the natural target for a custom GPT action, subject to which services your version exposes.
- BODs and Data Lake: replicated LN objects you can query with SQL, suitable for building a curated, read-only endpoint instead of exposing LN directly.
- Direct database access: possible on-prem but unsuitable to expose to any external tool; keep it behind your own service.
- Network position: an ION or LN host inside a private network is not reachable by ChatGPT's cloud unless you publish a gateway endpoint, which is a security decision in itself.
How it works
- 1
Decide the data classification first
List which LN data is fine to send to a hosted model (item descriptions, generic statuses) and which is not (customer pricing, controlled technical data, defense or aerospace programs).
- 2
Start with governed exports
If you begin with files, use a scheduled export of a defined, trimmed view instead of ad hoc dumps, and store it where only approved users can reach it.
- 3
Build a thin read-only API
Create a small service that exposes a few endpoints, such as order status and item availability, backed by a replica or the Data Lake and authenticated per request.
- 4
Describe it as an action
For a custom GPT, describe the endpoints in an OpenAPI schema and configure authentication so the action calls your service, not LN. Check your plan's rules for actions and data retention.
- 5
Constrain what comes back
Return only the fields needed, mask sensitive ones, and cap result size so the model never receives an entire table.
- 6
Log and review
Record who asked what, which endpoint ran and what was returned, then review samples with the LN owners.
- 7
Re-evaluate quarterly
Decide whether the workflows deserve a private model once usage and data sensitivity are clear.
Example flows
Buyer drafts a chase note
Trigger: Buyer opens a custom GPT and gives a purchase order number.
- The action calls your gateway with the order number.
- Gateway checks the user, queries the replica and returns line, promise date and receipt history.
- The GPT drafts a note referencing the specifics.
- Buyer edits and sends it from their own email.
Result: A faster, consistent chase message with no copy and paste of ERP screens.
Weekly project narrative
Trigger: Project manager asks for a status paragraph for the review.
- A scheduled export produces a small file of project cost and milestones.
- The file is shared with the assistant under the organisation's workspace controls.
- The assistant summarises variances and open risks.
- The manager edits the text and adds it to the review pack.
Result: A first draft in minutes, with the numbers checked by the manager before use.
Keeping data on-prem
ChatGPT itself is a hosted service, so a purely on-prem LN site has two choices. Either expose a narrowly scoped, authenticated gateway to the outside and accept the exposure, or keep the data inside and use a locally hosted open-weight model that offers a similar chat and tool experience. Many on-prem manufacturers pick a hybrid: hosted assistant for general writing with no ERP data, private model for anything that touches LN records.
Security checklist
- Review the provider's enterprise data controls, retention settings and whether inputs are used for training before any LN data goes in; this varies by plan.
- Never expose LN or its database directly to the internet; publish only a gateway with per-user authentication.
- Mask prices, customer identities and technical data fields not needed for the task.
- Keep export-controlled or defense program data out of hosted tools unless your compliance team has approved the specific arrangement.
- Rotate gateway credentials and log every call.
Ways to get it done
Governed file exports into an approved enterprise workspace
Effort: Days.
Low-sensitivity analysis and drafting where staleness is acceptable.
Custom GPT action over a read-only gateway you build
Effort: Weeks, plus ongoing security review.
Teams that want live answers and are comfortable exposing a controlled endpoint.
Private open-weight model with the same tool layer, via a partner such as Netray
Effort: A scoped pilot of several weeks.
Regulated, export-controlled or IP-sensitive sites that cannot send ERP data out.
Pitfalls
- !Ad hoc pasting by individuals bypasses every control you have; give them an approved route instead of banning it.
- !Exports go stale quickly in a live planning environment, so answers can contradict the current LN state.
- !Over-broad API responses leak fields the user should not see; LN authorisations are not applied automatically to your gateway.
- !Assuming the model understands LN company numbers and customised fields leads to confident but wrong answers.
- !Treating a pilot gateway as permanent leaves unmaintained credentials in place.
Frequently asked questions
Can ChatGPT connect directly to Infor LN?
Not out of the box. You connect it through an action or connector that calls an API you control, typically a gateway in front of ION API or a database replica. Direct connection to LN or its database from a hosted tool is not advisable.
Is it safe to paste LN data into ChatGPT?
That depends on your plan, your settings and the data. Enterprise tiers offer stronger controls than personal accounts, but you still need a classification policy. Customer pricing and controlled technical data are usually the first things to keep out.
Will it know our customised LN sessions?
No. It knows general Infor LN concepts at best. Your customisations, extension tables and company conventions must be supplied through tool descriptions, examples and curated data, otherwise it will guess.
Why would we choose a private model instead?
Because the data cannot leave, because contracts or export rules forbid it, or because you want predictable cost and full audit. Open-weight models can handle many ERP question and drafting tasks when paired with good retrieval and tools.
Can it update orders in LN?
Technically a gateway could allow it, but we recommend read-only access first. If writes are added, they should be drafts that a named person approves and that run through supported LN interfaces, not direct model-driven updates.
Want ChatGPT working on your Infor LN?
A free 30 minute assessment: we map the integration path for your version and setup, and where it should run.
Get a free assessment