# How to use ChatGPT with Infor SyteLine

> Source: https://netray.co/integrations/infor-syteline-chatgpt
> Publisher: Netray (netray.co), AI and integration engineering for ERP and manufacturing systems.
> Section: Integrations

## Short answer

There are three ways to connect ChatGPT to SyteLine: paste or upload exported data, build a custom GPT with actions that call an API in front of SyteLine, or use an MCP-style connector. For regulated or customer-controlled data, put a small read-only API in front of the IDOs and consider a private model instead.

## System

Infor SyteLine (CloudSuite Industrial)

## Capability

chatgpt

## Intro

Many SyteLine users already use ChatGPT informally: pasting a PO into the chat to draft a supplier email, or asking it to explain a SQL error. That works, but it is manual and it moves ERP data into a public tool one copy and paste at a time, usually without anyone in IT knowing.

A managed integration is different. It decides which questions ChatGPT may answer from live SyteLine data, which fields it may see, and who is allowed to ask. The design questions are the same as any ERP integration: authentication, scope, and auditing.

This page walks through what is practical today and where the limits are. Product features and plan names for ChatGPT change often, so confirm the current capabilities of your workspace plan before you commit to a design.

## Use Cases

### Order status answers for the sales desk

**Description:** A custom GPT with one action, get_order_status, lets a CSR type a customer order number and receive lines, promised dates, and shipments in plain language.

### Drafting supplier and customer emails

**Description:** Users supply a PO or order number, the action returns the facts, and ChatGPT writes the email in the company's tone for the user to edit.

### Explaining a BOM or routing change

**Description:** An engineer pastes a BOM comparison export and asks for a plain-language summary of what changed between revisions.

### Writing and explaining SyteLine reports and queries

**Description:** An analyst uses ChatGPT to draft SQL or Report Builder logic against a schema description, then validates it on a test database.

### Summarizing quality records

**Description:** Quality staff upload exported NCR text and ask for recurring root-cause themes by supplier or work center.

## System Surface

IDO web service (REST) through the Mongoose runtime, or through the ION API gateway for Infor OS tenants. A custom GPT action needs an HTTPS endpoint with an OpenAPI description, so you normally put your own thin API in front rather than exposing IDO endpoints directly.

Export paths: SyteLine grids and reports can export to Excel or CSV, and Report Builder / SSRS-style output can produce files. This is the simplest route for one-off analysis with no live connection.

The SQL Server database (tables with the _mst suffix and site_ref) for a read-only reporting replica that your API queries with parameterized, pre-approved queries.

OAuth and service accounts: on cloud and Infor OS setups the ION API uses OAuth 2.0 service accounts; on-premises you typically use SyteLine user credentials via a token or a login held by your API tier.

Network reachability: ChatGPT actions call from OpenAI's cloud, so an on-prem SyteLine is not reachable unless you publish a proxy endpoint through a DMZ, which many plants will not allow.

## How It Works

### Decide export versus live

**Detail:** If questions are occasional and data is low sensitivity, exports into a business workspace are enough. If you want live answers, you need an API.

### Define a small read-only API

**Detail:** Create endpoints such as order status, item availability, and PO status. Each calls an IDO load or a parameterized query and returns only the fields the use case needs.

### Publish an OpenAPI specification

**Detail:** Describe each operation with clear parameter names and descriptions. The quality of these descriptions largely determines whether the GPT calls the right action.

### Create the custom GPT

**Detail:** Add instructions (what it may answer, how to cite the record number, what to refuse), attach the action, and configure OAuth so each user signs in as themselves where your plan supports it.

### Map users to SyteLine permissions

**Detail:** Your API should look up the caller and apply the same site and customer restrictions they would have in SyteLine, not a single shared super-user.

### Review workspace data controls

**Detail:** Confirm retention, training-data, and sharing settings in your workspace and have legal sign off before real ERP data flows.

## Example Flows

### CSR checks an order

**Trigger:** Customer calls asking where order 100245 is.

**Steps:**

- CSR types the order number into the custom GPT.
- The get_order_status action calls the internal API.
- API reads the order lines and shipment records and returns a compact result.
- GPT replies with line status and promised dates, and offers a draft update email.

**Result:** The CSR answers in under a minute without opening three SyteLine forms.

### Weekly quality summary from an export

**Trigger:** Quality manager exports last month's NCRs to CSV.

**Steps:**

- Upload the file into a business workspace chat.
- Ask for top five root causes by supplier and trend versus the prior month.
- Review the chart or table and verify two items against SyteLine.

**Result:** A draft management summary in minutes, with the manager checking the numbers that matter.

## On Prem

ChatGPT runs in the cloud, so on-premises SyteLine data has to cross your boundary to be used, either as an uploaded file or through a published API. If part numbers, customer names, or technical data are export controlled or contractually restricted, that path may not be acceptable regardless of the vendor's enterprise controls. The alternative is a private assistant that behaves like a custom GPT but uses an open-weight model served inside your network, with the same read-only API in front of SyteLine. You keep the user experience (ask in plain language, get a grounded answer) while the data never leaves the plant. Many customers run both: ChatGPT for general drafting and a private assistant for ERP data.

## Security

- Use a business or enterprise workspace, not personal accounts, and review data retention and training settings.
- Never let the action accept free-form SQL or arbitrary IDO names; whitelist operations.
- Authenticate each user and enforce SyteLine site and customer restrictions in your API.
- Mask or omit fields that are not needed, such as pricing, cost, or personal data.
- Log requests at the API tier so you can answer who asked what and what was returned.

## Options

### Manual export plus a business workspace

**Fit:** Fine for analysts doing occasional work on low-sensitivity data.

**Effort:** Hours to set up policy; no integration work.

### Custom GPT with a thin internal API

**Fit:** Works when SyteLine or the API can be reached from the cloud and data policy allows it.

**Effort:** Two to six weeks including security review.

### Private assistant with open-weight models (Netray ERPray pattern)

**Fit:** For on-prem, air-gapped, or export-controlled environments that still want natural-language access.

**Effort:** Several weeks for a scoped pilot including hosting.

## Pitfalls

- Pasting ERP data into personal accounts: the most common leak, and invisible to IT.
- Exposing IDO endpoints directly to the internet without a gateway, rate limiting, or logging.
- Treating answers as authoritative: the GPT can misread a field, so require record numbers in every answer.
- Shared service accounts that bypass SyteLine's per-user restrictions.
- Assuming plan features are fixed: connector and action capabilities change, so recheck before rollout.

## Faq

### Does ChatGPT have a native SyteLine connector?

**Answer:** Not that I would assume. Check the current connector directory for your plan, but expect to build a custom GPT action or an MCP-style connector against your own API. Treat any third-party SyteLine connector as something to security review like any other integration.

### Can ChatGPT connect directly to my SyteLine database?

**Answer:** Not safely. Direct database access from a cloud service would require exposing SQL Server, and the model could generate harmful queries. Put a read-only API with fixed operations between them, even if it is small.

### Is uploading SyteLine exports to ChatGPT allowed?

**Answer:** That is a policy question, not a technical one. If your data is subject to export control, customer NDAs, or regulations, check with compliance first. Enterprise plans offer stronger data controls, but they do not remove contractual restrictions on where data may go.

### What about the SyteLine cloud version?

**Answer:** CloudSuite Industrial on Infor OS can be reached through the ION API gateway with OAuth, which is easier to call from outside than an on-prem server. You still want your own API layer for scoping and logging.

### When is a private model better than ChatGPT?

**Answer:** When data cannot leave your network, when you need predictable cost at high volume, or when you want to customize behavior deeply. For occasional drafting on non-sensitive data, ChatGPT is usually simpler.

## Search terms

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