# How to use ChatGPT with Sage X3

> Source: https://netray.co/integrations/sage-x3-chatgpt
> Publisher: Netray (netray.co), AI and integration engineering for ERP and manufacturing systems.
> Section: Integrations

## Short answer

You can use ChatGPT with Sage X3 by exporting data, by building a custom GPT whose actions call a small API in front of SData, or by connecting through an approved connector. For regulated or sensitive manufacturing data, a private model may be the better fit.

## System

Sage X3

## Capability

chatgpt

## Intro

Finance and operations people on Sage X3 often start by pasting an exported grid into ChatGPT. That works for one-off analysis but leaks data into a personal account and goes stale at once.

The next step is live access, and for X3 that means deciding what the model can call. X3 itself does not ship a public ChatGPT connector as far as we know, so access usually goes through an API you control.

The right choice depends on what the data is. Open item lists and generic questions are low risk. Bills of materials, customer pricing, defence part numbers and contract terms are not.

## Use Cases

### Explain a variance to the controller

**Description:** Paste or fetch a cost variance breakdown and ask for a plain explanation and likely causes to check in X3.

### Draft supplier emails from PO data

**Description:** Give a late purchase order summary and get a polite chase email in the buyer's tone.

### Write Sage X3 reports and SQL help

**Description:** Ask for help drafting a Crystal Reports layout idea or a read-only SQL query against documented tables, then review it by hand.

### Summarise a customer's order history

**Description:** A custom GPT with an action returns recent orders for a customer code and summarises patterns for the account manager.

### Translate and standardise item descriptions

**Description:** Clean up inconsistent item descriptions before they are loaded back through an import template.

## System Surface

- Grid and list exports from X3 functions to Excel or CSV, the simplest and least live route.
- SData 2.0 REST published by Syracuse, which a thin proxy can wrap into an OpenAPI schema that a custom GPT action can call. Endpoint availability varies by version.
- SOAP web services on older estates, usable behind the same proxy but clumsier to describe.
- Read-only reporting views on a database replica (Oracle or SQL Server) if you want pre-joined data for specific questions.
- Crystal Reports and X3 reporting output, which can be exported as PDF or data files for upload.

## How It Works

### Classify the data

**Detail:** Decide which X3 data may go to a cloud model at all. Keep a list of excluded fields such as pricing, cost and contract terms.

### Choose the ChatGPT plan

**Detail:** Use a business or enterprise plan with the vendor's data controls rather than personal accounts. Read the current terms on training and retention.

### Build a narrow API proxy

**Detail:** Expose a few read-only endpoints (orders by customer, PO status) that call SData and return trimmed JSON. Do not publish SData itself to the internet.

### Describe the API as OpenAPI

**Detail:** A custom GPT action needs a schema. Keep descriptions precise so the model picks the right call.

### Authenticate properly

**Detail:** Use OAuth or a per-user token tied to an X3 user so row access follows X3 rights, rather than one shared super user.

### Pilot with a small group

**Detail:** Review logs, wrong answers and data exposure with five users before opening it up.

## Example Flows

### Customer account briefing

**Trigger:** Account manager asks the custom GPT about a customer before a call

**Steps:**

- GPT action calls the proxy with the customer code.
- Proxy fetches recent orders and open invoices from SData.
- GPT summarises late deliveries and open balances.

**Result:** A one-page briefing in under a minute, with figures pulled live rather than pasted.

### Exported variance review

**Trigger:** Controller exports a variance grid at month end

**Steps:**

- Remove columns not needed for the question.
- Upload to an enterprise ChatGPT workspace.
- Ask for the top five drivers and questions to verify in X3.

**Result:** A faster review, with the controller still verifying against the ledger.

## On Prem

ChatGPT is a cloud service, so it does not run on-prem. If your X3 is on-prem because of data policy, a private model is the consistent choice: an open-weight model served on your own GPUs, with the same custom API proxy in front of SData. You lose some raw capability against the frontier models but keep BOMs, prices and drawings inside your network. A hybrid is common: ChatGPT for generic drafting with no X3 data, and a private model for anything that touches records.

## Security

- Use business or enterprise accounts and confirm training and retention settings from current vendor terms.
- Never give the model write access through actions without an approval step.
- Strip cost, price and personal data fields in the proxy, not in the prompt.
- Authenticate per user so X3 rights still apply.
- For export-controlled or defence data, check your own obligations before any cloud model is used.

## Options

### Manual export plus an enterprise ChatGPT workspace

**Fit:** Fine for low-sensitivity analysis and drafting.

**Effort:** Hours.

### Custom GPT with actions over your own proxy

**Fit:** Good for live read-only lookups of non-sensitive data.

**Effort:** A few weeks including security review.

### Private model with Netray and your X3 partner

**Fit:** Better when records are regulated, priced or proprietary.

**Effort:** A scoped pilot over several weeks.

## Pitfalls

- Staff using personal ChatGPT accounts with exported X3 grids.
- Exposing SData directly to the internet so a GPT action can reach it.
- Trusting generated SQL against X3 tables without a read-only account and review.
- Treating model arithmetic as accurate. Totals should come from X3, not the model.
- Forgetting that custom GPT sharing settings can expose the action to others.

## Faq

### Is there an official ChatGPT connector for Sage X3?

**Answer:** We are not aware of one maintained by Sage, and availability can change, so check the current marketplaces. In practice most teams build a small API proxy over SData or use exports. Verify with your Sage partner before relying on any third-party connector.

### Can ChatGPT see my live X3 data?

**Answer:** Only if you give it a path, such as a custom GPT action calling an API you operate. Without that, it sees only what users paste or upload. Live access should be read-only and scoped to a user's X3 rights.

### Is it safe to paste X3 data into ChatGPT?

**Answer:** It depends on the data and the account. Business and enterprise plans offer stronger controls than personal ones, but you must still decide what classes of data are allowed. Prices, BOMs and controlled technical data usually need stricter handling.

### Why would I use a private model instead?

**Answer:** Control and compliance. A private model on your hardware keeps data inside your network and avoids third-party processing. The tradeoff is less raw capability and the work of running it, which a partner can carry.

### Can ChatGPT post transactions into X3?

**Answer:** It can be wired to, but we advise against direct writes. Have it draft a document or import file, and let a user review and post it through X3 with normal controls.

## Search terms

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