# How to use ChatGPT with IFS Cloud

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

## Short answer

You can use ChatGPT with IFS Cloud by exporting reports for ad hoc analysis, or by giving a custom GPT or an API-based assistant a small set of read-only actions over IFS OData projections. For export-controlled or customer-restricted data, host an open-weight model privately instead.

## System

IFS Cloud

## Capability

chatgpt

## Intro

The common starting point is informal: a planner exports an Aurena list to Excel, pastes it into ChatGPT and asks which lines look wrong. It works, and it is also the riskiest pattern, because nobody governs what leaves IFS.

The next step is structured access. A custom GPT with an action, or a backend that calls the OpenAI API, can query IFS on demand and answer from live data. That removes copy and paste but adds questions about authentication, data scope and where the data is processed.

This page lays out the realistic options for IFS Cloud, including where ChatGPT is a good fit and where it is not.

## Use Cases

### Explain an IFS list in plain language

**Description:** Paste an anonymized export of overdue demand or inventory exceptions and ask for a ranked summary. Good for non-sensitive data and one-off analysis.

### Custom GPT for order status

**Description:** A GPT with an action over a sales order or purchase order projection answers 'where is order X' for the service desk without opening Aurena.

### Drafting supplier and customer replies

**Description:** Feed order facts from IFS into a prompt to draft a polite delay notice, then let a person review and send it from their own mail.

### Spreadsheet and report helper

**Description:** Use ChatGPT to write the formulas and pivots that turn an IFS export into a weekly report, without sending the data itself.

### Learning the data model

**Description:** Ask ChatGPT to explain a projection's entities after pasting its metadata document, speeding up onboarding for new integration developers.

## System Surface

IFS Cloud OData projections are the clean way to give a GPT action or API backend live read access. A GPT action needs an OpenAPI description, so you typically wrap the IFS call in a small gateway that publishes a trimmed spec.

OAuth through IFS IAM can back the action, but most teams put a gateway in front so the GPT never holds IFS credentials directly.

Aurena lists can be exported to Excel or CSV, and report designers produce scheduled outputs. These are the lowest-effort feeds for manual ChatGPT use.

The projection metadata endpoints and the API Explorer give you entity and field names to build accurate prompts. Availability varies by release.

## How It Works

### Classify the data

**Detail:** Decide which IFS data may leave your boundary. Part numbers for commercial items may be fine. Controlled technical data or customer-restricted drawings likely are not.

### Choose export or live access

**Detail:** For occasional analysis, governed exports are enough. For repeated questions, build live access.

### Build a thin gateway

**Detail:** Create an HTTPS service that calls IFS projections with a service identity, returns trimmed JSON, enforces row limits and logs requests.

### Publish an OpenAPI spec

**Detail:** Describe two or three read-only operations, such as get_order_status and list_open_po_lines, with clear parameter descriptions.

### Create the custom GPT or API assistant

**Detail:** Write instructions that tell it to answer only from tool results and to say when data is missing. Decide between a custom GPT inside ChatGPT Enterprise and your own app calling the API.

### Pilot with a small group and review logs

**Detail:** Check which questions people actually ask, then tune tools and instructions.

## Example Flows

### Service desk order lookup

**Trigger:** A support analyst asks the GPT where a customer order stands.

**Steps:**

- GPT calls get_order_status through the gateway.
- Gateway queries the sales order projection with a read-only identity.
- Result returns line status, planned ship date and last shipment.
- GPT summarizes and flags any line past its promise date.

**Result:** Faster answers with no one browsing multiple Aurena pages.

### Weekly export analysis

**Trigger:** Planner downloads an anonymized overdue-demand CSV.

**Steps:**

- Remove customer names and controlled part numbers.
- Upload to ChatGPT with a standard prompt.
- Review the ranked list and the reasons given.

**Result:** A quick prioritization, kept outside any system of record.

## On Prem

ChatGPT is a hosted service, so it cannot sit inside an air-gapped or customer-managed IFS network. If your data cannot leave, the equivalent pattern is a self-hosted open-weight model behind the same gateway design: an OpenAI-compatible endpoint served by vLLM or Ollama, the same OpenAPI-style tools, and a chat front end inside your network. Because the endpoint is OpenAI-compatible, you can often prototype against ChatGPT with non-sensitive data and later swap the base URL to the private model with limited code change.

## Security

- Read your plan's data handling terms. Business plans generally state that customer data is not used for training by default, but confirm for your contract and region.
- Never paste export-controlled technical data or customer-restricted material into a hosted service.
- Put a gateway between the GPT and IFS so credentials, row limits and logging stay under your control.
- Make actions read-only until you have an approval workflow.
- Restrict who can use the GPT and review its conversation logs.

## Options

### Manual exports into ChatGPT

**Fit:** Non-sensitive, occasional analysis.

**Effort:** None, but needs a clear policy.

### Custom GPT or API app through your own gateway

**Fit:** Repeated read-only questions on non-restricted data.

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

### Private open-weight model with the same tools (Netray or in-house)

**Fit:** Regulated, export-controlled or customer-restricted data.

**Effort:** Longer, because of GPU and hosting setup.

## Pitfalls

- Letting ChatGPT see entire OData entities, which leaks fields nobody intended to share.
- Granting the action an IFS admin token for convenience.
- Assuming a custom GPT answers only from tools, when without clear instructions it may guess.
- Ignoring that ITAR or EAR data cannot go to a general hosted service.
- Building on one-off exports and then wondering why numbers do not match IFS.

## Faq

### Is there an official ChatGPT connector for IFS Cloud?

**Answer:** Check the current ChatGPT connector directory and IFS marketplace, because availability changes. If none fits, the practical route is a custom GPT action or API app over a gateway that calls IFS OData projections with a read-only service identity.

### Can ChatGPT run on our IFS data without sending it out?

**Answer:** No, ChatGPT processes data in OpenAI's environment. If data must stay in your boundary, use an open-weight model hosted by you with the same tool design, which also keeps prompts and results in your logs.

### Does ChatGPT Enterprise change the risk?

**Answer:** It adds admin controls, retention settings and contractual data terms, which helps for ordinary business data. It does not make export-controlled data permissible to process in a general hosted service, so classify first.

### Can ChatGPT update IFS records?

**Answer:** It can if you build a write action, but that is rarely wise early on. Start read-only, add drafts that a person approves, and only then consider narrow writes through a separate approved service.

### What should we ask it first?

**Answer:** Pick lookups with a clear right answer, like order status, open PO lines by supplier or inventory by location. They are easy to verify against Aurena, which builds trust before you try analysis questions.

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