# How to connect ChatGPT to Oracle Fusion Cloud ERP

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

## Short answer

There are two honest routes: export or report data and upload it for analysis, or give a custom GPT or connector controlled access to a middle layer over Fusion REST. Do not point ChatGPT straight at Fusion with a powerful user, and use a private model when the data is regulated.

## System

Oracle Fusion Cloud ERP

## Capability

chatgpt

## Intro

Most Fusion users who try ChatGPT start by exporting an OTBI analysis to Excel and pasting it in. It works for a one-off question and falls apart the moment someone wants fresh data, a second user or an audit trail.

The next step is a custom GPT with actions, or a ChatGPT connector, that calls an API you control. That API sits in front of Fusion and decides what is reachable. The GPT never holds Fusion credentials and never sees more than your API returns.

Whether this is acceptable depends on the data. Item masters and public lead times are one thing. Supplier pricing, contract terms or export-controlled technical data are another, and for those many manufacturers prefer a model they host themselves.

## Use Cases

### Analyse an exported OTBI report

**Description:** Upload a late-deliveries or aged-inventory export and ask for patterns by supplier, buyer or commodity. Good for one-off analysis with no live access.

### Custom GPT for order status

**Description:** A GPT with one action that calls your gateway for sales order and shipment status, used by customer service who lack Fusion access.

### Draft supplier communications

**Description:** Give the GPT a purchase order summary from your API and have it draft a delay notice, a quote request or a corrective action request for review.

### Explain a variance

**Description:** Pass a small, pre-aggregated cost or price variance table and ask for likely drivers, with the caveat that the model sees only what you send.

### Write report specifications

**Description:** Ask ChatGPT to turn a business question into an OTBI or BI Publisher report specification, then build it yourself in Fusion with your own roles.

## System Surface

- Fusion REST resources reached through your own gateway. The GPT action describes the gateway with an OpenAPI schema, not Fusion directly.
- OTBI analyses and BI Publisher reports for export to CSV or Excel, which fit the upload route with no integration at all.
- BICC extracts to Object Storage for a governed copy that a gateway can query without hammering the production pod.
- Identity domain OAuth 2.0 for your gateway to authenticate to Fusion with a scoped service user, hedged by what your tenancy supports.
- Fusion's own AI assistant features, which use Oracle-hosted models and may overlap for simple Q&A inside the application.

## How It Works

### Decide what data class is allowed

**Detail:** List the fields and records a hosted model may see. Public item attributes may be fine, supplier prices and technical data often are not. Write it down before building.

### Build or buy a gateway

**Detail:** Expose a handful of read endpoints such as order status or inventory by item, backed by Fusion REST or a BICC copy. Return trimmed JSON only.

### Describe it with OpenAPI

**Detail:** Write a precise schema with clear parameter descriptions. A vague schema produces wrong calls. Keep each action narrow.

### Authenticate the gateway, not the model

**Detail:** Use OAuth between ChatGPT and your gateway, and a separate scoped service user between the gateway and Fusion. Map the human to the gateway role.

### Check workspace data controls

**Detail:** Confirm your ChatGPT plan's retention and training settings with your admin and legal team. Terms differ by plan and change over time.

### Log and review

**Detail:** Log every action call at the gateway with user, parameters and row counts, and sample responses for accuracy.

## Example Flows

### Customer service order lookup

**Trigger:** A representative asks the GPT where an order is

**Steps:**

- GPT calls the order status action with the sales order number
- Gateway queries Fusion REST for the order, fulfillment lines and shipments
- Gateway returns status, ship date and carrier only

**Result:** A plain-language answer with no Fusion login handed out.

### Late delivery analysis from an export

**Trigger:** A buyer exports an OTBI late receipts analysis

**Steps:**

- Strip supplier bank and contact fields before upload
- Upload the CSV and ask which suppliers drive the lateness
- Review the grouped answer and verify two examples in Fusion

**Result:** A ranked supplier list in minutes, with no standing integration to maintain.

## On Prem

Fusion data lives in Oracle's cloud, but the model does not have to. If a hosted ChatGPT workspace is not acceptable, put the same gateway in front of an open-weight model that you serve with vLLM or Ollama in your own network, and give users an internal chat front end. The gateway pattern and OpenAPI tools carry over unchanged. You trade some model quality and polish for control over where supplier, pricing and technical data is processed, which is often the deciding factor in aerospace and defense supply chains.

## Security

- Never put Fusion usernames or passwords into a custom GPT's instructions or knowledge files.
- Keep the gateway read-only and return the minimum fields needed for the answer.
- Check export-control and contract clauses before any technical data or controlled program information reaches a hosted model.
- Rate limit the gateway so a loop or prompt injection cannot drain the Fusion REST quota.
- Review who in your organization can publish or share custom GPTs that carry actions.

## Options

### Export and upload

**Fit:** Occasional analysis by one analyst, low-sensitivity data.

**Effort:** Hours, with no integration to maintain.

### Custom GPT with a gateway you build

**Fit:** A few repeatable questions for a defined set of users.

**Effort:** Days to weeks, mostly the gateway and its security.

### Private model with Netray

**Fit:** Regulated or competitive data where a hosted model is not allowed.

**Effort:** Scoped pilot, then rollout to more questions and users.

## Pitfalls

- Pasting full exports with supplier banking or personal fields into a hosted chat is the most common leak.
- Large REST responses blow the context window. Aggregate and trim before returning.
- The GPT may guess an item or order number when it cannot find one. Make the API return clear not-found errors.
- Quarterly Fusion updates can change REST attributes and quietly break an action. Keep a smoke test.
- Treating a plausible answer as verified. Show the source record link so users can check.

## Faq

### Can ChatGPT connect directly to Oracle Fusion?

**Answer:** Not usefully on its own. A custom GPT can call an API, so the practical design puts your own gateway between ChatGPT and Fusion REST. The gateway holds the credentials, limits scope and logs usage.

### Is uploading an OTBI export to ChatGPT safe?

**Answer:** It depends on your plan's data controls and what the file contains. Remove banking, personal and controlled technical fields first, and confirm retention terms with your administrator and legal team.

### Can ChatGPT update Fusion records?

**Answer:** It can if you build a write endpoint, but that is risky. Prefer proposals that a person approves in your own workflow, with the actual REST or FBDI submission done by a separate controlled service.

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

**Answer:** Mainly for regulated or commercially sensitive data, and for predictable cost at volume. An open-weight model on your own GPUs keeps prompts and results inside your network, at some cost in capability and setup effort.

### Does Oracle already offer this?

**Answer:** Oracle provides AI features inside Fusion using its own hosted models. They are convenient for in-application tasks. A ChatGPT or private-model setup makes sense when you want one assistant across Fusion and other systems.

## Search terms

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