# How to add an AI chatbot to Oracle Fusion Cloud ERP

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

## Short answer

Build a chat assistant that answers from live Fusion data through a controlled REST layer, embed it in a portal or the Fusion UI extension points, and enforce the asker's roles. External suppliers and customers need a separate, stricter design than internal staff.

## System

Oracle Fusion Cloud ERP

## Capability

ai-chatbot

## Intro

Fusion has deep screens and a lot of them. Many users do not need the screens, they need an answer: where is this shipment, did this invoice get paid, what is the status of this requisition. A chatbot is a shorter path to the same records.

The useful ones are grounded. They look up the actual record and show where the answer came from. A chatbot that answers from the model's general knowledge about Oracle will confidently describe a process your configuration does not follow.

There are really two products here. An internal assistant for buyers, planners and AP, and an external one for suppliers or customers. The second has to cope with strangers, stricter data limits and abuse.

## Use Cases

### Requisition and approval status

**Description:** An employee asks where a requisition is and who must approve it. The bot reads the approval chain and reminds the approver.

### Supplier self-service

**Description:** Suppliers ask about payment status and open orders in a portal. The bot answers only for their own supplier site and nothing else.

### Customer order inquiries

**Description:** Customers ask about order and shipment status. The bot reads sales orders and fulfillment lines and gives a ship date with caveats.

### How-do-I help for new buyers

**Description:** Answer process questions from your own procedures, with links to the right Fusion task, instead of generic Oracle documentation.

### Inventory questions on the floor

**Description:** Supervisors ask whether a part is available and where, with answers from on-hand balances by subinventory and locator.

## System Surface

- Fusion REST resources for requisitions, purchase orders, receipts, invoices and sales orders, read through a service layer that applies the asker's permissions.
- Redwood and Visual Builder extension options in Fusion Applications for embedding custom UI, subject to what your release and pod allow.
- Supplier Portal and Oracle's customer-facing options, where a chat widget can sit alongside rather than inside the standard pages.
- BICC extracts to Object Storage for a retrieval copy of history and documents, used for non-real-time questions.
- Identity domain single sign-on, so chat sessions map to a real Fusion user or an external supplier identity.

## How It Works

### Pick the audience first

**Detail:** Internal staff, suppliers and customers need different data, tone and abuse controls. Build one at a time, internal first.

### Build the grounding layer

**Detail:** Implement lookup functions over Fusion REST and a retrieval index of procedures. The model chooses a function, the code calls it.

### Carry identity through

**Detail:** Authenticate the user with SSO, derive allowed business units or supplier sites, and apply them to every query in code.

### Choose the front end

**Detail:** A web widget in an intranet or supplier portal, a Teams or Slack surface, or a Fusion UI extension. The widget talks to your chat service only.

### Show sources and refuse well

**Detail:** Return record links and key fields. When data is missing or out of scope, say so rather than guessing.

### Evaluate before launch

**Detail:** Build a set of 100 real questions with known answers, measure accuracy and refusals, and re-run it after each Fusion update.

## Example Flows

### Supplier payment inquiry

**Trigger:** A supplier asks the portal bot whether invoice 4471 is paid

**Steps:**

- Bot identifies the supplier from the login
- It queries the invoice limited to that supplier's sites
- It returns status, due date and payment date if present

**Result:** The supplier gets an answer without an AP clerk handling an email.

### Requisition chase

**Trigger:** A requester asks why a requisition is stuck

**Steps:**

- Bot fetches the requisition and its approval history
- It identifies the pending approver and age
- It offers to send a reminder through the approver's preferred channel

**Result:** Fewer status emails and faster approvals.

## On Prem

Fusion data stays in Oracle's cloud, but the chat service and model can run on your premises. Host the chat back end, retrieval index and an open-weight model with vLLM on your own GPUs, and have the chat service call Fusion REST over outbound HTTPS. For supplier-facing chat, put the service in a DMZ with strict rate limits and no direct line to internal systems beyond a tight API. This keeps prompts, pricing and order data out of third-party model services, at the cost of maintaining the stack.

## Security

- Enforce identity and data scope in code. Never ask the model to remember whose data it may show.
- For external users, return only fields designed for external use and block cross-supplier queries.
- Defend against prompt injection from supplier-entered text, such as notes and descriptions.
- Retain chat logs only as long as policy needs, and mask personal data in them.
- Rate limit and monitor for scraping, especially on public or supplier endpoints.

## Options

### Oracle's built-in assistants

**Fit:** Questions about standard Fusion tasks within the application.

**Effort:** Configuration, subject to licensing and release.

### Build a grounded chat service

**Fit:** Teams with developers who want custom flows and their own model.

**Effort:** Weeks for an internal pilot, more for external users.

### Netray-built private chatbot

**Fit:** On-prem model, strict data rules or a multi-system assistant beyond Fusion.

**Effort:** Pilot on one audience and ten question types, then grow.

## Pitfalls

- An unrestricted external bot is a data exposure waiting for a clever question. Scope by login.
- Answers that drift from your actual configuration happen when the bot leans on general Oracle knowledge. Ground in your records and procedures.
- Stale data from a nightly extract answered as if live misleads users. Show the as-of time.
- Embedding a widget without testing across quarterly UI updates can break it.
- No evaluation set means accuracy problems show up as user complaints.

## Faq

### Can a chatbot see Fusion data the user cannot?

**Answer:** It should not. Authenticate the user, map them to their business units or supplier sites, and apply those limits to every query in your service code. Do not rely on the model to withhold information.

### Where can I embed the chatbot?

**Answer:** Common choices are an intranet or supplier portal, Teams or Slack, or a UI extension inside Fusion if your release supports it. The widget should only talk to your chat service, never to Fusion directly.

### Can it run without sending data to the cloud?

**Answer:** The model and chat service can run on-prem with an open-weight model. Fusion is still Oracle-hosted, so the service pulls data over REST. Only what the user asks for is fetched, and nothing goes to a third-party model.

### Is a chatbot better than OTBI dashboards?

**Answer:** They serve different needs. Dashboards suit regular monitoring. A chatbot suits one-off, specific questions from people who rarely log in. Many teams use both, with the bot linking back to the source record.

### How accurate will it be?

**Answer:** It depends on the questions and your data quality. Grounded lookups for status questions can be reliable. Open-ended analysis is less so. Measure on a real question set before launch and keep a human route for escalations.

## Search terms

oracle fusion ai chatbot, chatbot for oracle fusion cloud erp, oracle fusion supplier portal chatbot, oracle erp chatbot on premise model, oracle fusion virtual assistant, oracle fusion chat with data, conversational ai oracle fusion scm, oracle fusion natural language assistant

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