# How to add an AI chatbot to Infor VISUAL

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

## Short answer

An AI chatbot for Infor VISUAL answers questions from live ERP data through a fixed set of read-only lookups plus retrieval over your documents. It can sit in a web page, Teams or a portal, and it can run entirely on-prem with an open-weight model when data must stay in the plant.

## System

Infor VISUAL

## Capability

ai-chatbot

## Intro

VISUAL users spend a lot of time opening windows to answer questions that are simple to ask and tedious to answer: when will this ship, do we have the material, what did we pay last time. A chatbot front end removes the navigation without replacing the system.

There are two audiences. Internal users want fast lookups and explanations. Customers and vendors want a status answer without phoning someone. The second audience needs much stricter scoping, because the bot can only ever show them their own records.

A good bot is boring in the right way: it cites the order, shows the date, and says when it cannot find something. It is not a creative writer with database access.

## Use Cases

### Internal order and job lookup

**Description:** Planners and CSRs ask about customer orders, work orders and shipments in plain language and get a short answer with the record ids.

### Customer order status portal

**Description:** A logged-in customer asks where their order is. The bot only reads records tied to that customer id and shows promised and ship dates.

### Vendor self-service

**Description:** A vendor asks which POs are open and what dates are expected, and can confirm a date through a form that a buyer reviews.

### Procedure and quality Q&A

**Description:** Retrieval over work instructions and quality procedures lets operators ask how to handle a nonconformance, with the source cited.

### New hire onboarding

**Description:** A bot explains how a part flows through routing, which windows to use, and where reports live, using your own documentation.

## System Surface

- Read-only views over the VISUAL SQL Server database for the structured answers, such as CUSTOMER_ORDER, CUST_ORDER_LINE, WORK_ORDER, PURC_ORDER_LINE and shipment tables. Confirm names in your version.
- Customer and vendor identifiers that let you scope every query by external user, so a portal user can see only their records.
- Document stores such as file shares or SharePoint for retrieval over procedures, certificates and drawings.
- Hosting surfaces for the front end: an internal web page, Microsoft Teams, or a customer portal with its own login.
- Optional Infor ION or toolkit interfaces for creating records, normally kept out of a chatbot beyond a request form.

## How It Works

### Separate internal and external bots

**Detail:** Do not share one configuration. External access needs identity, row scoping and rate limits that internal users do not.

### Define the question catalog

**Detail:** Write down the twenty to forty questions that cover most traffic, and map each to a query or a document search.

### Build lookup tools and retrieval

**Detail:** Create parameterized, read-only queries. Index documents in a vector store with source metadata so answers can cite them.

### Ground the answers

**Detail:** Instruct the model to answer only from tool results and retrieved text, show record ids, and say it does not know when nothing is returned.

### Add identity and scoping

**Detail:** Tie each session to a user. For customers, force a customer id filter in code, never in the prompt.

### Choose where the model runs

**Detail:** A hosted API for lower-sensitivity use, or an open-weight model served locally for plants that cannot send data out.

### Test with real transcripts

**Detail:** Replay a month of actual questions, score accuracy, and fix gaps before opening to customers.

## Example Flows

### Customer asks about shipment timing

**Trigger:** Customer types 'when will PO 8841 ship?' into the portal bot

**Steps:**

- Session identifies the customer account.
- Tool looks up order lines matching that customer PO number.
- It returns line status, promised date and shipped quantity.
- Bot replies with the dates and offers to notify a CSR if the date has slipped.

**Result:** The customer gets a self-service answer and the CSR only sees exceptions.

### Operator asks about a procedure

**Trigger:** Operator asks how to tag nonconforming parts on a job

**Steps:**

- Bot retrieves the relevant section of the quality procedure.
- It summarizes the steps and quotes the source name and revision.
- It offers a link to open the document.

**Result:** Operators get consistent guidance tied to the controlled document.

## On Prem

A VISUAL chatbot can run fully inside the plant. Host the front end on an internal web server, run the lookup service next to SQL Server, index documents on a local vector store, and serve an open-weight model such as a Qwen, Llama or Mistral class model on a local GPU. For customer access, put only an authenticated front end in a DMZ and keep the model and database behind it. Smaller models handle structured lookups well, but expect weaker performance on long, messy documents than larger hosted models.

## Security

- Enforce customer or vendor scoping in code on every query.
- Treat user text as untrusted and never let it alter tool parameters beyond typed fields.
- Rate limit and log external sessions, and avoid storing personal data you do not need.
- Keep the bot read-only; send changes to a form or a person.
- Version and review the document index so superseded procedures do not get cited.

## Options

### Build on an open-source chat framework

**Fit:** In-house developers and an internal-only audience.

**Effort:** Weeks for a solid internal bot, longer for external users.

### Use a hosted chatbot platform with a custom data connector

**Fit:** You accept cloud processing and want quick deployment.

**Effort:** Days to weeks, plus the connector to SQL Server.

### Partner build such as Netray

**Fit:** You need on-prem hosting, customer scoping and ongoing tuning.

**Effort:** Scoped pilot on internal users first, then external.

## Pitfalls

- Putting customer scoping in the prompt instead of the query.
- Letting the bot answer from memory when a lookup fails.
- Indexing every file share, including obsolete procedures, which creates confident wrong answers.
- Launching to customers before testing on real, messy questions.
- Ignoring who owns the bot after launch, so answers go stale.

## Faq

### Can the chatbot show live VISUAL data?

**Answer:** Yes, by calling read-only lookups against the SQL Server database at question time. It does not need to copy the data elsewhere, although a reporting copy can reduce load on production.

### Is it safe to expose a bot to customers?

**Answer:** It can be, if every query is filtered by the authenticated customer in code, the bot is read-only, and you test for prompt injection. Start with internal users and add customers afterward.

### Which model should power it?

**Answer:** For structured lookups, a current open-weight model served locally is often adequate. For nuanced document questions or multilingual use, a larger hosted model may do better if your policy allows it.

### Does it need a new VISUAL version?

**Answer:** Generally no. Because it reads the database, it works on older installs too. Anything that creates records depends on what your version's toolkit supports.

### How is this different from Power BI Q&A?

**Answer:** Power BI works from a modeled dataset and charts. A chatbot can combine live lookups, documents and follow-up questions in conversation. Many plants use both for different jobs.

## Search terms

infor visual chatbot, ai chatbot for infor visual, infor visual customer portal chatbot, chat with infor visual data, infor visual natural language query, visual manufacturing chatbot, on-prem erp chatbot, infor visual order status bot, infor visual ai assistant

---
Talk to an engineer: https://netray.co/contact?intent=erp_ai | Machine catalog: https://netray.co/agent/catalog.json