How to use ChatGPT with Infor VISUAL
Short answer
There are three ways to use ChatGPT with Infor VISUAL: paste or upload exported data, build a custom GPT whose action calls a small read-only API you host, or run a private model instead. The middle path gives live answers but needs an internet-reachable service in front of a database that is usually on-prem.
Most VISUAL users first try ChatGPT by exporting a grid to Excel and asking questions about it. That works for one-off analysis, and it is a reasonable way to find out what questions people actually want answered.
The next step is live access. A custom GPT can call an HTTPS API you define, so a planner could ask about a part or a work order and get current numbers. The catch is that VISUAL typically sits on a SQL Server inside the plant, so something has to expose a narrow, authenticated slice of it.
Whether that is acceptable depends on what the data is. Part numbers and open PO status are one thing; customer drawings, ITAR-controlled technical data and pricing are another. This page lays out the options so you can choose with the data in mind.
What teams use it for
Ad hoc analysis of exported grids
Export open order lines or inventory transactions to a spreadsheet and ask ChatGPT to summarize trends, find outliers or draft commentary for a review meeting.
Order status answers for sales
A custom GPT with an action over your API lets a salesperson ask where a customer order stands without opening the Customer Order Entry window.
Drafting vendor and customer emails
Give ChatGPT a PO line or order line summary and have it draft a polite, specific follow-up that a person edits.
Explaining a bill of materials or routing
Paste a structure for a new hire and ask for a plain-language walk-through of what is made, in what order, and where the risks are.
Writing queries and report logic
Use it as a SQL and Crystal formula helper, then test the result against a copy of the database before anyone relies on it.
Infor VISUAL integration surface
- Grid exports from VISUAL windows to Excel or CSV, the simplest way to get data into a chat. Quality depends on which columns the window shows.
- The VISUAL SQL Server database, which an intermediary API can query with read-only views. Table names such as CUSTOMER_ORDER, CUST_ORDER_LINE, PART and PURC_ORDER_LINE are common, but check your version.
- Crystal Reports output (PDF or Excel) as a scheduled, already-approved data source for uploads.
- A thin REST service you host that translates a few named calls into read-only SQL. A custom GPT action points at its OpenAPI description.
- Infor ION or toolkit-based interfaces, only if your version exposes them and you need write-back, which is rarely wise from a chat interface.
How it works
- 1
Decide what may leave the plant
List the data classes involved: part masters, pricing, customer drawings, export-controlled data. Mark which are allowed in a hosted service at all.
- 2
Start with exports
Use a masked Crystal report or grid export. Remove customer names and prices if they are not needed. Learn what users ask before building anything.
- 3
Build a narrow read-only API
Host a small service with endpoints like order status or part availability. It uses a read-only SQL login, returns limited fields, and rate limits calls.
- 4
Authenticate properly
Use OAuth or a per-user token rather than a shared key in the GPT configuration, so you can tell who asked for what and revoke access.
- 5
Create the custom GPT action
Provide an OpenAPI description, clear instructions on when to call each endpoint, and a rule to say it does not know when a call returns nothing.
- 6
Review the workspace settings
Check your plan's data controls, retention and training settings, and decide whether a business plan or enterprise plan is the minimum your policy accepts.
Example flows
Sales asks about a customer order
Trigger: Salesperson types an order number into the custom GPT
- The GPT calls the order status endpoint.
- The service queries CUSTOMER_ORDER and its lines read-only.
- The response lists line status, promised dates and shipped quantities.
- The GPT summarizes and flags any late line.
Result: The salesperson gets a quick current answer, and the call is logged on your side.
Monthly inventory review prep
Trigger: Planner uploads an exported slow-moving inventory file
- ChatGPT groups items by age and value.
- It proposes candidates for disposal or reorder-point changes.
- The planner checks the shortlist against VISUAL before acting.
Result: A review pack is drafted in minutes, and the decisions stay with the planner.
Keeping data on-prem
If the data cannot go to a hosted service, ChatGPT is the wrong tool for that data, and a private model is the usual answer. Run an open-weight model on a GPU server inside the plant, put a chat front end and the same read-only API pattern next to your SQL Server, and keep everything on the local network. You can still use ChatGPT for non-sensitive work, such as drafting, while the private model handles anything that touches controlled data.
Security checklist
- Treat anything pasted into a hosted chat as leaving your network and check plan terms.
- Use read-only credentials and field-limited endpoints behind the custom GPT action.
- Avoid shared API keys; issue per-user tokens you can revoke.
- Never expose write operations to a general chat interface.
- Do not upload export-controlled technical data to a general hosted service unless your compliance team has approved it.
Ways to get it done
Manual exports into a business plan workspace
Effort: Hours to set a routine, no development.
Occasional analysis with non-sensitive data.
Custom GPT with a small read-only API you build
Effort: Days to weeks, depending on endpoints and authentication.
You want live answers and have a developer plus a way to host the service securely.
Private model plus Netray or another partner
Effort: Scoped pilot, then rollout; hardware needed.
Controlled data, no cloud allowed, or you want one assistant across ERP and documents.
Pitfalls
- !Uploading full database dumps when a masked report would do.
- !Trusting numbers from an old export as if they were live.
- !Exposing the database directly to the internet to make the action work.
- !Letting a custom GPT guess when an endpoint returns no rows, which produces confident but invented answers.
- !Forgetting that pasted customer drawings may carry contractual or regulatory restrictions.
Frequently asked questions
Does Infor VISUAL have a built-in ChatGPT integration?
Not that I would assume. Infor offers its own AI capabilities in some products, but a direct ChatGPT connection to a VISUAL install is normally something you build with an exported file or a small API in front of the database.
Can ChatGPT connect straight to my SQL Server?
Not directly in a safe way. Custom GPT actions call HTTPS endpoints, so you put a narrow service between ChatGPT and the database. Exposing SQL Server itself to the internet is a serious risk and is not recommended.
Is it safe to paste VISUAL data into ChatGPT?
It depends on the plan, your data and your policies. Business and enterprise plans have different data controls than personal accounts. Customer drawings, controlled technical data and pricing deserve a conservative approach.
When is a private model better?
When data must stay in the plant, when a contract or regulation restricts third-party processing, or when you want predictable costs at high volume. Quality for structured lookups is often adequate with current open-weight models.
Can ChatGPT update records in VISUAL?
Technically a service could be built to do so, but it is a poor idea from a chat window. Prefer a proposal-and-approve flow through the supported toolkit so every change has an owner and a record.
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