How to use ChatGPT with Made2Manage
Short answer
ChatGPT cannot reach an on-prem Made2Manage database by itself. You either paste or upload exports for one-off analysis, or put a small read-only API in front of the data and connect it as a custom GPT action or app. For customer drawings and cost data, many shops prefer a private model instead.
Plenty of Made2Manage users already paste a shortage list or a job cost report into ChatGPT and ask for a summary. It works, and it is the fastest start. The limits show quickly: the data is a stale snapshot, someone has to export it, and it is easy to upload more than policy allows.
The next step is live access: a question like 'which open jobs for customer X are waiting on material' answered from current data. That needs a thin service between ChatGPT and your SQL Server, and a decision about what you are willing to send to a third party.
This page lays out the three levels: manual exports, a custom GPT with actions, and a private alternative, so you can pick the lowest-risk one that solves your problem.
What teams use it for
Explain a cost variance
Upload a job's estimated versus actual report and ask where hours and material drifted, then get a plain-language explanation for a review meeting.
Clean up an item master export
Paste duplicate-looking part descriptions and ask for normalised names and likely duplicates, which a person confirms before any edit.
Draft supplier and customer emails
Give ChatGPT a late PO line or a delayed order and have it draft a polite, specific email, using only the fields you provide.
Write SQL or Crystal Reports ideas
Describe a report you want and get a draft query to review against your schema. Never run it unreviewed on production.
Summarise a quality history
Feed a de-identified set of rework notes and ask for recurring root causes by operation.
Made2Manage integration surface
- No direct connection exists between ChatGPT and an on-prem Made2Manage. You must supply data by export or through an API you host.
- Reports and screens can usually export to CSV or Excel, which is the lowest-friction manual path.
- A read-only SQL Server login behind your own small web API (for example a REST service exposing a few endpoints) is the usual live path. Names and schema vary by version.
- Custom GPT actions require an HTTPS endpoint with an OpenAPI description, so the service must be reachable, which is a real network decision for on-prem shops.
- Check what your Aptean agreement and version offer for official APIs before building your own.
How it works
- 1
Decide the data classification
List what is allowed to leave: part numbers, prices, customer names, drawings. Write it down before anyone uploads.
- 2
Start with manual exports
Use reports exported to CSV and a ChatGPT plan with the data controls your policy requires. Treat output as a draft.
- 3
Build a narrow read-only API
Expose two or three endpoints such as order status by sales order and open lines by part, with row limits and a service account.
- 4
Describe it as an action
Write an OpenAPI spec and attach it to a custom GPT with clear instructions about when to call each endpoint.
- 5
Secure the endpoint
Use an API key or OAuth, restrict by IP where possible, and put the service in a DMZ rather than on the ERP server.
- 6
Review usage and decide on private hosting
If the useful questions all involve sensitive data, move the same API to a private model instead.
Example flows
Monthly variance review prep
Trigger: Controller exports the job cost report to CSV.
- Remove customer names if policy requires.
- Upload to ChatGPT and ask for the ten largest overruns with likely causes.
- Cross-check each claim against the source rows.
Result: A first-draft talking sheet in minutes, verified by the controller.
Order status custom GPT
Trigger: A salesperson asks the GPT about a sales order.
- The GPT calls your status endpoint with the order number.
- The service queries SQL Server read-only and returns a compact JSON.
- The GPT answers in plain text and cites the fields it used.
Result: Salespeople self-serve status without calling the office.
Keeping data on-prem
If data cannot go to a third-party cloud, ChatGPT is the wrong endpoint, not the wrong idea. Keep the same pattern, a read-only API plus a chat interface, but serve an open-weight model locally with vLLM or Ollama and a small internal chat page. Your SQL Server stays on the LAN, the model runs on a GPU box beside it, and no part number or price crosses the firewall. You lose some raw model strength compared with the largest hosted models, which is an honest trade-off, but for lookups, summaries and drafting a mid-size open model is usually sufficient.
Security checklist
- Use enterprise or business plans with training opt-out and retention terms reviewed by your security lead.
- Never upload export-controlled drawings or technical data to a consumer account.
- Give any API a dedicated read-only account and expose only the fields needed.
- Authenticate every call, rate-limit, and log requests.
- Remember that anything the GPT can read, any user of that GPT can ask for; scope by audience.
Ways to get it done
Manual exports into ChatGPT
Effort: Hours; no engineering.
One-off analysis with non-sensitive data and no integration budget.
Custom GPT with a small API
Effort: A few weeks including security review.
Repeatable lookups for staff who already use ChatGPT, and data you are cleared to share.
Private model via a partner such as Netray
Effort: A scoped pilot over several weeks.
Cost, customer or controlled data must stay in your network.
Pitfalls
- !Uploading a full cost file to a personal account because it was convenient.
- !Exposing the API to the internet without authentication to make the GPT work.
- !Assuming the GPT's answer is right when the export was filtered or out of date.
- !Letting the GPT infer business rules (like costing method) that it cannot see.
Frequently asked questions
Can ChatGPT connect directly to Made2Manage?
No. There is no built-in connector to an on-prem Made2Manage. You either export data manually or host a small API that ChatGPT can call. Which is right depends on how sensitive the data is and whether you can safely expose an endpoint.
Is it safe to upload Made2Manage reports?
It depends on the plan and the data. Business and enterprise plans offer data controls, but you must still follow your own policy and any customer contracts. Do not upload export-controlled or customer-restricted material to a public service.
What is a custom GPT action in this context?
It is a described API call the GPT can make during a chat. For Made2Manage that means your own HTTPS service returning order, job or inventory data. The GPT decides when to call it based on your instructions and the OpenAPI description.
Why would we choose a private model instead?
Mainly data control. Cost rates, customer pricing and drawings are often sensitive. A locally hosted open-weight model gives the same chat-over-data experience with nothing leaving your network, in exchange for hardware and some capability trade-offs.
Can ChatGPT update jobs or POs?
It should not, at least at first. Writes can bypass ERP business rules. Keep ChatGPT read-only and let it draft messages or change requests that a person enters or approves.
Want ChatGPT working on your Made2Manage?
A free 30 minute assessment: we map the integration path for your version and setup, and where it should run.
Get a free assessment