How to use ChatGPT with ProShop ERP
Short answer
You can use ChatGPT with ProShop in three ways: paste or upload exported data, build a custom GPT whose actions call a small proxy over the ProShop API, or use an approved connector. For export-controlled or customer-restricted data, a privately hosted model is usually the safer route.
Many ProShop shops already have staff using ChatGPT on the side: drafting emails to customers, summarising a long spec, or turning a pile of inspection notes into a report. The next step is connecting it to real shop data so answers about jobs, parts and POs are grounded rather than guessed.
There is a spectrum. At one end is manual export and upload, which is quick and has no integration cost but goes stale immediately. At the other is a live connection through an API, which gives current answers but needs authentication, scoping and a decision about what data is allowed to leave your boundary.
The deciding question is rarely technical. It is whether the data in your ProShop instance includes customer drawings, export-controlled technical data or contractually restricted information. If it does, the data-handling terms of the model service matter more than any feature.
What teams use it for
Summarise a work order history
Upload an exported traveler or work order notes and ask ChatGPT for a plain summary of what happened, which operations ran long and where rework occurred.
Customer update drafts
Give ChatGPT the status fields of a job and have it draft a short status email in your tone for the account manager to review.
Quote assist from past jobs
Provide a table of past similar parts with actual hours and ask for a reasoned starting estimate and the assumptions behind it.
Custom GPT for job lookups
A custom GPT with an action pointing at a small proxy lets planners ask where a job is and get a live answer drawn from the ProShop API.
Spec and PO clause review
Paste a customer purchase order's flow-down clauses and ask ChatGPT to list obligations such as certs, record retention and sub-tier requirements.
ProShop ERP integration surface
- ProShop's API, GraphQL based in current releases with token authentication, is what a custom GPT action would ultimately call. Verify endpoint and schema details for your version with ProShop support.
- A custom GPT action expects an OpenAPI description, so you normally put a small proxy between ChatGPT and ProShop that exposes a few simple read endpoints and hides the GraphQL complexity and the credentials.
- Built-in reports and exports, such as spreadsheet or CSV style outputs, support the manual upload route and avoid any live connection.
- Inspection and quality records, work orders, parts and POs are the objects people most often ask about, so design the proxy around those.
- Role-based access in ProShop applies to the API user, not to the person chatting, so your proxy must enforce who may ask what.
How it works
- 1
Classify your data first
List which ProShop data is export-controlled, customer-restricted or sensitive pricing. This decides if ChatGPT is allowed at all.
- 2
Choose a plan with business data terms
Use a business or enterprise offering whose terms exclude training on your data, and review admin, retention and region options. Verify current terms with OpenAI directly.
- 3
Start with exports
Pilot with sanitised exports to learn which questions are useful before you build anything live.
- 4
Build a read-only proxy
Create a small service exposing endpoints like job status and part lookup, calling the ProShop API with a restricted API user.
- 5
Define the custom GPT action
Write an OpenAPI schema for the proxy, add authentication, and write instructions telling the GPT to cite fields and never guess.
- 6
Control sharing
Limit the GPT to named users, and make the proxy check the caller identity before returning data.
Example flows
Where is my job?
Trigger: A planner asks the custom GPT about a work order number
- GPT calls the proxy action with the number.
- Proxy queries ProShop and returns status, current operation and due date.
- GPT answers in a sentence and lists the fields used.
Result: A fast lookup without opening several screens, with the source fields visible.
Weekly review document
Trigger: Ops manager uploads a late jobs export every Friday
- ChatGPT groups jobs by cause using notes and status.
- It drafts a review summary and open questions per customer.
- Manager edits and circulates.
Result: A review pack prepared in minutes, built only from the file that was uploaded.
Keeping data on-prem
ChatGPT is a hosted service, so a fully on-prem posture means not using it for the sensitive data. The usual compromise is a split: use ChatGPT for non-controlled tasks such as drafting and general analysis, and use an open-weight model on your own hardware for work touching drawings, technical data or restricted customer information. A private assistant can offer a similar chat experience over the same proxy, so staff do not need to learn two tools. Since ProShop itself is generally vendor-hosted, you are already moving some data through the cloud, so document exactly which flows exist and who approved them.
Security checklist
- Never upload export-controlled technical data to a service not cleared for it.
- Use business plans with no-training terms and admin controls; verify terms yourself.
- Proxy uses a least-privilege ProShop API user and enforces per-user checks.
- Log every action call, including who asked and what was returned.
- Redact pricing and customer identifiers when the task does not need them.
Ways to get it done
Manual export and upload
Effort: Hours.
Fastest start and good for drafting and summaries on non-sensitive data.
Custom GPT plus your own proxy
Effort: One to three weeks for a minimal proxy.
Teams wanting live job lookups with a controlled, read-only surface.
Private model assistant from a partner such as Netray
Effort: Weeks, scoped per use case.
Shops with controlled data that still want a chat interface over ProShop.
Pitfalls
- !Uploading drawings or controlled data to a personal account.
- !Treating an export as live; answers go stale the moment the file is saved.
- !Giving the GPT action broad API credentials rather than a narrow proxy.
- !Letting the GPT answer without citing records, so errors look confident.
- !Assuming ProShop role permissions follow the chat user automatically; they do not.
Frequently asked questions
Is there an official ChatGPT connector for ProShop?
I am not aware of one you should rely on, and availability changes quickly. Check the ProShop integrations area and the ChatGPT connector directory. If none exists, a custom GPT action over a small proxy of the ProShop API is the common approach.
Can ChatGPT see my ProShop data directly?
No. It only sees what you upload or what an action you built returns. That is useful, because you decide the exact fields exposed. Design the proxy to return only what the question needs rather than whole records.
Is ChatGPT acceptable for ITAR or CUI data?
Do not assume it is. Whether any hosted service is acceptable depends on its authorisations and your contract. Many shops keep controlled data on infrastructure they control and use hosted tools only for non-controlled work. Ask your compliance lead before any upload.
What is the difference between a custom GPT and the API?
A custom GPT is a configured chat experience inside ChatGPT with optional actions. The OpenAI API lets you build your own application with your own interface and controls. The API gives more control over logging and workflow, while a custom GPT is quicker for simple lookups.
Will it replace ProShop reports?
No. Reports are repeatable and exact, which is what you want for numbers. ChatGPT is better for reading text, comparing records and drafting. Use it alongside reports, and verify figures against the system of record.
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