How to add an AI chatbot to ProShop ERP
Short answer
An AI chatbot for ProShop answers questions from live ProShop data through its API, with retrieval over manuals and procedures, and shows the records behind each answer. It can run on a private open-weight model so shop data stays in your network.
A chatbot is the most visible way to give people access to ProShop data: operators asking what is next on a machine, estimators asking for similar jobs, a customer service person asking where an order is. The challenge is not the chat window. It is making answers trustworthy and keeping each user inside what they are allowed to see.
Two kinds of knowledge are involved. Live structured data, such as work order status, comes from the ProShop API at question time. Slow-changing documents, such as work instructions, quality manual sections and customer requirements, are better served through retrieval over an indexed library.
The honest warning is that a chatbot sitting on top of a good report is sometimes enough. Build one when the questions are varied, cross several records, or need text understanding, and not merely to display a number that a dashboard already shows.
What teams use it for
Operator what-is-next questions
At a terminal, an operator asks which jobs are queued for a work center and gets the list with due dates and special notes.
Customer service status answers
An office user types a customer's PO number and gets ship status and expected dates drafted into a reply.
Procedure lookup
Staff ask how to handle a first article, calibration due or nonconforming product, and get the relevant section of your own procedure with a link.
Estimator history search
Ask for past jobs on similar material and size and see hours and issues, with links back to the records.
New hire onboarding
A guided assistant explains terms, the traveler layout and where things live in ProShop, using your documents, not generic advice.
ProShop ERP integration surface
- ProShop API, GraphQL based in current releases with token authentication, supplies live answers. Verify endpoint and schema for your version with ProShop.
- Document sources for retrieval include your quality manual, work instructions and SOP library, typically as PDF or office files outside ProShop, plus text fields stored in ProShop.
- Identity: ProShop users and roles do not automatically follow into your chatbot. Use your own sign-in, such as Microsoft Entra ID, and map people to allowed data sets.
- Embedding the chatbot: as a web page linked from the shop's intranet or as a kiosk page on shop-floor terminals. Any embedding inside ProShop's own interface depends on what the vendor allows, so check.
- Exports and scheduled extracts can build a searchable history store without hitting the live API for every question.
How it works
- 1
Define audiences and scope
Decide who uses it and which questions are in scope. Operators and estimators need different tools and different data.
- 2
Build the live data tools
Wrap a few API queries as tools, with trimmed outputs and citations to record IDs.
- 3
Index the documents
Chunk procedures and manuals, keep version and effective date metadata, and exclude obsolete versions.
- 4
Pick the model and host
A hosted model is simplest. For controlled work, serve an open-weight model with vLLM or Ollama on your own GPU and test it on real questions.
- 5
Add sign-in and scoping
Authenticate users, then restrict tools and documents by group so a terminal operator cannot query pricing.
- 6
Require citations and a fallback
Answers must name the work order or document section. If nothing is found, the bot says so and points to a person.
- 7
Pilot and review transcripts
Review real conversations weekly, fix wrong answers, and add the missing tool or document.
Example flows
Operator asks about next job
Trigger: Operator types: what is next on the Haas VF-2?
- Bot identifies the work center from the kiosk.
- It calls the operations tool for queued work on that center.
- It lists the next jobs with due dates and setup notes.
Result: The operator sees the queue without leaving the machine area.
Procedure question
Trigger: Lead asks how to disposition a nonconformance on a customer-controlled part
- Bot retrieves the relevant procedure section and customer requirement.
- It summarises the required steps and approvals.
- It links the NCR form and cites the document versions.
Result: A consistent answer from controlled documents, not from memory.
Keeping data on-prem
The chatbot service, document index and model can all run on your own server or private cloud. The only outbound dependency is the call to the ProShop API, because ProShop is typically vendor-hosted. A modest GPU, or several, running an open-weight model is enough for a small shop's concurrent users, though quality depends on the model and the task, so evaluate on your own questions. Keep the document index inside your network so manuals and customer requirements are never copied to an outside service.
Security checklist
- Sign-in through your identity provider; no anonymous kiosk access to sensitive tools.
- Group-based limits on tools and documents, enforced in code.
- Citations required; unsupported answers refused.
- Conversation logs retained under policy with access limited to administrators.
- Treat free-text from records and documents as untrusted when building prompts.
Ways to get it done
Vendor-provided help or AI features in ProShop, if available
Effort: Minimal.
When your questions are about using the software; check what your plan includes.
Build with an open-source chat front end and your own tools
Effort: Two to six weeks for a useful pilot.
Teams with development help who want control of data and hosting.
Netray private assistant
Effort: Weeks, scoped by users and sources.
Shops that need on-prem hosting, document retrieval and ProShop data in one assistant.
Pitfalls
- !Indexing obsolete procedures, so the bot cites rules no longer in force.
- !No user scoping, exposing pricing or customer data on shared terminals.
- !Letting the bot answer from general knowledge when the data lookup fails.
- !Undersizing GPU capacity then judging the model by slow responses.
- !Skipping transcript review, so wrong answers repeat.
Frequently asked questions
Can the chatbot run inside ProShop's screens?
That depends on what the vendor supports for embedding or custom pages, which I cannot confirm for your version. The reliable alternative is a separate web page or kiosk linked from your intranet, authenticated with your own sign-in and calling ProShop through its API.
Does it need a GPU?
Only if you host the model yourself. A hosted model needs no GPU on your side. For private operation, a single workstation-class or server GPU can serve a small team with a mid-size open-weight model. Size it by concurrent users and test response times before committing to hardware.
How do we stop wrong answers?
Ground every answer in a tool result or a retrieved document, require citations, and make the bot say it does not know when nothing matches. Then review transcripts regularly. You cannot reach zero errors, so keep the records one click away.
Can customers use it?
Possibly, but treat it as a separate, stricter project. A customer-facing bot should see only that customer's orders, use authentication, and have a tightly limited tool set. Begin with internal staff first and expose customers later after the controls are proven.
Is a chatbot better than dashboards?
They solve different problems. Dashboards are best for numbers you watch daily. A chatbot is better for ad hoc questions, text search and cross-record reasoning. Many shops use both, with the chatbot linking back to the dashboard or record as the source.
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