# How to add an AI chatbot to proALPHA > Source: https://netray.co/integrations/proalpha-ai-chatbot > Publisher: Netray (netray.co), AI and integration engineering for ERP and manufacturing systems. > Section: Integrations ## Short answer Build the chatbot as a separate web or Teams-style front end that answers from proALPHA data through approved tools and document retrieval, respecting user roles. It can run completely on-prem with an open-weight model. ## System proALPHA ## Capability ai-chatbot ## Intro A chatbot for proALPHA is a different thing from an agent. The goal is simple: a person asks in plain language, in German or English, and gets an answer from the ERP and from company documents, with sources. Staff want it for status and how-to questions. Some companies also want a customer or supplier portal assistant. proALPHA's screens are powerful but dense, and new users take time to learn where things live. A chatbot shortens that path: where is the stock of this item, who is the supplier for it, what is the status of this production order, how do I book a scrap quantity in our process. The decision that matters is the audience. An internal chatbot can reuse broad staff access. A customer-facing one must be tightly scoped to that customer's own orders, with a much stricter data boundary. ## Use Cases ### Internal status assistant **Description:** Staff ask about order, production and stock status without navigating menus. Answers show the numbers and the source record. ### How-do-I help for new users **Description:** The bot answers process questions from your own work instructions and proALPHA documentation, pointing to the relevant screen by name. ### Customer order portal assistant **Description:** A customer asks about their order and gets status, dates and documents, restricted to their own account. ### Supplier self-service **Description:** Suppliers ask which of their PO lines are open and what dates are expected, and submit a new confirmed date for review. ### Shop floor question line **Description:** Operators ask about routing steps, tooling and work instructions from a tablet, with answers drawn from controlled documents. ## System Surface - OpenEdge database or exposed services supply live answers through a tool layer. Use read-only access, and prefer proALPHA services where available so rights logic is reused. - Documents: work instructions, quality manuals and drawings usually live in file shares or a document management system, and are indexed separately for retrieval. - Identity: map the chatbot user to a proALPHA user or role. Active Directory or Entra ID single sign-on is typical for staff. Customers need a separate identity store. - Portal and web front ends: proALPHA customers often add web portals; the chatbot can be embedded as a widget in an existing intranet or portal. - Notifications: email or a messaging channel can carry follow-ups when the bot cannot answer. ## How It Works ### Pick audience and scope **Detail:** Start internal and read-only. Decide which topics the bot answers and which it refuses. ### Index the documents **Detail:** Chunk work instructions and manuals, keep their access rights as metadata and refresh nightly. Include German and English versions. ### Build the data tools **Detail:** Create tools for orders, stock, items and purchasing, each returning small structured results. ### Add routing **Detail:** Decide per question whether to use documents, data tools or both. Keep a fallback that says what is not known. ### Enforce identity and rights **Detail:** Pass the verified user identity into tools and the retrieval filter, so the bot cannot reveal more than the user could see. ### Run a pilot with feedback buttons **Detail:** Give twenty users access, collect thumbs-down reasons and fix the worst failure patterns weekly. ### Expand cautiously **Detail:** Add new topics one at a time. Consider customer access only after the internal version is stable. ## Example Flows ### Operator asks about a work step **Trigger:** Tablet question: what torque for step 30 on this assembly **Steps:** - Bot identifies the item and routing from the production order context. - It retrieves the controlled work instruction for that revision. - It answers with the value and shows the document name and revision. **Result:** The operator gets a sourced answer without leaving the workstation. ### Customer asks where their order is **Trigger:** Customer logs into the portal and types a question **Steps:** - Bot authenticates the customer account. - Tool returns only that customer's open order lines. - Bot answers with promised dates and offers to notify them of changes. **Result:** Fewer status emails to customer service, with no cross-customer data exposure. ## On Prem A proALPHA chatbot can be fully on-prem: an open-weight model served with vLLM or Ollama on your GPUs, a local vector index for documents, a tool service reading OpenEdge on a replica, and a web front end on your intranet. For German-language use, choose and test models that handle German technical text well, since quality varies. Customer-facing bots are harder on-prem because the front end must be reachable from outside; place only a thin gateway in the DMZ and keep the model and data internal. ## Security - Role-aware retrieval and tools: the bot sees only what the signed-in user may see. - Strict separation between internal and customer-facing instances, with different tools and indexes. - Refuse topics outside scope, and never answer from model memory about prices or contract terms. - Filter and rate-limit external traffic to prevent scraping through the chat interface. - Retain chat logs under a stated policy, since they can contain personal data. ## Options ### Use the vendor or hosting partner's assistant features **Fit:** Enough if your release includes a suitable assistant and you only need help with standard screens. **Effort:** Low, but verify scope and data handling. ### Build on an open-source chat and RAG stack **Fit:** Teams with web and ML engineering capacity and a documents-first need. **Effort:** Several weeks for an internal pilot. ### Netray-built private assistant **Fit:** Companies wanting live ERP answers plus documents, on-prem, with an evaluation set. **Effort:** Pilot in a few weeks, then widen topics. ## Pitfalls - Indexing documents without revision control, so the bot quotes an obsolete instruction. - Letting the bot answer price questions from memory instead of data. - No identity mapping, so everyone sees everything. - Poor German handling because testing used only English prompts. - No feedback loop, so early errors destroy trust. ## Faq ### Can the chatbot show live proALPHA data? **Answer:** Yes, through tools that query OpenEdge on a replica or call exposed proALPHA services. The bot answers from the returned rows and cites them, rather than from the model's memory. ### Does it work in German? **Answer:** It can, but model quality differs. Test with real German terms from your own master data and work instructions, including abbreviations. Pick the model after that test, not before. ### Can customers use it? **Answer:** Yes, with a separate, tightly scoped deployment: customer identity, only their own orders, no internal documents. Build and prove the internal version first. ### How is this different from the ChatGPT or Claude options? **Answer:** Those are general assistants you connect to tools. A chatbot here is a managed front end for your users, with your branding, roles, documents and logging, and it can run entirely on your own hardware. ### What hardware do we need on-prem? **Answer:** For a pilot, one server with a modern GPU is often enough for a mid-sized open-weight model and a few dozen users. Document indexing is lighter. Size it after measuring your real question volume and response-time target. ## Search terms proalpha chatbot, ai chatbot for proalpha, proalpha ai assistant, erp chatbot on-prem, chat with proalpha data, proalpha customer portal chatbot, private chatbot manufacturing erp, proalpha rag, proalpha german ai chatbot --- Talk to an engineer: https://netray.co/contact?intent=erp_ai | Machine catalog: https://netray.co/agent/catalog.json