# How to add an AI chatbot to JD Edwards EnterpriseOne > Source: https://netray.co/integrations/jd-edwards-ai-chatbot > Publisher: Netray (netray.co), AI and integration engineering for ERP and manufacturing systems. > Section: Integrations ## Short answer An AI chatbot for JD Edwards EnterpriseOne answers questions from live E1 data by calling read-only Orchestrator orchestrations through AIS and by searching your documents. It can live in a web portal, an E1 landing page or a chat tool, and the whole stack can run on-prem with an open-weight model. ## System JD Edwards EnterpriseOne ## Capability ai-chatbot ## Intro E1 users spend a lot of time navigating. To answer a customer, a CSR may open Work With Sales Orders, then Item Availability, then Supply and Demand Inquiry. A new buyer learns a dozen fast/path codes and still struggles to find a PO. A chatbot collapses that navigation into a question. The risk is a chatbot that sounds confident and is wrong. The fix is grounding: every answer is built from data fetched at question time, shows the records it used, and says so when it cannot find something. There is also a second audience. Suppliers and customers ask 'where is my order' all day. A narrow, external-facing bot can handle that without giving anyone an E1 login. ## Use Cases ### Internal 'where is my order' assistant **Description:** CSRs ask for sales order status, ship dates and tracking and get one answer from several lookups. ### Buyer helper for PO questions **Description:** Ask what is open with a supplier, what is late, or what is on order for an item. ### Shop-floor and supervisor assistant **Description:** Ask about work order status, parts shortages or routing steps from a tablet. ### Supplier and customer self-service **Description:** An external bot confirms PO receipt, shows own-order status and accepts a revised promise date for review. ### Procedure and how-to answers **Description:** Retrieval over your own work instructions, quality procedures and E1 user guides, so new staff learn your way of working. ## System Surface - AIS Server and Orchestrator orchestrations as the tool layer for live questions. - E1 landing pages, Composite Page or custom form extensions (depending on release) for embedding a chat panel; many teams use a separate web portal instead. - Document sources: SharePoint, file shares and the media object attachments in E1, indexed for retrieval. - Database read replicas or reporting views for analytical questions about history. - Identity: Oracle Identity Management, Entra ID or another SSO provider in front of both chat and AIS. ## How It Works ### Choose the audience **Detail:** Internal staff, suppliers or customers have very different security needs. Build internal first. ### Define the question set **Detail:** Collect real tickets and emails. Group them by intent and decide which are in scope for version one. ### Build read orchestrations **Detail:** One per intent, returning small, labelled data. Add UDC translation so statuses show as words. ### Add retrieval over documents **Detail:** Index procedures and guides with metadata such as plant and document owner, and require citations. ### Pick the front end **Detail:** A web chat inside your intranet, a Teams bot, or an E1 page. Authenticate users with SSO. ### Add guardrails and fallbacks **Detail:** Refuse out-of-scope questions, hand off to a person with the conversation attached, and never answer without data. ### Measure and iterate **Detail:** Track resolution rate, handoffs, wrong-answer reports and tool errors weekly. ## Example Flows ### Customer asks about order status **Trigger:** Customer types their order number into the portal chat. **Steps:** - Bot verifies the customer identity and order ownership. - Calls the sales order status orchestration. - Returns the status, scheduled ship date and carrier reference. - Offers to raise a request if the date is late. **Result:** A routine inquiry resolved without a phone call or an E1 login. ### New buyer learns the approval process **Trigger:** Buyer asks how to get a PO over a threshold approved. **Steps:** - Bot retrieves the purchasing procedure document. - It checks the buyer's open approvals through a read tool. - It answers with the steps and the document link. **Result:** Faster onboarding and fewer interruptions to senior staff. ## On Prem A fully on-prem chatbot is straightforward with E1 because the data lives in your database already. Host an open-weight model on your own GPUs with vLLM or Ollama, run the chat service and vector index inside your network, and call AIS locally. Smaller models handle status questions well; more complex reasoning over long documents may need a larger model, which changes your GPU budget. For external users, place only the chat front end in a DMZ and keep AIS and the model internal, with the bot calling through a narrow internal API. Many aerospace and defense sites choose this design to keep controlled data inside. ## Security - SSO for every user, and role-based tool access that mirrors E1 security. - For external bots, verify the user owns the order or supplier account before returning any data. - Bounded, labelled responses; no raw table dumps, no free-form SQL. - Prompt injection defences for user text and any documents indexed for retrieval. - Conversation logging with retention rules, and masking of personal data where needed. ## Options ### Generic chatbot platform with a thin E1 connector **Fit:** Works for FAQ-style bots and light status lookups with hosted models. **Effort:** 2 to 4 weeks. ### In-house build on Orchestrator and an open-source stack **Fit:** Teams with web and E1 skills who want full control. **Effort:** 6 to 10 weeks for internal use; longer for external-facing. ### Netray-built grounded assistant, on-prem if required **Fit:** When accuracy, citations and private deployment matter more than speed to a demo. **Effort:** A pilot in a few weeks. ## Pitfalls - Using retrieval over exported order data instead of live lookups, then answering with stale information. - Opening an external bot before ownership checks are airtight. - Letting the bot answer pricing questions without role checks. - Not providing a human handoff, so failures become complaints. - Choosing a model too small for the document questions you actually have. ## Faq ### Can I put a chatbot inside the E1 interface? **Answer:** Often yes, using custom pages, extensibility features or an embedded component, but exact options depend on your Tools release. Many teams find a separate intranet portal or a Teams bot quicker and easier to maintain. ### How do I stop it from inventing order data? **Answer:** Only answer from tool results, show the source record, and return a 'not found' message when a tool returns nothing. Test against real historical questions before launch. ### Can customers use it? **Answer:** Yes, with care. Authenticate them, verify they own the order, expose only narrow orchestrations and keep AIS internal. Start with internal users and add external access once logs show it behaves. ### Does it need a large GPU? **Answer:** For status lookups a mid-size open-weight model on one or two GPUs is often enough. Heavy document reasoning and many concurrent users raise the requirement. Size from a pilot, not from a guess. ### Is Oracle's own assistant enough? **Answer:** Possibly, if it covers your questions and your release supports it. Check what is included for your edition. Where you need your own documents, custom data or on-prem models, a purpose-built assistant is the usual route. ## Search terms jd edwards chatbot, ai chatbot for jd edwards enterpriseone, jd edwards virtual assistant, enterpriseone chatbot, jde chatbot integration, chat with jd edwards data, on-prem chatbot for jd edwards, jd edwards order status bot --- Talk to an engineer: https://netray.co/contact?intent=erp_ai | Machine catalog: https://netray.co/agent/catalog.json