# How to add an AI chatbot to DELMIAworks (IQMS)

> Source: https://netray.co/integrations/delmiaworks-ai-chatbot
> Publisher: Netray (netray.co), AI and integration engineering for ERP and manufacturing systems.
> Section: Integrations

## Short answer

An AI chatbot for DELMIAworks answers questions in plain language from live ERP data and plant documents, inside a web page, Teams or a customer portal. Build it on curated read-only views, add retrieval over SOPs, and run the model on your own servers if data must stay on-prem.

## System

DELMIAworks (IQMS)

## Capability

ai-chatbot

## Intro

DELMIAworks screens are dense. A supervisor wanting to know whether a job will ship Friday may need the order form, the work order, the press schedule and the material view. A chatbot collapses that into one question, and it can also serve people who never log into the ERP: sales, customers and executives.

The difference between a chatbot that earns trust and one that gets switched off is grounding. Every answer must come from a query or document the user can see, and the bot must say when it does not know.

Here is how to design that for an IQMS-based plant, including the on-prem path.

## Use Cases

### Supervisor job questions

**Description:** Ask 'Will the housing job on press 9 finish by Thursday?' and get status, quantity remaining, cycle rate and a clear answer.

### Customer order portal assistant

**Description:** Customers ask where their order is and see ship dates and tracking drawn from their own orders only.

### Sales quick answers

**Description:** Reps ask about stock, lead time and last price for an item without calling customer service.

### Operator SOP help

**Description:** An operator asks how to do a color change on a given press and gets the controlled work instruction with a link.

### Buyer questions on suppliers

**Description:** Ask for on-time delivery history and open lines for a supplier, with the PO list attached.

### Executive daily brief

**Description:** A scheduled message summarizing shipments, late orders and scrap by plant, with the ability to ask follow-ups.

## System Surface

- Oracle read views: the main source of grounded answers for orders, inventory, purchasing, work orders and MES events.
- Vendor web services or a customer portal module: where available, these provide a supported path for external users to see their own data.
- Document sources: controlled SOPs, work instructions and quality procedures from file shares or a document system, indexed for retrieval.
- Authentication: Active Directory or single sign-on for staff, and a separate portal identity for customers, mapped to customer IDs.
- Exact tables and modules vary by version and licensed options, so map them for your site.

## How It Works

### Define who asks what

**Detail:** List the top thirty questions by role. Staff, customers and executives need different data and different tone.

### Build the data tools

**Detail:** Wrap read-only views in parameterized functions the chatbot can call. Avoid letting the model write SQL.

### Index documents

**Detail:** Chunk SOPs and procedures, store embeddings in an on-prem vector store, and keep document version and approval status as metadata.

### Compose the bot

**Detail:** A router decides between data tools and document retrieval, and the answer always shows source tables or document titles.

### Apply identity and scope

**Detail:** Pass the user's role into every tool so customers see only their orders and staff see their plants.

### Choose the surface

**Detail:** Embed a chat panel in an intranet page, a customer portal or Teams, rather than modifying the ERP client.

### Measure and tune

**Detail:** Track unanswered questions and thumbs-down answers weekly and fix the tools behind them.

## Example Flows

### Customer asks about a shipment

**Trigger:** Customer types 'When will PO 55102 ship?' in the portal.

**Steps:**

- Bot identifies the customer from login.
- It calls the order tool with the PO filtered to that customer.
- It reads promise date, quantity shipped and the next planned ship.
- It answers with the date and offers to notify on change.

**Result:** Customer service receives fewer status calls and the customer sees only their own data.

### Operator asks for a procedure

**Trigger:** Operator on press 6 asks how to purge before a color change.

**Steps:**

- Bot searches the approved SOP index for the press and process.
- It returns the current approved procedure step by step.
- It links the full document and shows revision date.

**Result:** The operator follows the controlled instruction rather than an outdated printout.

## On Prem

A chatbot for an on-prem DELMIAworks plant can be fully self-contained: an open-weight model served with vLLM or Ollama on a GPU server, an embedding model and vector store on the same network, and tools that connect to Oracle read views. Nothing needs to leave the plant. For customer-facing access, publish only the chat front end through a DMZ or reverse proxy, with the model and database behind it, and limit tools to the customer-scoped ones. Smaller models are often adequate for status lookups, with larger ones reserved for document questions.

## Security

- Resolve every user to a role or customer ID and apply it in each tool query.
- Keep customer-facing and internal tools in separate toolsets.
- Show sources in answers and refuse to answer when no source supports it.
- Filter out cost, margin and supplier pricing from non-finance roles.
- Log conversations with retention rules, and tell users they are logged.

## Options

### Vendor portal or built-in features

**Fit:** If your release has a customer portal and basic alerts, they may meet simple status needs.

**Effort:** Configuration only.

### Build in-house

**Fit:** Possible with a developer who knows retrieval and your schema.

**Effort:** A month or more for a reliable first version.

### Partner such as Netray

**Fit:** For on-prem models, role scoping and evaluation handled together.

**Effort:** A few weeks to a pilot.

## Pitfalls

- Letting the bot answer from general knowledge when ERP data is missing, producing confident wrong dates.
- Exposing internal fields in a customer-facing bot because the tools were shared.
- Indexing superseded SOPs and returning obsolete procedures.
- No fallback to a human, so frustrated users abandon the bot.
- Skipping latency work: answers over thirty seconds will not be used.

## Faq

### Can the chatbot live inside the DELMIAworks screen?

**Answer:** Embedding in the desktop client is usually impractical. Place the chat in a web page, Teams or a portal next to the ERP, and deep link into DELMIAworks records where your setup allows it.

### How do I stop it making things up?

**Answer:** Ground every answer in a tool result or document passage, display the source, and instruct the bot to say it does not know when none is found. Test with questions that have no answer.

### Can customers use it?

**Answer:** Yes, with a separate customer-scoped toolset, authenticated identity and strict filtering by customer ID. Keep it in a separate deployment from internal tools.

### What hardware does an on-prem bot need?

**Answer:** A single modern GPU server can run a mid-size open-weight model for a plant's chat load, and smaller models run on less. Size it from concurrent users and acceptable response time, then test with real questions.

### Will it work in other languages?

**Answer:** Many open models handle major languages, but quality varies. Test your languages on real plant vocabulary, such as part descriptions and defect names, before committing.

## Search terms

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