# How to use ChatGPT with ECI JobBOSS2

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

## Short answer

You can use ChatGPT with JobBOSS2 in three ways: paste or upload exports, build a custom GPT with an action that calls an API you control, or connect a tool layer through a ChatGPT connector. Each trades convenience against how much shop data leaves your network.

## System

ECI JobBOSS2

## Capability

chatgpt

## Intro

Many JobBOSS2 users already use ChatGPT informally: pasting a customer email to draft a reply, or asking it to explain a spreadsheet exported from a report. That is useful but manual, and the data that reaches the model is whatever someone remembered to copy.

The next step is giving ChatGPT a controlled way to ask JobBOSS2 questions itself. Because JobBOSS2 sits on SQL Server, often inside the shop's own network, the question is not whether it is possible but where the boundary goes: what is exposed, to whom, and under what contract with the model provider.

This page walks through the options in order of risk, and is honest about when a private model is the better fit.

## Use Cases

### Explain a report to a non-expert

**Description:** Upload a job cost or WIP export and ask where the margin went. Good for owners who do not live in the system.

### Draft customer and vendor emails

**Description:** Give ChatGPT a job's status fields and ask for a customer update. No live connection needed, just a copied block of text.

### Custom GPT for order status

**Description:** A custom GPT with an action calling a small internal endpoint, so the sales team can ask 'where is job 12345' and get the status.

### Summarise quote history for an RFQ

**Description:** Export past quotes for one customer and ask for pricing patterns and win/loss clues to prepare a response.

### Translate and standardise routings

**Description:** Convert free text process notes into consistent operation descriptions before loading them back through an import.

## System Surface

- Exports: JobBOSS2 reports can typically be sent to Excel or PDF, and Crystal or SSRS reports built on the SQL Server database can be exported as CSV. This is the lowest-risk path.
- SQL Server read views: you can build a small web service (REST) over read-only views. A custom GPT action can then call that service via an OpenAPI schema.
- The JobBOSS2 API, where your edition provides one, can serve as the service behind the action. Confirm available endpoints and authentication with ECI.
- Scheduled extracts to a staging database or file share let you keep ChatGPT away from the production server entirely.
- Table names such as Job, Job_Operation, Material_Req and Delivery are common but should be verified for your version.

## How It Works

### Classify your data

**Detail:** Decide which JobBOSS2 data is public to the model, which needs masking (customer names, prices), and which is off limits (export-controlled part data).

### Start with exports

**Detail:** Use ChatGPT Enterprise or Business workspace settings that exclude your data from training, and let a small group try exported reports for a few weeks.

### Build a read-only service

**Detail:** Write a small HTTPS service over SQL views. Return only the fields needed. Add per-user API keys or OAuth.

### Describe it with OpenAPI

**Detail:** Create a schema with clear operation names and descriptions. A custom GPT action uses this to know when and how to call it.

### Publish the GPT only to your workspace

**Detail:** Do not make it public. Limit to named users or groups.

### Add logging and limits

**Detail:** Log every call with the user, parameters and row count. Cap results per call so a prompt cannot dump a whole table.

## Example Flows

### Customer asks about a delivery

**Trigger:** A salesperson opens the company custom GPT and types a job number.

**Steps:**

- The GPT calls the get_job_status action.
- The service queries a read view and returns status, next operation and promised ship date.
- ChatGPT writes a reply for the salesperson to review.

**Result:** A consistent, accurate update in under a minute, still sent by a person.

### Monthly margin review

**Trigger:** The owner exports a closed-jobs cost report to Excel.

**Steps:**

- Upload the file to a ChatGPT workspace chat.
- Ask for jobs where actual hours exceeded estimate by a wide margin.
- Ask for common factors: customer, material, operation.

**Result:** A short list of estimating issues to fix, without any live connection.

## On Prem

If part data or customer terms forbid sending information to a third party, ChatGPT is the wrong tool for the live link. Keep the same pattern but swap the model: run an open-weight model on a server in your network, put a chat interface in front of it, and point it at the same read-only service. Users get an experience similar to a custom GPT, but prompts and rows never leave. Some shops use ChatGPT only for non-sensitive tasks (email wording, general questions) and a private model for anything that touches JobBOSS2 records.

## Security

- Use a business or enterprise workspace with data-training exclusion, and review the provider's data retention terms with your counsel.
- Never give the action write access. Read-only endpoints with narrow, named operations only.
- Authenticate each user so the service can apply role-based filtering, not a single shared key.
- Strip or tokenise sensitive fields such as customer names and unit prices if users do not need them.
- Keep the service on a restricted network zone and review its logs regularly.

## Options

### Manual exports into ChatGPT

**Fit:** Fine for ad hoc analysis and drafting with low-sensitivity data.

**Effort:** Hours

### Custom GPT with a read-only action

**Fit:** Good for sales and customer service questions where live status matters and data can go to a vendor.

**Effort:** 1 to 3 weeks including security review

### Private model with the same interface, via Netray or in-house

**Fit:** Fits regulated or NDA-bound work where data must stay on your servers.

**Effort:** A few weeks for a scoped pilot

## Pitfalls

- Pasting full customer lists or price books into a personal ChatGPT account.
- Exposing a general SQL endpoint to the action, which lets a prompt request arbitrary data.
- Assuming the model's arithmetic is right on exported totals. Have it show the rows it used.
- Forgetting that exports are stale the moment they are created, and quoting a status from last week.
- Not checking whether your customer contracts allow third-party AI processing.

## Faq

### Can ChatGPT connect directly to my JobBOSS2 SQL Server?

**Answer:** Not directly and you should not try. Put a small read-only service in between that exposes specific operations. That service is the thing a custom GPT action calls, and it is where you apply access control, limits and logging.

### Is it safe to upload JobBOSS2 exports to ChatGPT?

**Answer:** It depends on the plan and the data. Business and enterprise plans offer controls such as no training on your data, but you still send the content to a third party. For export-controlled or NDA data, check your obligations first or use a private model.

### Does ECI offer a ChatGPT integration?

**Answer:** Check with ECI and your reseller for current features, as offerings change. If nothing fits, the read-only service plus a custom GPT approach works on most JobBOSS2 setups.

### Can ChatGPT enter data into JobBOSS2?

**Answer:** Technically it could call a write endpoint, but that is not recommended for job shops. Let it produce drafts, and have a person enter or approve them through the application or a controlled import.

### What if we only want ChatGPT for emails and summaries?

**Answer:** That is a reasonable scope. Copy only the fields you need into the chat, use a workspace with the right data controls, and keep the live database connection for a private model if you later want one.

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