# How to use ChatGPT with Fishbowl

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

## Short answer

ChatGPT cannot reach an on-prem Fishbowl server by itself. You either export data and upload it, expose a small read-only gateway to a custom GPT as an action, or skip public ChatGPT and run a private model. Which is right depends on how live the answers must be and how sensitive your costs and customers are.

## System

Fishbowl

## Capability

chatgpt

## Intro

Many Fishbowl users already paste stock reports into ChatGPT to ask things like which parts are slow movers. It works, and it is a fine place to start. The limits are quickly obvious: the data is stale the moment you export it, nobody checks what was pasted, and a spreadsheet of vendor costs now sits in a chat history.

The next step up is giving ChatGPT live access through an API action. Fishbowl runs on your server, usually behind a firewall, so ChatGPT cannot call it directly. You need a gateway service you control that translates a few safe questions into Fishbowl API or SQL calls.

There is a third path that matters for defense subcontractors and anyone with customer drawings or contract pricing in Fishbowl: do not send it to a public service at all, and use a private model with the same chat experience.

## Use Cases

### Slow-mover analysis from an export

**Description:** Export on-hand and sales history by part, upload to ChatGPT, and ask for items with no movement in a year, grouped by cost tied up.

### Cleaning up part descriptions

**Description:** Paste a batch of inconsistent part numbers and descriptions and ask ChatGPT to propose a naming standard, then import corrections via CSV.

### Drafting vendor emails

**Description:** Give it a list of late PO lines and have it write polite, specific follow-ups for a buyer to review and send.

### Custom GPT for order status

**Description:** A custom GPT with a read-only action lets sales staff ask where a sales order stands without logging into Fishbowl.

### Explaining a BOM or routing

**Description:** Ask it to summarize a bill of materials in plain language for a new hire, from an exported structure.

### Formula help for reports

**Description:** Ask for help building Excel formulas or Jasper-style report logic against exported Fishbowl data.

## System Surface

- CSV and Excel export from Fishbowl screens and reports: the zero-integration route for one-off analysis.
- Fishbowl REST API: token auth via an approved integrated app. A custom GPT action needs a public HTTPS endpoint, so you put a gateway in front instead of exposing Fishbowl itself.
- MySQL database: a read-only user lets your gateway answer inventory questions quickly. Verify table names for your version.
- Legacy API on older installs: usable from the gateway if the REST API is not available.
- Scheduled report exports to a folder: a simple way to refresh a dataset that ChatGPT or a pipeline can use.

## How It Works

### Decide the data sensitivity

**Detail:** Classify what you would paste: stock levels are mild, vendor costs and customer pricing are not. This decides export versus gateway versus private model.

### Start with export-and-ask

**Detail:** Use filtered, minimal exports with no customer names or costs unless needed. Keep the prompt and result for review.

### Build a thin gateway

**Detail:** Write a small HTTPS service that exposes three or four read-only endpoints such as part availability and SO status. It authenticates to Fishbowl internally.

### Describe it as an OpenAPI action

**Detail:** Give the custom GPT the gateway's OpenAPI schema and a key. Keep responses small and structured.

### Limit what the gateway returns

**Detail:** Strip cost fields for sales users. Enforce the same visibility rules Fishbowl uses for roles.

### Check your ChatGPT plan's data controls

**Detail:** Business and Enterprise plans have different training and retention terms than personal accounts. Confirm current terms with OpenAI before sending real data.

### Compare with a private model

**Detail:** If the gateway's answers are good enough on a local model, you can drop the external dependency entirely.

## Example Flows

### Dead stock review

**Trigger:** A finance manager wants to reduce inventory.

**Steps:**

- Export on-hand with last sale date and average cost.
- Upload to ChatGPT with a prompt asking for items untouched for twelve months.
- Review the grouped list and sort by tied-up value.
- Take decisions back to Fishbowl for scrapping or discount.

**Result:** A working short list of write-off and sell-down candidates.

### Where is my order

**Trigger:** A salesperson is asked about an order by phone.

**Steps:**

- They ask the custom GPT for the status of an SO number.
- The GPT calls the gateway action.
- The gateway queries Fishbowl read-only and returns status, items and ship dates.
- The GPT answers in a sentence.

**Result:** Quick, consistent answers without opening Fishbowl.

## On Prem

Because Fishbowl typically sits on-prem, the gateway for a custom GPT must be reachable from the internet, which many IT teams will not allow. If that is your situation, the better design keeps everything inside: users chat with a private model hosted on your own GPU server, and it calls the same gateway over the LAN. You get a ChatGPT-like experience without any inventory data leaving the network. Open-weight models served with vLLM or Ollama are enough for status lookups and summaries.

## Security

- Never expose the Fishbowl server or MySQL port to the internet for ChatGPT's sake.
- Authenticate the gateway with a rotating key and restrict it to read-only endpoints.
- Minimize and anonymize exports; remove cost and customer fields unless they are the point.
- Review your ChatGPT plan's data retention and training settings before using real data.
- Log every gateway call with the asking user.

## Options

### Export and ask in ChatGPT

**Fit:** One-off analysis with non-sensitive data.

**Effort:** Minutes, no setup.

### Custom GPT plus self-built gateway

**Fit:** Live status questions for a small team willing to maintain a small service.

**Effort:** A few days to two weeks.

### Private model with a partner like Netray

**Fit:** Sensitive pricing, drawings or defense work where data cannot go to a public service.

**Effort:** Scoped pilot, then production.

## Pitfalls

- Pasting full cost reports into a personal ChatGPT account and creating an untracked data leak.
- Trusting arithmetic from the model on large tables. Have it generate formulas or code and verify totals.
- Exposing the Fishbowl API directly instead of through a gateway with limited endpoints.
- Forgetting that exports go stale, then making purchasing decisions on a week-old file.
- Giving the GPT write endpoints before read answers are trusted.

## Faq

### Is there an official ChatGPT connector for Fishbowl?

**Answer:** I am not aware of an official one. Check Fishbowl's app marketplace and OpenAI's connector directory for current options. Without one, you use exports or a custom gateway exposed as a custom GPT action.

### Can ChatGPT read my Fishbowl database directly?

**Answer:** No. It cannot reach a server on your private network. You would build a gateway that queries Fishbowl read-only and exposes a few safe endpoints. Exposing MySQL itself to the internet is a serious risk and should be avoided.

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

**Answer:** It depends on the data and the plan. Business and enterprise plans have stronger data controls than personal accounts, but you should still remove costs, customer names and anything under contract restrictions. Confirm current terms with the vendor.

### What is the best use of ChatGPT with Fishbowl for a small shop?

**Answer:** Export-and-ask for analysis, writing vendor emails, and cleaning part descriptions. It needs no integration and delivers value on day one. Move to live access only when you find yourself exporting the same report repeatedly.

### When should I use a private model instead?

**Answer:** When pricing, customer data or controlled technical data is in play, or when your IT policy blocks external AI services. A local open-weight model can answer the same status and summary questions without data leaving your network.

## Search terms

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