You asked AI about Fishbowl and got a wrong answer. Here's why.
If you’ve asked ChatGPT or Claude how to do something in Fishbowl, you may have gotten a confident answer with a menu that doesn’t exist, or a cost explanation that didn’t match what you were seeing. The AI was answering a question about your Fishbowl without being able to see it, working from whatever it remembered about Fishbowl in general.
Why the answers go wrong
It answers from memory. A general AI learned about Fishbowl from public pages: old help articles, forum threads, and blog posts written for versions from years ago. It doesn’t know which version you run, and it often mixes up Fishbowl Advanced and Fishbowl Drive, which are different products with different screens.
It can’t see your data. “Why did this part’s cost jump?” depends on your costing method, your units of measure, and the receipts behind that part. Without them, the AI can only explain how costing works in general, and the general answer is rarely the one you need.
A lot of Fishbowl isn’t written down anywhere public. Where Fishbowl saves the QuickBooks class on a scrap adjustment, or how it rounds a unit of measure conversion, isn’t in the help pages. When the AI hits a gap like that, it fills it with something that sounds right.
It doesn’t know how your company uses Fishbowl. Your part numbering, your location groups, and the custom fields your team relies on are different from everyone else’s.
What changes when it’s connected
ILC MCP connects ChatGPT or Claude to four things a general chat doesn’t have:
- Your live Fishbowl data. The AI runs read-only queries against your database, so “why did this cost jump?” is answered from your actual receipts.
- Fishbowl’s current help. It can search Fishbowl’s documentation as it is today, instead of what it remembers.
- ILC’s notes on how Fishbowl works. We’ve documented Fishbowl’s data and behavior from years of implementations, reconciliations, and testing against real databases, and the AI can read those notes before it answers.
- Your company’s notes. What it learns about your setup is saved, so the next chat starts from it.
It also shows its work. Every query it runs is logged with what it was trying to find out, so you or your admin can check how it got the answer.
An example
Ask a general chat why a part’s cost jumped last week, and you’ll get a tidy explanation of average costing.
Ask the same question through ILC MCP, and the AI looks at that part’s receipts. It finds a purchase order received in cases where the case-to-each conversion was set up wrong, so each unit was costed as a full case. It shows you the receipt, the conversion, and the query it used to find them, which tells you exactly what to fix.
When it’s still not enough
Some questions need someone who has seen the problem before: a reconciliation that won’t tie, an integration that fails one order in a thousand. The AI can find the records, and our team can tell you why they look that way. That combination is what we built ILC MCP for.
If you have a Fishbowl question an AI got wrong, send it to us. We’d like to see it.
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