Compare franchises with AI
Comparing franchises means lining up costs, earnings, growth, and risk from documents that all look different. FranDB structures those figures across thousands of brands so you can ask an AI assistant to put any shortlist side by side and see where each one really stands.
The four things worth comparing
A useful comparison comes down to four things: cost (Item 7 investment and the ongoing fees in Items 5 and 6), earnings (Item 19, where disclosed), momentum (Item 20 openings versus closures), and risk (SBA default rates and litigation history).
Pulling all four for a handful of brands by hand means opening several documents and several tables for each one. Structured data collapses that into a single question.
Let the AI build the table
Connect FranDB to ChatGPT, Claude, or Cursor and you can name a few brands and ask for a side-by-side on the metrics you care about. The AI pulls each figure from the structured data and lines them up, including flagging where a brand discloses no Item 19 so a gap doesn't read as a zero.
Treat the result as a shortlisting tool. The comparison narrows the field. The validation calls and an attorney's read of the agreement decide it.
What you can ask
Connect FranDB to ChatGPT, Claude, or Cursor, then ask in plain English:
- Compare these three brands on total investment, royalty, and Item 19 revenue.
- Which of these franchises has the best net unit growth over the last three years?
- Build a side-by-side of cost, earnings, and SBA default rate for these five brands.
- Of these brands, which has the lowest all-in investment with disclosed earnings?
Connect FranDB to your AI tools
FranDB plugs into ChatGPT, Claude, and Cursor over MCP, so the data on this page is one question away inside the tools you already use. No code required.
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Frequently asked questions
Can AI compare franchises for me?
Yes. With FranDB connected over MCP, you can give ChatGPT or Claude a list of brands and ask for a side-by-side on investment, fees, Item 19 earnings, unit growth, and SBA default rates, all pulled from structured FDD data.
What should I compare franchises on?
Cost (Item 7 investment plus ongoing fees), earnings (Item 19 where disclosed), momentum (Item 20 openings versus closures), and risk (SBA default rates and litigation). Those four cover most of the decision.
How do I handle a brand with no Item 19?
Treat the gap as missing data, not a zero. Ask the AI to flag which brands disclose nothing, then lean harder on Item 20 closures, SBA default rates, and validation calls for those brands.