Claude, Chat, Gemini, and other AI platforms are not trained on and do not have all specific FDD data. You can (and should) upload any FDD you receive from a brand for your research.
Franchise Signal goes three steps further - we offer you the option to use *multiple* brand FDDs (to compare differences). We structure individual brand data for specific item retreieval. And we provide multiple years worth of the same FDD - so you can see how a given franchise system has changed over time.
Franchise Signal lets you select the franchise brand years you want access to, connect Claude, and query structured FDD data through MCP tools.
Ask Claude to compare years, deep dive into specific FDD items, explain disclosure changes, pull operating inputs, or build a pro-forma from the underlying franchise data.
This is built for franchise searchers, attorneys, lenders, franchisees, analysts, and advisory teams who want more than a static PDF.
Select brand-years. Add AI credits. Query away in Claude.
FDDs are dense, repetitive, and hard to compare by hand. Franchise Signal turns selected brand-year data into a queryable workflow inside Claude.
You still control the analysis. Claude gets the structured FDD context it needs to help you research faster, compare more clearly, and build useful diligence outputs.
Select from available franchise brands and FDD years, then ask Claude natural research questions backed by Franchise Signal data.


Ask Claude to review a single item, compare multiple years, find key changes, or explain how a disclosure trend has moved over time.
Once Claude can access the selected FDD data, you can ask it to pull investment ranges, fees, revenue disclosures, outlet counts, and assumptions into a working model.

Franchise Signal brings FDD-backed intelligence into Claude for searchers, attorneys, lenders, franchisees, investors, and advisory teams.