Built for quick service restaurants

Speed is your product. Data is your edge.

In quick service, minutes matter. Ask Tilly what's selling, when the rush is coming and how to keep the line moving.

Customers ordering food at a fast food restaurant counter

The challenge

Quick service runs on margins and seconds

Every menu decision, every shift and every promotion needs to land. Tilly gives you the velocity data to keep the line moving and the till ringing.

01

Review momentum

Know when ratings shift and which customer themes need attention.

02

Rush hour chaos

Lunch rush, dinner rush, unexpected surges. Prepare for peaks before they hit.

03

Menu velocity

Which items move fastest? What slows down the queue? Optimise for throughput.

04

Competition

Fast food is crowded. Know exactly how you compare to nearby options.

How Tilly helps

Throughput-first analytics for QSR

Peak prediction, item velocity and multi-location intelligence.

  • 01

    Ask Tilly anything

    Ask about item velocity, peak hours or a slow day and get a straight answer from your live data.

  • 02

    Peak prediction

    Know exactly when rushes will hit so you can prep food and staff the counter.

  • 03

    Item velocity tracking

    See what sells fast, what sits and what combos drive ticket size up.

  • 04

    Location intelligence

    Multi-location? Compare performance across stores and replicate what works.

Before and after Tilly

Before Tilly

✕Reviews slipping through the cracks with no one keeping track

✕Staffing the wrong hours because rush patterns are guesswork

✕High-margin items buried under weak performers

✕Multi-location data scattered across separate reports

With Tilly

✓Reviews tracked automatically with alerts when they dip

✓Accurate rush-hour predictions with staffing recommendations

✓Item velocity and margin data surfaced automatically

✓All locations compared side by side in one view

Frequently asked questions

Free trial

Start free. No credit card needed.

Connect your POS and ask Tilly which hour of the week you are most understaffed. The answer is in your existing data.