See what your kitchen data can tell you.
PantryIQ is for restaurant operators who want a clearer view of sales, purchasing, and waste. It keeps you in control and shows the numbers behind each decision.
One location at a time. No POS connection required.
One location
Scoped to one operating unit
Show your work
Facts stay separate from predictions
You decide
Recommendations stay suggestions
The problem
You know it's happening.
You bought three cases of salmon because you always do. You sold it twice. The rest went gray in the walk-in on a Tuesday and you found out when you emptied the bin.
You know it's happening. You don't know what it costs. By the time it shows up in the P&L, the loss is three months old.
How it works
Three screens, and the numbers on all of them.
Every figure below comes from a real-world bad-month example, grounded in reported restaurant purchasing and waste ranges. 166 rows of one restaurant's sales, purchase, and count data, run through the same engine the product runs.
Step 01
You map your columns once.
PantryIQ reads the header names your system already exports and tells you why it matched each one. No recipe library, no integration call.
sales-export.csv Read, no setup- ITEM_DESCItem nameHeader matches “item”. 100% of sampled values look text.
- QTY_SOLDQuantityHeader matches “qty”. 100% of sampled values look numeric.
- NET_SALESTotal revenueHeader matches “net sales”. 100% of sampled values look numeric.
- TRN_DTTransaction date and time100% of sampled values look date.
Every column is yours to confirm or change before anything is saved.
Step 02
You get a ranked list, not a dashboard to interpret.
Biggest dollar first. The figure sits on the row, so nothing needs decoding and nothing depends on telling two colors apart.
Monthly purchasing volumeFour-week bad month- Salmon fillet
- $12,000
- Heirloom tomato
- $10,400
- Ribeye 12oz
- $14,000
- Burrata
- $6,800
- Sourdough loaf
- $12,000
These five categories total about $55,200 in four weeks. The risk view below is current dollars at risk. It deliberately illustrates a bad month, with roughly 9–11% of each category's buying volume still at risk. It is an illustration, not a forecast.
Ranked by dollars at risk 1 needs a decision- Salmon fillet
- Heirloom tomato
- Ribeye 12oz
- Burrata
- Sourdough loaf
Each row prints its own figure, and each bar carries a pattern as well as a color, so the chart still reads in grayscale or in bad kitchen light.
Step 03
Each one comes with its receipt.
The arithmetic, the files it was read from, and the one thing PantryIQ had to assume. You decide what happens next; it never orders anything.
Real-world example
This real-world example generalizes reported restaurant purchasing and waste ranges across five common categories. It shows the kind of bad-month loss an operation can surface from its own counts.
- Monthly food purchases
- $55,000
- Observed food-cost variance
- 4.1%
- Unaccounted this month
- $2,255
- Recoverable gap in the case
- $1,540
Reference case: 4.1% variance fell to 1.3% after controls. Read the source.
Where the figure comes from
- On hand at the last count
- 160.00 lb
- Your unit cost
- $6.50 / lb
- At risk right now
- $1040.00
Read from
- inventory-counts.csv20 rows
- purchase-orders.csv20 rows
- sales-export.csv126 rows
What we assumed
- Shelf life: 7 daysYour value · change it at Settings → Item master → shelf life
Every assumption is labeled with where it came from, and every one is yours to change. Change the shelf life and the figure changes with it.
Pricing
One location. One clear price.
per month
One location, one baseline tier. Start with the sales and purchasing data you already have.
Start free