Retail
Build a vending machine management system
Kliv is an AI app builder. Describe your vending machine management system, with machines, planograms, route visits, card-reader exports, host commissions, and statements, and Kliv builds it.
Just enter your idea into the text box and AI will build it for you
What is Kliv?
Kliv isn’t enterprise vending telemetry software you sign up for and configure — it’s an AI app builder that builds software for you.
You describe the app you need in your own words, and Kliv builds it: the data, the rules, the screens, and the workflows. What you get is a real application that’s yours. You can use it and keep changing it later by asking for changes. It isn’t a template with your name on it.
On this page, that app is a vending machine management system for your own routes and host deals. Kliv builds all kinds of web apps; this is one of them, and that’s what the rest of this page covers.
Why build your own vending machine management system?
A vending operator runs many small shops with no staff. The daily questions are practical: which machines need a visit, what should go in the totes, which card-reader exports show a quiet machine, and what commission each location host is owed.
Kliv builds the back office around your routes, machines, planograms, reader exports, cash counts, spoilage, and host statements.
Fleet records and route data
Five records carry the operation and turn route decisions into data.
Machines and locations
Each machine knows its site, host deal, planogram, and service history. The fleet list is the business record.
Products and coils
Par levels, prices, and slots live per machine. The gym can sell water at $2.10 while the courthouse tracks $1.60 crackers.
Route visits
Each visit records fills, pulls, and cash counted at the machine, with the driver, location, and date attached.
Host deals and accruals
Twelve percent of gross at the gym and eight percent at the courthouse can accrue as ledger lines, then settle on statements hosts can read.
Spoilage and pulls
Date-coded pulls are logged where they happened, so each machine’s economics include losses as well as sales.
Planning tomorrow’s route
The system should make tomorrow’s route clear before the van is loaded.
Prekit list
Per machine, from last counts and par levels, so totes are packed by coil in the warehouse.
Skip list
Machines that do not need a visit yet become a decision, not a guess from the van.
Quiet-machine flag
If yesterday’s reader export shows zero taps at a busy courthouse machine, it gets surfaced before anyone calls.
Statement run
On the second of the month, each host statement is generated from accrual lines and emailed on your letterhead.
Machine verdict
Revenue per visit can be compared with the drive time it costs, so weak placements show themselves in numbers.
An example: Dritan’s vending routes
Here’s how one operator might use it. It’s only an example — you would describe your own machines, products, routes, reader exports, host deals, and statements.
The fleet starts with machines and planograms
Dritan records 120 machines across offices, gyms, a courthouse, and two campuses. Each machine has a location, host deal, planogram, par levels, and prices.
Reader exports complete the sales picture
Daily card-reader exports upload as sales rows beside cash counts from route visits. The readers keep settling through their own systems.
Tomorrow’s prekit list is based on counts
The system compares last counts, sales, and par levels to produce a prekit list by machine and coil before the van is loaded.
A quiet machine gets attention early
A courthouse machine that usually sells every weekday shows zero taps in the reader export. It lands on the route as a likely jam or power issue.
Host commissions accrue automatically
Gym machines accrue 12% of gross and courthouse machines accrue 8%. Each month, host statements generate from those ledger lines.
Weak placements become visible
Revenue per visit is compared with the drive time and spoilage. A machine that does not justify its route cost is easier to move or renegotiate.
Describe the vending system you want
Kliv builds from your description, so the more detail you give, the closer the first version. Include machine count, planograms, route visits, reader exports, host deals, statements, and exception flags. Here are three to build on:
Snack route operator
Machines, planograms, prekits, and commissions.
“Build a vending machine management system for 120 machines across offices, gyms, a courthouse, and two campuses. Include machine records with planograms, par levels and prices per coil, route visits with fills and cash counts, uploaded daily card-reader exports, spoilage logs, and monthly commission statements per location host.”
Coffee machine service
Placements with usage and monthly billing.
“Build a coffee machine placement tracker where each client site has a machine, service schedule, consumable usage per visit, water-filter changes, and a monthly invoice built from what was actually delivered.”
Laundry route board
Coin machines and owner statements.
“Build a route management app for coin laundry machines across apartment buildings with collection visits, cash counts per machine, uploaded card-reader exports where available, and owner statements by building.”
How the vending back office works
Reader exports become rows. Kliv does not need to talk directly to your card readers. Their daily settlement files can upload and land as sales rows beside the cash counts.
Drivers see their route. Each driver’s login can show their machines, visits, and counts, but not another driver’s route. That matters when a count is disputed.
Hosts see their own wall. A gym owner can sign in to see that location’s machines, sales summary, and statements, and nothing else.
A vending machine is a shop with no staff and no opening hours. Retail that dispersed runs on its back office. The back offices Kliv builds start from a detailed description.
Common questions
What exactly is Kliv?
Kliv is an AI app builder that builds custom web apps from a description. For vending, it can build machine records, planograms, route visits, reader-export uploads, spoilage logs, host commissions, and statements.
Do I get a real app, or a template?
A real app. Kliv builds the database, workflows, screens, access rules, and reports around your vending operation.
Can I change it after it’s built?
Yes. You can change planogram fields, route rules, host commission rates, statement timing, or reader-export formats later by asking.
Does it connect to our card readers?
It does not need to pretend to be the reader system. Reader exports can upload as rows and live beside cash counts. If a machine’s taps go quiet, the morning numbers can show it.
If we grow to 200 machines, what changes in Kliv?
Nothing special on Kliv’s side. The routes, pars, and statements simply have more rows to work with.
Can a location host check their numbers?
Yes. A host login can show that location’s machines, sales summaries, and statements, and nothing beyond them.
How do refunds to machine customers work?
However you handle the refund, the app can record it against the machine and day, so repeat problems become visible.
Can it build prekit lists?
Yes. Prekit lists can use last counts, reader-export sales, and par levels to calculate what each machine needs.
Can it tell us what to skip?
Yes. Machines that do not need a visit can appear on a skip list, so routes are planned from current counts.
Can drivers see only their own routes?
Yes. Driver access can be scoped to assigned machines, visits, and counts.
Can I move off Kliv later?
Yes. The app’s code syncs to your own Git repository.
Do I need a separate reporting tool?
No. Host statements, sales summaries, spoilage, route efficiency, and machine performance can be reports inside the app.
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Skip machines that do not need a visit
Describe your machines, planograms, products, route visits, reader exports, host deals, and statements. Kliv builds the back office that turns those details into route decisions.