An intelligence layer over your operation.
No new till. No second dashboard. A system that joins your data from every corner of the operation into one model, and returns not metrics but decisions.
You have enough metrics.
Most operations do not suffer from missing numbers. The till and the BWA supply plenty. What is missing is the step after that.
"While reading, I kept asking myself which concrete decisions I would actually make differently because of these additional reports."
That is the right question, and it is the benchmark for this product. A metric describes a state. A decision needs three more things: an expectation of what comes next. An estimate of what a measure achieves. And a ranking, because nobody implements twelve recommendations at once.
A digital model of your operation.
At the core sits not a database but a knowledge graph: the objects your operation consists of, and the links between them. Those links are the product, not the individual table.
Site, item, ingredient, supplier, shift: the things your operation consists of.
An item consists of ingredients. Sales consume stock. Shifts cost hours.
Weather, events and bookings act on all of it, together and not separately.
Every change is logged, reversible, and waits for you.
Only because the model knows a cappuccino contains milk from a particular dairy with three days' lead time can a demand forecast become an order proposal. Till, inventory, rostering and bookings stay where they are. The layer sits above them, not beside them.
One decision, worked through.
Rather than claiming the system computes, here is the whole path from raw data to measure. Every step names its source.
Croissant, Saturday, per part of day, read from your export and never estimated.
Rain in the morning. The model knows the relationship from your own history.
A date that has already fallen on a Saturday twice in your history.
As a range, never as a single number. Order quantity and staffing derive from it.
Through the stored recipe to flour, butter, yeast, and from there to pack sizes.
Stock netted off, supplier lead time accounted for, separated per supplier.
"Order one pack size more flour from the mill supplier. Reason: expected higher Saturday-morning demand, two-day lead time, current stock otherwise runs out Friday evening."
That is the difference from a report. A report tells you cost of goods has risen. Here there is a named measure, on a named item, with a named supplier, and every step behind it can be read back.
Code. Every value above comes from a verified computation path.
The AI layer. It retrieves the result and phrases it. It does not recompute it.
Schematic illustration of the computation path. Item, quantities and supplier are placeholders for explanation. They do not come from a real operation. In use, every value is formed from that site's own data. Forecasts are issued as ranges; where the data is insufficient, the statement is withheld rather than estimated.
Where the boundaries are drawn deliberately.
Most vendors treat law and co-determination as a side issue. Here they are part of the product, and the reason the system survives a works council.
Only exports are read. Nothing is written back, no signed transaction data is altered. Your KassenSichV obligations remain untouched.
Business analysis, not tax advice. Your Steuerbüro stays responsible, and gets better numbers.
Team level is the default. Named planning stays technically locked until co-determination is settled. No absence prediction, not even on request.
The separation of your data is enforced in the database, not programmed in the application, and backed by an automated test.
EU endpoints only for employee and guest data, with no retention. No training on your data.
Rest periods and maximum hours are hard constraints. Every plan is re-checked after computation.
How an operation begins.
After the first call and an NDA, you send a till export. You get an honest assessment, including when it is "not yet".
A few daysRecipes, purchase prices, fixed costs, staffing structure. The only step that genuinely needs your involvement.
One to two weeksThe forecast is run backwards against your actual sales before you have to believe anything.
Straight afterYou plan as you do today and get the proposal next to it. You switch only once it beats your experience.
Four to eight weeksCost-of-goods ratio. Write-offs and spoilage. Staffing ratio.
Three figures from your own accounts. If they have not moved after a quarter, the system has not earned its price. We deliberately name no percentage in advance: every operation starts from a different level, and a number we cannot evidence against your data would be an assertion.
Frequently asked.
No. Spice sits as a layer above your operation, not beside it. Your Kasse, Warenwirtschaft, Dienstplan and booking system stay where they are. Only exports are read. Nothing is ever written back into the till and no signed transaction data is altered, so your Kassensicherungsverordnung obligations are untouched.
No, and that boundary is deliberate. Spice delivers betriebswirtschaftliche Auswertung and controlling. No bookkeeping, no Jahresabschluss, no representation before the Finanzamt. Your Steuerbüro stays responsible, and gets better numbers to work with.
Then the system says so explicitly rather than inventing a forecast. Where the data does not support a reliable statement, the statement is withheld, with the reason given. The first step is an honest assessment of your data situation anyway — including when the answer is "not yet".
By default, nothing personal. Staffing works at team level: hours, cost and requirement per role and period. Named-employee analysis is an additional tier that stays technically locked until the works-council requirements are met in your operation, not merely forbidden by contract. Prediction of individual absence will not exist, not even on request.
The effort is front-loaded: recording recipes or pack structures, purchase prices, fixed costs and staffing structure once. After that the model largely maintains itself, and the ongoing work is reviewing the proposals and releasing them.
You send a till export covering any period and get an assessment of your data situation. The forecast is then run backwards against your actual sales before you have to believe anything. After that it runs alongside for four to eight weeks: you plan as you do today and get the proposal next to it. You switch only once it has beaten your experience over several weeks.
Start with a conversation.
Twenty minutes to establish whether this is worth it for your operation. Only once that is settled do we talk about data. You send us nothing before you know who we are and what happens to it.