Label at the bar, modifiers that hold, a display case that counts itself down — 17 café and bakery solutions, narrated by Iris.
Yield, recipe cost and tomorrow's production plan built from what actually sold — not from what was baked last Tuesday.
A café earns its week before 11am — and loses it in a display case. The busiest format in F&B is also the most guessed-at.
Milk steamed into queues that were never forecast. Iris estimates the wait and flows the bar at barista speed.
Croissants baked on instinct. Production limits follow the forecast, not last Tuesday's tray count.
Yesterday's bake quietly binned. Markdown fires before anything reaches the bin — going earlier beats going deeper.
Drinks per hour, case sell-through, waste and margin — live, with Iris naming the item that quietly stopped paying.
Orders, modifiers, any tender, receipt in seconds — while Iris suggests the right add-on, flags margin drift and keeps selling even when the internet drops.
The order becomes a label. A drink rung at the till prints at the bar with its full build — no verbal relay, no call-back.
Stock depletes as you sell. Every unit sold moves the display case count and the ingredient ledger in the same second.
It keeps selling when the line drops. Trading continues through an outage and reconciles when the connection returns.
Financial, customer, operational and AI-learning KPIs on one live board — with Iris narrating the one move that matters and the beat that recovers it.
Every module feeds one board. Bar throughput, case sell-through, waste and labour all post into the same view.
Forecasting, not just reporting. L2 models project where the day is heading while the bake can still be changed.
Iris names the item. Not just that margin moved — which product quietly stopped paying, and when.
The finance cockpit — trial balance, P&L, balance sheet, cash flow and sub-ledgers that tie out to the penny. Every sale from the till posts here in real time.
Sub-ledgers that tie to the penny. AR, AP and inventory reconcile themselves rather than being chased at close.
Loyalty carried as a liability. Outstanding points are valued live and posted to finance — never an unbudgeted surprise.
Month-end is a review. Sales, tax, tips, refunds and settlement post themselves, already coded to the right accounts.
Demand-planned replenishment across every outlet and distribution centre. Iris forecasts by daypart and weather so the right stock lands before you need it — not after.
The bake plan drives the order. Tomorrow's production quantities become today's purchase orders automatically.
Weather is in the forecast. A cold Tuesday and a warm Saturday do not get the same iced-drink plan.
Par levels that learn. Reorder points come from forecast demand and supplier lead time, not from habit.
Digital checklists, shift routines and standard operating procedures across every outlet — completion tracked live, exceptions escalated. Frontline execution you can actually see.
Opening and bake routines, signed off. Completed in the system rather than assumed to have happened.
Bar throughput rolls up. Drinks per hour by barista and by site, aggregated across the estate.
Exceptions escalate themselves. The routine that didn't complete surfaces to a human; the rest stays quiet.
Rostering, attendance and labour percentage measured against forecast demand. Iris flags the overstaffed lulls and the understaffed peaks before the shift, not after.
Staffed to the 8am peak. Bar cover is built from forecast drinks per hour, not from an even spread across the day.
Bakers rostered to the bake plan. Production quantities and shift plans come from the same forecast.
Flagged before the shift. An overstaffed lull or an understaffed peak is caught while the roster can still change.
Deterministic logic under the hood (the “Zen Rules” layer) — vouchers validated, channels sequenced, stock depleted and integrations watched, the same way every time. Four solutions run on this layer.
Sync latency on one delivery channel has tripled. 14 orders currently at risk. Raised before a branch discovers it at the rush.
Digital vouchers, gift cards and campaign codes issued to the guest's phone and redeemed by QR scan, NFC tap or code entry — validated against every rule in under a second.
Created once, delivered wherever the guest already is — app wallet, email or WhatsApp.
Three routes to the same redemption: scan the QR, tap the phone, or type the six-character code.
Expiry, minimum spend, eligible items, per-guest limit, stacking and remaining budget all checked between scan and confirmation.
The discount lands as its own line, attributed to its campaign; gift-card value draws down the liability rather than posting as revenue.
Campaign spend ceilings stop redemption at the cap, and anomaly detection flags shared codes and staff-assisted abuse.
Every order source on one book — counter, kiosk, drive-through, web, app, call centre, WhatsApp and the aggregators. Same menu, same live stock, same ledger, one promise time.
Eight sources write into the same queue — the kitchen sees one sequenced list, not four screens and a phone.
A sell-out at the counter removes the item from web, app, kiosk and aggregator in the same second.
Promise times are channel-aware: the app accounts for the live counter queue, drive-through for lane depth, a rider for prep plus travel.
Fulfilment routes by type — pass, bagging station, lane window, rider shelf, or the bake plan for advance orders.
Every channel settles into the same ledger with its commission, packaging and delivery cost attached — true margin by channel, not a blended number.
Raw-material stock that depletes itself against every sale through the bill of material — with par levels, batch and expiry tracking, and low-stock alerts before a bake is at risk.
Selling a croissant removes flour, butter, yeast and salt in recipe proportion. Stock is a consequence of trading, not a weekly chore.
Each delivery carries a batch code and use-by date, rotated first-expiry-first-out, with alerts before product is written off.
Reorder points are set from forecast demand and supplier lead time rather than habit, and adjust as the season shifts.
When actual consumption drifts from theoretical, the anomaly is raised while the cause is still findable.
Every system a café actually depends on, connected and watched: card and wallet payments, the delivery aggregators, local tax rules, and the accounting ledger — configured per market rather than coded per customer.
Processors and methods both covered — the guest pays with whatever they carry, you get one settlement file and one reconciliation.
One menu published to every aggregator, one live stock, and their orders land in the same order book — no tablet farm behind the till.
Sales tax, GST/HST, VAT and eat-in versus take-away rates are configured per market, so a new country takes days rather than a release.
Sales, tax, tips, refunds and settlement post themselves to the accounting system, already coded to the right accounts.
Every connection is health-checked continuously — when an API degrades, Iris quantifies the orders at risk rather than letting a branch find out at the rush.
Forecasting, pricing and cost models under the hood (the “Zen Models” layer) — queue wait, production quantity, sell-through, markdown depth and true recipe cost, predicted before they land. Nine solutions run on this layer.
Orders flow from the counter to the bar as a printed label — cup size, milk, temperature, syrup and customer name. The barista never has to call back to the till.
The label prints the moment the drink is ordered — the barista begins immediately, with no verbal relay.
A queue-wait estimator predicts the current wait and displays it at the entry, so guests decide whether to join.
The guest's name prints alongside the build, so the barista calls the drink rather than an order number.
A digital board shows orders in queue — guests see their position and estimated wait.
Drinks per hour by barista are tracked, identifying where the bar workflow actually bottlenecks.
Items priced by weight are sold accurately without pre-packaging — the guest chooses the quantity, the scale talks to the POS, and the price is calculated and added to the bill instantly.
A connected scale sends weight directly to the POS — no manual entry, no rounding errors.
A daily production recommender uses weight-based sales velocity to forecast how much to make tomorrow.
Partial purchases are logged properly — half a loaf leaves the remainder on the shelf as sellable stock, not a written-off broken unit.
Container and packaging tare is deducted automatically — the guest pays only for the product.
Every weight transaction is logged, supporting HACCP and portion-control audits.
Each product carries a daily production quantity. When it sells out it is removed from the menu on every channel — guests cannot order what hasn't been made.
Morning entry of units produced — counter, kiosk and app all start the day from that same inventory.
A production recommender predicts tomorrow's optimal quantity per item from day-of-week, weather and seasonal trend.
Below a set threshold, the counter and back-of-house are alerted while there's still time to bake more.
At zero the item removes itself from every channel automatically.
Non-carry-over items are zeroed at close — yesterday's stock is never misrepresented as fresh.
Items on display are tracked as inventory units. Each sale depletes the case count automatically — staff always know what remains without counting physically.
Every pastry, slice and display item is tracked as a unit, deducted in real time as it sells.
A sell-through forecaster predicts which items will have excess stock by close, triggering a markdown prompt two hours before closing.
Counter case, window display and chilled cabinet are counted separately, so a sell-out in one section doesn't hide stock sitting in another.
Below threshold, back-of-house is alerted to replenish from production.
Expected remaining units at close are cross-checked against the physical count to record waste honestly.
Two hours before closing, Iris identifies which items have excess stock and recommends a markdown price — maximising recovery and minimising waste without manual judgement.
Items with more units remaining than historically sell in the final two hours are identified automatically.
A dynamic pricing model trained on sell-through and past markdown outcomes predicts the optimal discount per item, per day.
The manager accepts the recommendation once — the new price lands on the till, the app, the kiosk and the shelf label together.
Revenue recovered through markdown is tracked against the cost of unsold wastage, daily.
Whether the markdown actually cleared the stock is logged per item — the model improves on outcome, not intent.
Every guest scored on Recency, Frequency and Monetary value, then auto-segmented — champions, loyal regulars, slipping, at-risk and lost. Iris tells you who is quietly leaving before they are gone.
Three scores combine into a single RFM cell per guest — the fastest honest read of who matters and who is fading.
A guest sliding from weekly to monthly moves from Champion to Slipping without anyone running a report. The segment is the alert.
Iris flags the slipping cohort while the habit is still recoverable, and proposes the offer and channel most likely to bring each one back.
When too much revenue concentrates in too few guests, the platform says so — a quiet risk most cafés discover only after a churn event.
Feedback captured right after collection, scored for sentiment, tracked as NPS by outlet and day-part — with automatic recovery flows when a guest has a poor experience.
A single tap rating, sent on the guest's own channel, earns far more responses than a long survey emailed the next day.
Written comments are scored and themed automatically — queue, temperature, staff, freshness, price — so patterns surface without anyone reading every line.
A low score triggers an immediate apology and make-good offer, usually before the guest reaches a public review site.
Every score links to outlet, day-part and shift, so a service problem is traced to a window rather than argued about in the abstract.
Recipe cards, nested sub-recipes and bills of material with real yields, batch scaling, live cost per sellable unit, and allergens that propagate automatically into the printed label.
One card per product — ingredients, method, oven and proofing parameters. The baker follows it and the platform costs from it, so they never diverge.
Laminated dough is its own recipe consumed by six products. Change it once and every product recosts, re-labels and re-plans automatically.
Proofing loss, baking loss and trim are recorded as real yields, so cost is per sellable unit rather than per theoretical unit.
When butter moves 6%, every laminated product recosts in the same second and anything crossing its margin floor is flagged with the size of the gap.
Allergens travel up through every sub-recipe into the printed label, and publishing is blocked if any ingredient is untagged.
Theoretical versus actual usage per recipe and per batch — surfacing shrinkage, over-production, portion drift and spoilage with a cost attached to each.
What the recipes say you should have used, against what actually left the store. The gap is the number that quietly decides food-cost percentage.
Unsold, spoiled, damaged, staff meal and trial bake are separated — the fix for over-production is nothing like the fix for spoilage.
Slow, invisible over-portioning is caught by variance patterns long before it shows up as a margin problem in the P&L.
Waste by item and day-part flows back into production planning, so a repeatedly over-baked product is corrected rather than repeated.
Not a screenshot — the actual agent. Natural-language answers over the live counter. Four solutions run on this layer.
Full drink configurability — every combination of size, milk, syrup, shots, temperature and special instruction captured accurately and printed on the barista label.
Size variants adjust price automatically for every drink on the menu.
Clustering surfaces the most common combinations as one-tap presets, cutting order time on frequent drinks.
Syrups and flavour add-ons are priced individually and by pump count, printed on the label exactly as ordered.
Milk type, shot strength and temperature — oat, almond, decaf, iced or hot — each priced and recorded.
Free-text instructions for unusual requests print verbatim on the barista label.
Returning guests are recognised at the counter or in the app. From their order history, the POS surfaces their most likely order before they have to say anything.
Identified by phone, loyalty card or app profile — history retrieved instantly, before staff ask what they want.
A customer memory model builds a preference vector per individual and surfaces the top three likely orders.
A guest who takes a flat white at eight and a decaf at four gets the right suggestion for the hour, not an average of both.
Order the same drink three times and it becomes the ‘usual’ — available as a one-tap reorder.
When a seasonal item replaces a favourite, the closest equivalent is suggested rather than a blank.
Points, tiers, stamp cards and streak rewards on one wallet — earned and redeemed identically at the counter, on the web store and in the app. Iris decides which reward actually moves a guest.
A guest earns on a counter flat white and redeems on an app cake order — one ledger, never per-channel silos.
Stamps for the daily habit, tiers for annual value, streaks for consecutive weeks — each a rule you switch on, not a rebuild.
Rather than blanket 10% off, Iris picks the offer with the highest modelled redemption for that guest — a free pastry for the coffee-only regular, a bulk discount for the office buyer.
Outstanding points are a liability — valued live and posted to finance, so loyalty never becomes an unbudgeted surprise.
An Iris-powered agent that sells wherever the guest already is. It answers, recommends, quotes a real price against live stock, takes payment and tracks collection — all inside the conversation.
One agent across every messaging and social surface — because most café discovery now happens in a feed or a thread, not on a website.
A returning guest types a few words and the agent reconstructs their exact regular order, confirming it in one tap.
It quotes only what is genuinely available, at today's price and lead time — a sell-out removes the item from the conversation instantly.
Item, quantity, collection slot, payment link and ready-for-collection ping all happen in the thread, landing in the same order book as every other channel.
Complaints, allergy questions and custom cake briefs are handed to a person with the full thread attached. The agent never bluffs.
Seventeen solutions — the largest sector on the platform. Every card opens a live guided demo.
From a two-group specialty bar to an artisan bakery running a production plan — the same stack, tuned to how each format actually makes and loses money.
You configure a sector playbook, not a custom project. Iris applies it herself — agentically, from day one.
Onboarding is agentic. Iris connects the till, the scale, the label printer and every aggregator feed directly — no manual data mapping.
The playbook applies itself. Sector thresholds go live immediately, then tune from real trading data, site by site.
A new market takes days. Tax and compliance are configured per market rather than coded per customer — not a release cycle.
The first line of support is agentic — Iris resolves most of it herself. Our engineers pick up from there.
Layer 1 — Iris, 24/7. Configuration questions, anomalies and routine issues resolved directly, instantly.
Layer 2 — our engineers. Anything Iris can't close escalates automatically to a Zentallio engineer.
No blank tickets. Every escalation arrives with Iris's own diagnosis — engineers start from an answer.
“Croissants baked on instinct, milk steamed into queues that were never forecast, yesterday's bake quietly binned. The busiest format in F&B is also the most guessed-at.”
Zentallio removes the guessing: the bar flows at barista speed, the display case depletes unit by unit as the till sells, production limits follow the forecast, and end-of-day markdowns fire before anything reaches the bin. Underneath sits the same platform every sector runs — one data spine from the till to the ledger, three layers of intelligence, and an agent that narrates every screen, flags what needs a human, and acts on the rest.
Croissants baked on instinct — over-production that goes in the bin, or a sell-out before noon.
A queue nobody forecast, at the one hour of the day that pays for the week.
Butter moves 6% and nothing recosts until the month-end report says it already happened.
Stock depletes through the recipe on every sale, and a sell-out pulls the item from all eight channels at once.
Production quantity, queue wait and markdown depth are forecast before the day starts — not reviewed after it.
“Which product quietly stopped paying?” — answered from the same live ledger the baker costs from.
Every Zentallio sector runs the same three-layer intelligence, tuned to how that format actually loses margin.