Intake week
is one week.
And it is half the season.
Uniform size packs planned against the intake calendar — weeks before the queue forms at the counter, on a date that cannot be moved.
Illustrative · not a client store
The AI that
Age-band sell-through, school-season readiness and repeat-family rate — on one board, narrated by Iris.
The bell. The growth spurt.
The gifting spike.
Three demand events with fixed dates and lumpy shapes — and not one of them behaves like an average.
Uniform size packs planned against the intake calendar, weeks before the queue forms at the counter.
Iris counts down to intake week for you.
The size-up moment predicted per family, and the offer timed to land before they shop somewhere else.
Iris spots the size-up before the family does.
Eid, birthdays and holidays modelled as the lumpy demand they actually are, not smoothed into an average.
Iris sees the spike before the calendar does.
Every store
on one board.
Rules fire. Models predict.
Iris explains.
| Rule | Threshold | Now | State |
|---|---|---|---|
| Gross margin · season-block | ≥ 42% | 44.8% | clear |
| Repeat families | ≥ 44% | 47% | clear |
| School-season readiness | ≥ 88% | 92.4% | watch |
| Age-band forecast accuracy | ≥ 87% | 90.6% | clear |
| Model | Predicts | Accuracy |
|---|---|---|
| Age-band demand | units by band | 90.9% |
| Season readiness | pack fill by school | 92.3% |
| Growth cycle | size-up timing | 88.4% |
Readiness is 92.4% and still on watch, because the rule reads pack fill — 89% — not the headline. The model puts the gap on named schools. Iris names the one move that clears a fixed date without paying for it in airfreight, which is the only version of the answer a buyer can act on this week.
Pick one. It opens
the real screen.
Age-band sell-through, school-season readiness and repeat-family rate.
Point of Sale
A kidswear basket is rarely one child and rarely one size. The till has to hold a family, a school list and a size the parent is guessing at — at 96 baskets an hour in intake week.
The school list is the basket. A uniform pack rings as one line against a named school and year group, not as nine items typed in by hand.
The family is the customer, not the buyer. Sizes bought are recorded per child, which is what makes the size-up prediction possible at all.
Iris knows what the next size is. The upsell is the size above, offered at the till because the child was already measured against it.
Balanced Scorecard
Season-block margin, repeat families, school-season readiness and age-band forecast accuracy on one live board — with Iris naming the school that is about to miss a date nobody can move.
Margin is read by season block, not by month. A uniform mix of 38% behaves nothing like the rest of the range, and averaging the two hides both.
92.4% is on watch, not celebrated. Readiness looks healthy until you read pack fill underneath it at 89% — the number that decides whether intake week trades.
The fourth lens audits the AI. Age-band forecast accuracy is reported beside margin, because a buy is only as good as the band split behind it.
Numerus · CFO
Trial balance, P&L, balance sheet and cash flow on a ledger that ties out to the penny — with the school season carried as the working-capital event it is, not as four ordinary months.
The season is a cash shape, not a run rate. Stock is paid for months before intake week trades it, and the ledger shows that gap rather than smoothing it.
Uniform and range are costed apart. A 38% uniform mix at contract prices is a different margin engine from the rest of the floor, and blending them hides which one is working.
Ask Iris for any number. Any store, any school, any block — answered in the conversation rather than a week after close.
Nexus · Supply Chain
Packs planned against a calendar rather than a lead time. Intake week does not slip because a container did — so the buy has to be placed against the date, not against the average.
The intake calendar drives the order, not the reorder point. Twelve schools with twelve fixed dates are twelve separate clocks, and one reorder rule cannot serve all of them.
Being 19 days behind is a supply fact, not a store fact. The gap is reported where it can be closed — at the order — and not at the counter where it can only be apologised for.
Airfreight is a costed option, not a panic. Iris prices pulling a reorder forward against shipping it late, so the expensive answer is chosen deliberately or not at all.
Motus · Operations
Floor standards, fitting capacity and store assets across 74 stores — run to a standard rather than to whoever happens to be on shift in the busiest week of the year.
Intake week is an operating plan, not a busy week. Fitting capacity, queue management and floor layout are planned for it in advance, because it will not be rehearsed twice.
Standards are evidenced, not asserted. Sign-off against a checklist with a timestamp, so a claim about the floor is a record rather than a recollection.
Iris watches the exception, not the routine. The stores that met standard are quiet; the ones that did not are named.
Manus · Workforce
The season's roster built against the intake calendar — because 46% of a season trading in one week is a staffing problem long before it is a stock problem.
The roster follows the calendar, not the month. Seasonal cover is booked against each school's intake date rather than spread evenly across a quarter that is not evenly busy.
Seasonal hires are onboarded, not just scheduled. Training, compliance and till access are ready before the week they are needed, not during it.
Hours, payroll and compliance on one record. Who worked, where, at what rate — without a second spreadsheet running alongside the season.
Every solution,
on the real screen.
Grouped by what they act on. Pick one and it runs.
How Iris enforces the rules.
Thresholds that fire on the shift the breach happens (the “Zen Rules” layer) — the school list, the safety file and the family's first year. Three solutions run on this layer.
Pack fill below 90% with five weeks to intake. Three schools are running 19 days behind last year — against a date that is printed on a letter to every parent.
School-season readiness
| School | Packs | Filled | Gap | State |
|---|---|---|---|---|
| Grammar Yr 7 | 1,050 | 61% | 410 | 3w late |
| St Mary’s Yr 4 | 1,200 | 74% | 312 | sizing late |
| Lycée CM2 | 640 | 88% | 77 | tight |
| Beaconhouse Yr 1 | 900 | 97% | 27 | ready |
| Other 8 schools | 2,800 | 96% | 112 | ready |
Two schools are three weeks behind on a date that cannot move, and intake week is 46% of the season. There is no recovery window after it — last year the same gap cost 14% of intake footfall to queue walkouts. Pull both reorders forward now and it clears without airfreight.
Safety & compliance traceability
Children's clothing carries obligations adult clothing does not — cords, small parts, flammability, fibre content. A recall is not a merchandising problem; it is a question of which stores, which batch, and which families, answered within the hour.
Batch and supplier are carried on the item record, so a style is traceable back to the run it came from rather than to the season it sold in.
Compliance attributes are held as data, not as a certificate in a folder — which is the difference between a rule that can fire and a claim that has to be looked up.
A flagged batch is blocked at the till by rule, across all 74 stores at once, rather than by an email asking staff to remember.
Affected baskets are identifiable because sizes are recorded per child, so the families who bought it can be reached rather than broadcast to.
The audit trail is a by-product of trading, not a document assembled after an incident.
Maternity-to-newborn journey
The most valuable customer a kidswear retailer ever acquires arrives before the child does — and then needs a different department every few weeks for two years. Handled as separate transactions, that customer is acquired and lost repeatedly.
Maternity, newborn and infant are routed as one continuous journey rather than three departments that happen to share a customer.
An expected date is a schedule, not a note — it tells the system when the next need arrives without anyone asking the customer again.
The handover from maternity to newborn is a rule with a date on it, which is the moment most retailers silently lose the family.
The first year of sizes feeds the growth-cycle model directly, so this customer's size-up timing is understood earlier than any other's.
Repeat-family rate at 47% is measured across the whole journey, not per department, because a department-level number cannot see the leak.
How Iris predicts the season.
What the calendar is about to do (the “Zen Models” layer) — modelled early enough to act on, with the cause named and the move costed. Three solutions run on this layer.
Age-band demand forecast
| Band | Forecast | Actual | Var. | State |
|---|---|---|---|---|
| 0–2 Infant | 18,400 | 18,900 | +3% | on plan |
| 2–5 Toddler | 26,100 | 25,400 | −3% | on plan |
| 6–8 | 22,800 | 23,600 | +4% | on plan |
| 9–10 | 19,200 | 17,700 | −8% | under |
| 11–14 Teen | 14,600 | 16,900 | +16% | over-selling |
Age 9–10 is not losing customers, it is losing them upward — 4,600 families moved into 11–14, which is over-selling by 16% on a smaller buy. The two variances are the same children. Rebalancing the buy between the bands fixes both numbers at once.
Growth-cycle repeat prediction
Every kidswear customer has a built-in reason to come back and a narrow window in which they will. The size-up moment predicted per family, and the offer timed to land before they shop somewhere else.
Size-up timing is modelled per child at 88.4% accuracy, from the sizes that family has actually bought — not from a national growth chart.
The window matters more than the message. A size-up offer that lands two weeks late lands after the purchase has already happened elsewhere.
Size-up capture is measured at 62%, which makes the missed 38% a number a buyer can be held to rather than a feeling about loyalty.
The model is the reason the age-band forecast can distinguish a band that shrank from a band that aged — those are the same children, differently filed.
Repeat families at 47% is the outcome this model exists to move, and it is reported on the board next to margin.
Gifting & occasion spike planning
Eid, birthdays and holidays modelled as the lumpy demand they actually are, not smoothed into an average. A spike planned for is a margin event; a spike averaged away is a stock-out followed by a markdown.
Occasion demand is modelled as its own shape, because a week that does five times the trade of the week before is not a trend line.
Gifting skews the size mix as well as the volume — a gift is bought a size up, deliberately, and the buy should expect that.
Occasion and school season compete for the same working capital, and planning them together is the only way to see that competition before it happens.
Capacity — stock, staff and fitting room — is planned against the spike rather than against the month it falls in.
The same discipline that makes intake week survivable makes every other fixed-date spike survivable, which is why one model serves both.
Ask Iris · the season
Not a screenshot — the actual agent, reading the same season board the rules and models write to.
Twelve core POS solutions,
in every sector.
The sector solutions sit on top of these. They are not an upsell and they are not configured per customer — every Zentallio retail deployment ships with all twelve.
Implementation,
run by Iris.
You configure a sector playbook, not a custom project. Iris applies it herself — agentically, from day one.
Onboarding is agentic. Iris connects the school list, the pack definitions and the intake calendar directly — no manual data mapping.
Last season's sizes are imported, not retyped. Historic per-child purchases carry in, which is what makes the growth-cycle model useful in month one rather than month twelve.
Live in weeks, learning from day one. Go-live is a configuration, not a project plan — which matters when the next fixed date is already on the calendar.
Support starts
with Iris.
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 — including through intake week.
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.
Ready before the bell.
five weeks out
not a group average
costed against airfreight
reads as growth
Where a fixed date
quietly becomes a queue.
A season planned as four months when nearly half of it trades in one week that cannot be moved.
A band read as lost customers when it is the same children, one size up, filed under a different heading.
The size-up offer sent on a monthly cycle, arriving two weeks after the family already bought it somewhere else.
Readiness fires on pack fill by school, safety blocks apply across all 74 stores at once, and the family is routed as one journey.
Pack fill predicted by school at 92.3%, band demand at 90.9%, size-up timing at 88.4% — so the cause is named, not guessed.
“Why, and what do I do?” — pull the reorder forward now, and intake week clears without airfreight.
The same spine,
a different floor.
Kidswear & Teen is sector three of nine in Fashion Retail. The layers, the six products and Iris herself are the same ones running across ten Food & Beverage sectors.
on your shop floor.