AI for retail & hospitality, grounded in your own POS and booking data.
AI for retail & hospitality is grounded, auditable software RMAI builds on your own POS, booking, and supplier data — so demand-led ordering, demand-matched rosters, the calls that ring through service, and the reviews waiting for a reply get forecast, drafted, and cited in minutes, with a manager signing off before anything reaches a customer or a shift.
What actually slows a retail or hospitality business down.
The biggest constraints in retail and hospitality are not the craft — they are operational: ordering and rostering run on gut feel, the phone goes unanswered through service, and reviews pile up unwritten. Each one is a data-and-handoff problem that consumes senior time, which is exactly where grounded AI pays back.
Ordering and prep run on gut feel, not demand
Orders anchored to last week miss seasonal swings, local events, and weather, so a kitchen over-preps perishables and runs out of the lines that sell. Just 1–2 kg of avoidable protein waste a day costs a single venue roughly $5,000–$10,000 a year — before the cost of stockouts on the floor.
Rosters are built from memory, not from demand data
Labour can reach ~40% of sales — its single largest controllable cost — on thin margins, yet rosters are still built from last year's pattern and a manager's sense of a typical Saturday. Overstaffing a slow lunch and understaffing a busy Friday are both expensive, and the spreadsheet that builds them eats hours of senior time every week.
The phone rings through service and tables sit empty
During a lunch or dinner rush the host stand has to choose between the guest in front of them and the phone, so bookings and orders go to voicemail. One Sydney operator reported AI handling 859 overflow calls in a month — work that had been costing 20–40 staff hours a week — while automated reminders cut no-shows.
Reviews go unanswered because no one has the time
Review platforms reward operators who reply promptly, in ranking and in guest trust, but writing a genuine response to every rating across Google, the delivery apps, and the booking sites rarely gets prioritised. The drafting is repetitive manager time — one published case cut the task from 30 minutes to 5.
What changes once the floor is grounded in your own POS and booking data.
Operators working with RMAI recover manager hours and tighten margin at the same time. The outcomes below are illustrative of shipped patterns; every one keeps a person on the final call — nothing orders stock, finalises a roster, or answers a customer on its own.
The questions leaders ask first.
The questions below are the ones RMAI hears in the first call — on safety, staffing, compliance, cost, and feasibility.
What the time recovered is worth.
Move the sliders for your own volumes; the benchmark shows where shipped builds have landed.
| Task | Before | After |
|---|---|---|
| Weekly roster build | ~4 hrs | ~30 min (manager signs off) |
| Phone reservations during service | missed / to voicemail | 100% answered, no-shows down |
| Review response (all platforms) | ~30 min / left unanswered | ~5 min (manager approves) |
| Weekly ordering & prep | gut feel / last week | demand-led draft, less waste |
What RMAI has built for this sector.
The applications below are grounded, human-in-the-loop tools RMAI has built or scoped for this sector — illustrative of the patterns we ship.
Visual Product Search
Dramatised Visual Product Search demo for retail-hospo (fake data) — shows the pattern; a person approves each output.
Review-Response Drafter
Reads new reviews across Google, the delivery apps, and the booking sites, drafts an on-brand reply matched to the rating that quotes the detail the reviewer mentioned, and flags anything about food safety, illness, or staff for the manager. It drafts for approval and never posts on its own.
Menu Engineering Dashboard
Dramatised Menu Engineering Dashboard demo for retail-hospo (fake data) — shows the pattern; a person approves each output.
Demand-Led Prep & Order Planner
Builds next week's prep and ordering plan from your POS sales history, the bookings calendar, and forecast weather and events, recommends quantities line by line with the reasoning, and flags any line where history is too thin to be confident. The operator reviews and approves every order.
Demand-Matched Roster Builder
A draft weekly roster matched to your forecast demand and held inside the labour budget, with every shift checked against the Restaurant Award and anything risky flagged for the manager to fix before sign-off.
Booking & Phone Agent
Answers calls 24/7, takes and confirms bookings from your own availability and menu information, sends reminders to cut no-shows, and routes anything complex or sensitive to a person with the call context attached. It works from your approved information and escalates rather than guessing.
Timesheet-to-Payroll Reconciler
Pulls approved hours into the pay run and flags overtime spikes, missing breaks and absent clock-offs for review — turning a two-day reconcile into an hour.
Shipment-Status Responder
Reads inbound "where is my order?" enquiries, pulls the current milestone and ETA from your TMS and carrier feeds, and drafts a cited reply for a person to approve — escalating any delayed or unavailable shipment instead of inventing a time. It never sends on its own.
Spec & Allergen Concierge
Answers spec, allergen, and COA questions from your approved documents only, quoting the source document and section on every line — and refusing to answer when the approved pack is incomplete, so customer service and sales stop waiting on one experienced person.
No-Show Predictor
Dramatised No-Show Predictor demo for healthcare (fake data) — shows the pattern; a person approves each output.
POD Reconciliation
Dramatised POD Reconciliation demo for logistics (fake data) — shows the pattern; a person approves each output.
Council Enquiry Assistant
A 24/7 first responder for routine rates, waste, and permit enquiries that answers from the council's own published information with citations, and hands the complex or vulnerable cases to staff with the full context attached.
First-Gift Welcome Drafter
Spots first-time donors who haven't been thanked, drafts a warm, personalised welcome sequence tied to the program they actually gave to, and queues it for staff to approve and send — targeting the sector's weakest metric, first-year donor retention.
Inbox Triage
Turns a chaotic shared inbox into a sorted, urgency-ranked queue with drafted replies — so the morning starts with the work, not the sorting.
Missed-Call Rescue & Booking Agent
Answers or texts back missed service calls in seconds, captures the vehicle and issue, offers real open slots, and writes the booking into the scheduler — routing anything ambiguous to an advisor instead of confirming on its own.
Order Inbox Drafter
Reads orders arriving by email, PDF, voicemail, and text, extracts the line items, matches each to your SKU master, and produces a structured draft sales order — flagging anything ambiguous for a person to approve, never posting an order on its own.
Order Intake Sorter
Reads inbound customer orders arriving as email text and varied PDFs, extracts them into ERP-ready fields, and flags any mismatch against your item master — landing a clean, reviewable record instead of a re-keyed one.
Award Rate Checker
Reconciles every payslip line against the modern award before the pay run leaves, so an underpaid weekend penalty or missed allowance gets caught — and corrected — before it becomes a wage-theft exposure.
Carrier-Invoice Reconciler
Extracts every line on a carrier invoice, matches it against the contracted rate card — fuel levies, zone pricing, weight breaks — and flags overcharges with a draft dispute attached, holding each exception for a person to clear. It never approves or pays an invoice on its own.
Prompts you can use today, for free.
Sector-specific prompts RMAI uses as starting points. Copy one, run it against your own documents in any assistant, and see the shape of the answer before you talk to us.
How RMAI would work with you.
Every engagement starts with the diagnostic and scales from there. These link through to how RMAI works.
What a defensible result looks like.
These are published third-party results in comparable Australian and NZ operators — illustrative of the target RMAI builds toward, with a human in the loop throughout. They are not RMAI client claims.
Considering AI for your retail or hospitality floor?
The two-week diagnostic is the right place to start. Fixed scope, fixed price. We’ll tell you whether the pattern fits and what the build would look like.