AI for food & beverage, grounded in your own specs, orders, and supplier records.
AI for food & beverage is grounded, auditable software that RMAI builds on your own specs, orders, and supplier documents — so emailed orders, supplier COAs, and traceability records are extracted, checked, and cited in minutes, with a human signing off every order and release.
What actually slows a food and beverage operation down.
The binding constraints in food and beverage are not the craft — they are operational: orders rekeyed by hand, supplier documents checked one by one, allergen and traceability records scattered across folders, and waste noticed only after the bin is full. Each one is a documents-and-data problem, which is exactly where grounded AI pays back.
Orders arrive after hours, then get keyed by hand
Orders land by voicemail, email, PDF, and text between 6pm and 2am, then staff retype every line into the ERP the next morning. Non-EDI orders run 15–30 minutes each, so the morning rush — not demand — caps how many a team can process.
Undeclared allergens cause most food recalls
FSANZ coordinated 92 recalls in 2025, and 38% were undeclared allergens — usually a packaging swap or a supplier ingredient change that a manual spec check missed at speed. Direct recall costs run into the millions before brand damage is counted.
QA checks every supplier COA by hand
Certificates of analysis and specs arrive as non-standard PDFs, and a quality officer reads each one against the master spec — 10–15 minutes a document. So highly trained QA staff spend the day as data-entry clerks instead of managing supplier quality.
Perishable stock becomes avoidable waste
Australia throws away about 7.6 million tonnes of food a year, worth A$36.6 billion. On thin margins, every short-dated pallet and over-prepped batch is margin in the bin — and backward-looking spreadsheet forecasts catch it too late to prevent.
What changes once orders and specs are grounded in your own data.
Operators working with RMAI recover skilled-staff hours and tighten their audit trail at the same time. The outcomes below are illustrative of shipped patterns; every one keeps a person on the final call — nothing finalises an order, a release, or a recall 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 |
|---|---|---|
| Order entry (per order) | 15–30 min | < 1 min (review) |
| Supplier COA / spec check | 10–15 min | seconds (review) |
| Recall / traceability pack | days | minutes |
| Preventable spoilage (in-scope) | baseline | 10–40% lower |
What RMAI has built for this sector.
The applications below are grounded, human-in-the-loop tools for this sector — some already built and shipped, some scoped from real briefs. Ask us which is which.
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.
Recall War Room
Traces an affected lot forward through production and shipments to scope a recall, drafts the customer and retail notification list, and builds the action log — with a recall coordinator approving each stage before any notification is sent.
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.
Label Compliance Checker
Extracts every required field from a label — ingredients, allergens, nutrition panel, country of origin, net weight — and checks each against the current FSANZ Food Standards Code, flagging gaps and non-compliant wording with the standard reference, for the reviewer to clear before reprint.
Batch Traceability & Recall Simulator
Maps lot and batch genealogy from raw-material receipt through production to customer shipments, tracing any lot forward to every affected product or backward to its source — with a simulation mode to run a mock recall and find traceability gaps before a real event.
Weighbridge-to-Ledger Reconciler
Reads weighbridge dockets, matches each against the grower contract, and drafts the invoice in Xero — flagging missing tares, unwarranted shrink deductions, and price mismatches for a person to clear.
Spray-Diary Formatter
Turns informal field spray notes into a structured, APVMA-compliant chemical-application log — date, block, product, batch, rate, wind, operator, withholding period — quoting the source note and flagging any gap rather than inferring it.
Audit-Pack Assembler
Watches the folders where spray diaries, water tests, and training certificates land, flags anything missing or expired, and assembles a Freshcare- or HARPS-ready evidence pack against the standard's checklist.
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.
Prompts you can use today, for free.
Sector-specific prompts RMAI uses as starting points. Copy one and run it against de-identified samples — or inside an approved private environment — to 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 food and beverage operators — illustrative of the target RMAI builds toward, with a human in the loop throughout. They are not RMAI client claims. RMAI has not yet shipped to a named food-sector client — we are early in this sector, and the $4,500 working session exists to prove the pattern on your own data before you commit to any build.
Considering AI for your food and beverage operation?
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.