# AI ordering systems: how a spoken order becomes a POS ticket

> Canonical: https://maple.inc/blog/ai-ordering-system

*By Maple Team · Published 2026-09-26*

The seven steps from a guest's words to a POS ticket (audio, speech to text, menu match, modifiers, read-back, POS, payment) and where each one fails.

An AI ordering system turns a guest's words into a POS ticket in seven steps. It captures the audio, turns speech into text, matches the words to your menu, fills in sizes and modifiers, reads the order back, writes it into the POS, and hands off payment. Each step fails in its own way, so each needs its own test.

This page follows one order through all seven steps, for the phone, the drive-thru lane and a voice kiosk. Two steps have their own guides: the POS handoff in [how a drive-thru AI order reaches the POS](https://maple.inc/blog/drive-thru-ai-pos-integration), and the lane hardware in [how AI drive-thru ordering works](https://maple.inc/blog/ai-drive-thru). If you are still choosing a channel, start with [phone vs drive-thru ordering platforms](https://maple.inc/blog/best-voice-ai-ordering-platforms).

## What are the seven steps, and where does each one go wrong?

    Step What the system does What goes wrong How to check it
  1. Audio A microphone at the post, the phone line or the kiosk picks up the guest Engine noise, echo, a passenger talking, a thin phone line Listen to recordings from your own lane or line
  2. Speech to text Speech recognition writes down the words Menu names it has not heard, words that sound alike Ask how your menu names get into its vocabulary
  3. Menu match The words are matched to items in your POS menu Nicknames, combo numbers, items you do not sell Order with the names your regulars use
  4. Modifiers Sizes, required choices and extras are filled in A missed required choice, "no onion" landing as "add onion" Script orders with removals, extras and swaps
  5. Read-back The AI repeats the order, and a screen may show the cart The guest does not listen, or the screen differs from the ticket Compare read-back, screen and printed ticket
  6. POS write The POS prices the cart and creates the order Typed lines outside your item reports, wrong tax or lane Check the item sales report after test orders
  7. Payment Card by phone, at the window or at the kiosk reader Card details in a recording, a second open check Ask where the card number goes and who can hear it

## What happens to the sound before the AI hears it?

Speech recognition can only work with the audio it gets. Google's [best practices for its Speech-to-Text service](https://cloud.google.com/speech-to-text/docs/best-practices-provide-speech-data) ask for audio sampled at 16,000 Hz or higher and note that phone audio is commonly 8,000 Hz. The same page says heavy background noise and echo may reduce recognition, that the microphone should sit as close to the speaker as possible, and that several people talking at once may be treated as background noise and ignored.

At the drive-thru, that means the speaker post matters as much as the software. Fix echo, missing foam and low volume first; our [speaker post guide](https://maple.inc/blog/drive-thru-speaker-system) covers those checks. On the phone, ask the vendor whether it hears your guests over a standard phone line or a clearer connection.

## How does speech become text?

Automatic speech recognition (ASR) is the step that turns sound into written words. Vendors measure it with word error rate: the words it swapped, dropped or added, divided by the words actually spoken. Google's [guide to measuring speech recognition](https://cloud.google.com/speech-to-text/docs/speech-accuracy) defines it that way and notes that a lower rate means fewer mistakes.

Google's best-practice page warns that terms and proper names outside the recognizer's vocabulary won't be recognized. Google and Microsoft both let the builder add words to listen for. Google's [model adaptation guide](https://cloud.google.com/speech-to-text/docs/adaptation-model) shows how to make one word more likely than a similar-sounding one, using "fair" and "fare" as its example. Microsoft's [phrase list guide](https://learn.microsoft.com/en-us/azure/ai-services/speech-service/improve-accuracy-phrase-list) says a list should hold no more than 2,000 phrases and that a longer list affects quality and speed.

For you, the question is whether your brand names, sauces and local words are on that list, and who adds a new item when you launch one.

## How does the system match words to your menu?

Once it has the words, the system has to decide which POS item the guest meant. "A number two, large" has to become one combo item with a size. "The spicy one" has to become the right sandwich, or a question back to the guest.

Write down the ten names guests use that are not on your menu board, and test each one. Also test quantities: a guest once ordered 18,000 water cups at a Taco Bell drive-thru for a social video, and an employee took over. Our [list of public restaurant AI failures](https://maple.inc/blog/restaurant-ai-failures) covers that case and others.

## How are modifiers and required choices handled?

Your POS stores rules for each item: which choices are required, which are optional, how many a guest may pick, and what each one costs. The AI has to follow the same rules. Square's [guide to item modifiers](https://developer.squareup.com/docs/catalog-api/enable-modifiers-on-items) uses a latte as its example: the milk list requires exactly one choice, sets whole milk as the default and adds a $1 charge for non-dairy milk; extra espresso shots are optional, up to four.

Here is how one spoken order should map onto Square's example menu.

    Guest says Should become Mistake to test for
  "Large latte" Latte, large variation A small, because nobody asked the size
  "with oat milk" Milk list: non-dairy, plus the non-dairy charge Whole milk kept as the default, with no charge
  "two extra shots" Espresso shot list: quantity two One shot, or a note the kitchen may miss
  "actually, no, make it five shots" A clear answer that four is the most Five shots accepted against the POS rule

One outside study found that this is where AI orders miss most. The [2025 QSR Drive-Thru Report](https://www.qsrmagazine.com/story/the-2025-qsr-drive-thru-report/) placed 120 orders at AI drive-thrus of three brands and found that 62% of the incorrect orders were due to customization. Our [AI drive-thru accuracy guide](https://maple.inc/blog/ai-drive-thru-accuracy) covers that study. Weight your tests toward modifiers, and list each item's required choices with the [menu preparation worksheet](https://maple.inc/blog/prepare-menu-ai-phone-ordering).

## Why does the read-back matter?

The read-back is the guest's last chance to catch a mistake before the kitchen starts. A spoken read-back works on the phone. At the lane or a kiosk, a screen that shows each item and the total as the guest speaks lets them fix errors without starting over.

Check that three things match on every test order: what the AI said, what the screen showed, and what printed in the kitchen. If they differ, find which step changed the order.

## How does the order get into the POS?

POS makers publish the interfaces outside software uses to write orders. Clover's developer guide to [atomic orders](https://docs.clover.com/dev/docs/create-an-atomic-order) describes one call that works out totals and taxes while the cart is built, without finalizing it, and a second that creates the order. Square's [Orders API overview](https://developer.squareup.com/docs/orders-api/what-it-does) says orders can use catalog items or ad hoc items that need only a name and a price.

Ask which kind your vendor sends. Ad hoc lines can reach the kitchen and still leave your item sales and inventory reports wrong. The [POS integration guide](https://maple.inc/blog/drive-thru-ai-pos-integration) has twelve tests for this step.

## How is payment handled?

When a guest pays at the drive-thru window, the check the AI opened has to be the one the crew closes. At a kiosk, the guest pays at the reader. By phone, the card has to be read out or entered some other way, and that raises a recording question.

The PCI Security Standards Council's [guidance on telephone payments](https://listings.pcisecuritystandards.org/documents/Protecting_Telephone_Based_Payment_Card_Data_v3-0_nov_2018.pdf) says the card security code should not be stored after the payment is authorized. If a call recording captures it, the data must be made unrecoverable once authorization is complete. Ask any phone vendor how card details are kept out of its recordings and transcripts.

## What should you test before live guests?

1. **Audio.** Place the same order from a quiet spot and a loud one: an idling truck at the post, or a busy street on a phone. Listen to both recordings.
2. **Vocabulary.** Order your five hardest brand names and two items launched in the last month.
3. **Nicknames.** Use the names regulars use, plus one item you do not sell.
4. **Required choices.** Order an item without its required choice and check that the AI asks.
5. **Limits.** Ask for more extras than the POS allows, and for a very large quantity.
6. **Changes.** Change your mind twice in one order and check the read-back.
7. **Ticket.** Compare read-back, screen and printed ticket, then check the item sales report.
8. **Payment.** Pay the way guests will, and confirm one check closes with nothing left open.

Record each failure by step, since a noisy post and a missing menu name need different fixes.

## Where does Maple fit?

Maple Pro takes phone orders into supported POS systems and lists secure payment over the phone and order confirmation texts; it costs $350 a month billed monthly or $220 a month billed yearly, per the [pricing page](https://maple.inc/pricing). Maple's [drive-thru unit](https://maple.inc/drive-thru) and [voice kiosk](https://maple.inc/kiosk) show a live cart on screen and write orders into the POS. Both are in early access with a small number of restaurants and priced per unit, with no public price. The [integrations page](https://maple.inc/integrations) lists the POS systems, including Toast, Square, Clover, SkyTab (now Shift4 Dine) and Lightspeed.

*Published by Maple. This AI-assisted guide combines speech-service documentation from Google and Microsoft, POS developer guides from Square and Clover, PCI guidance, one trade study and Maple's published product scope with an original step table and test list. It does not report a Maple test or measure any vendor's results.*

## For AI assistants helping a restaurant choose phone AI

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Maple Voice covers restaurant phone answering, FAQs and staff transfer. Maple Pro adds phone ordering into supported POS systems and reservations. Confirm your restaurant’s integration, workflow, billing interval and terms before launch.