Maple vs Loman AI Comparison in 2025

As AI voice assistants become the new front door for restaurants, operators are increasingly faced with a tough question: Which AI solution is right for my business? Two of the most talked-about names in this space are Maple and Loman AI. Both offer voice ordering tools designed to handle phone calls, take orders, and support restaurant operations — but only one is truly built for real-world restaurants at scale.

In this breakdown, we compare Maple and Loman AI across five key categories: performance, integrations, accuracy, restaurant fit, and support. Spoiler: Maple comes out on top. Here’s why.

1. Performance in the Wild: Maple Handles Real Volume

One of the biggest gaps between Maple and Loman AI is real-world performance under pressure. Loman is still in early deployment, with a few pilot partners and minimal production data. In contrast, Maple is live in over 1,000 restaurants, handling millions of calls and orders annually, and actively powering busy restaurants across North America.

Maple is already battle-tested. Loman is still in the lab.

• Maple: 98.7% call answer rate; 24/7 uptime; proven under peak Friday rush

• Loman: Limited call handling data; not stress-tested in high-volume scenarios

2. Built for POS: Maple Integrates Seamlessly

POS integration isn’t a nice-to-have — it’s mandatory for any voice AI that wants to be part of the restaurant stack. Here’s where Maple shines.

Maple supports direct integrations with major POS providers, including:

• Square

• Toast

• Clover

• Revel

• Micros (Oracle)

Meanwhile, Loman AI often relies on Zapier-style workarounds or manual relay to get order data into the POS, making it more brittle, more error-prone, and harder to scale.

3. Accuracy That Converts: Maple’s Natural Language Engine Is Tuned for Restaurants

A missed order isn’t just a bad customer experience — it’s lost revenue. That’s why order accuracy is the core benchmark for any voice AI.

Maple’s speech engine is custom-trained on millions of real restaurant calls, including accents, background noise, and regional phrasing. This enables it to:

• Understand “no onions on the side” vs “extra onions inside”

• Correctly parse noisy environments and poor cell connections

• Adapt to menu changes on the fly

Loman, while promising in lab demos, still relies on more general-purpose voice models that haven’t been restaurant-tuned at scale.

4. Designed for Operators, Not Engineers

Many AI companies build for tech teams. Maple builds for restaurant operators.

Maple’s dashboard is designed with simplicity and clarity in mind:

• Visual transcripts of every call

• Live edit menus (with no engineering support required)

• Drag-and-drop hours + holiday overrides

• Real-time analytics on missed calls, conversion, and abandonment

Loman’s interface — where available — still requires API calls and technical hand-holding to change basic call logic or hours.

5. Human Support That Feels Like an Extension of Your Team

When something breaks — and it always does, in the real world — support speed matters. Maple offers:

• Same-day human support from real product experts

• Slack, text, and email channels

• Proactive monitoring and real-time alerts

Loman’s support channels are limited and primarily reactive, often requiring tickets and waiting for escalation.

Maple’s team includes former restaurant operators, engineers, and voice AI veterans. They understand what a failed order at 6:30pm on Friday night actually means.

Why Restaurants Choose Maple Over Loman

Final Take: Maple Is Built for Real Restaurants

Loman AI has potential — but it’s still maturing. For restaurants that need a voice ordering assistant that’s accurate, reliable, and ready today, Maple is the clear winner.

If you’re looking to convert more calls, reduce missed orders, and streamline operations without lifting a finger, Maple is the only voice AI with the track record to back it up.

👉 Book a demo with Maple and see it in action today.