Loman AI
answers the phone.

Here is the setup. A restaurant during peak service is a machine running at capacity. The line is out the door, the kitchen is calling tickets, and the phone - the phone is ringing. Someone glances at it. Someone lets it go. According to Loman's own framing, up to 30% of calls go unanswered during the busiest hours, and each of those calls is worth somewhere between $25 and $50 in order value. Multiply that across a Friday night, across a year, across a chain, and you arrive at a number large enough to fund a startup.

That is roughly what happened. Loman AI, founded in 2024 in Austin, Texas, by Christian Wiens, builds a voice AI that answers restaurant phones so that staff don't have to. The pitch is not "replace your people." It is closer to "stop making your people do the one job they are worst positioned to do while also running the floor." The AI takes pickup and delivery orders, books reservations, answers the eternal questions - are you open, do you have gluten-free, where are you, how long is the wait - and, crucially, drops the resulting order directly into the point-of-sale system as if a human had keyed it in.

The word "crucially" is doing real work there. Voice AI for restaurants is not a new idea, and it has a somewhat checkered history. The category is littered with demos that dazzled and deployments that stumbled, agents that could hold a conversation in a quiet conference room but fell apart the moment a caller mumbled a topping through a car window. Loman's investors are refreshingly direct about this. "Loman is proving it can deliver AI that works in the trenches, not just in demos," said Michael Maloney of Next Coast Ventures, which led the round. The subtext is that plenty of others did not.

What makes Loman's approach coherent is that it treats the phone as an operations problem rather than a chatbot problem. The AI does not exist to be impressive; it exists to produce a correct ticket in Toast, a booking in OpenTable, a captured card payment - the mundane artifacts of a restaurant that actually functions. Eric Pakravan of TenOneTen Ventures put the accuracy point plainly: Loman resonates "because it is accurate, easy to setup, and integrated with real operator workflows." Accuracy first. Everything else is downstream of getting the order right.

The company found its sharpest early traction in, of all things, pizza. This is less random than it sounds. Pizza is arguably the most brutal test environment in the entire restaurant phone universe - high volume, heavy customization, callers in a hurry, tickets that fork into a hundred combinations of size, crust, and topping. If your voice AI can survive a pizza rush, it can probably survive anything. Loman's customers here include operators like Crust Pizza, whose owner Nick Haselidis delivered the kind of testimonial that startups frame on the wall: "This paid for itself in 10 days. Phones are calm, tickets are bigger, and my team refuses to go back."

Note the three claims packed into that sentence, because they map neatly onto Loman's whole value proposition. "Paid for itself" is the ROI argument. "Tickets are bigger" is the upsell argument - the AI, unlike a harried human, never forgets to ask if you want to make it a large or add a drink. And "my team refuses to go back" is the labor argument, the quiet human benefit of taking a task nobody enjoyed off the plate of people who have plenty else to do.

That line - the phone becomes an asset again - is the cleanest summary of the business you will find. It reframes the ringing phone from a cost center into a channel. And it hints at why the economics work: Loman isn't asking restaurants to spend money on a novelty. It is asking them to stop leaving money on the table, and taking a cut of what gets recovered.

The numbers the company reports, which should be read as vendor figures rather than audited results, are the sort designed to make an operator do quick mental math. Early adopters, Loman says, see up to 22% higher revenue from recaptured calls and smart upsells, and up to 17% lower labor costs from not staffing the phones. Setup is pitched at under 24 hours - the AI can, in theory, be answering your line by tomorrow. And at the platform level, Loman claims to handle millions of calls and process tens of millions of dollars in order volume, the kind of scale that turns a clever idea into an actual company.

In August 2025, that trajectory was validated in the usual way, with money. Loman raised a $3.5 million seed round led by Next Coast Ventures, with participation from TenOneTen Ventures and Antler. It is not a fortune by the standards of AI fundraising in 2025, when models cost more than that to train for an afternoon. But it is the right size for what Loman is: a focused, operations-first company solving a specific, measurable problem for a specific, enormous market. There are a lot of restaurants. Nearly all of them have a phone. Most of them, at some point today, will let it ring.

The competitive field is real - ConverseNow, Kea, and the much larger SoundHound AI are all circling the restaurant voice opportunity - and none of this is guaranteed. Voice AI is hard, restaurant margins are thin, and integrations break. But Loman has picked a problem that is easy to describe, easy to price, and easy to prove. You can put a dollar figure on a missed call. That clarity, more than any single feature, is the company's real edge.