Why a former Google and Tinder exec is going all in on restaurant AI

^{A week in AI is like a year in other industries. I hope these issues become your weekly source of AI information, inspiration, and ideas. If we haven’t met before, I’m Amanda Smith. I write about AI and the fascinating folks who are building in this brave new world.}

Good morning. My favorite founder stories are the ones that take their decades of experience in a particular industry, then zag into another. 

They spot industries in need of innovation, then pivot – often out of lucrative, cushy careers like big tech. 

There’s no satiating that start-up spirit or need to build something for yourself. This week’s AI founder left Google (and Meta, LinkedIn and Tinder) to go all in on her AI agent for restaurants idea. 

Let’s dive in.

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Founded: February 2024 

Team size: 24 

Funding to date: $10 million in seed funding  

ARR: Not publicly disclosed 

Growth metric: After raising in October 2024, Palona is working with paying customers less than a year later.

Maria Zhang is the former CTO of Tinder, VP of Engineering at Google, LinkedIn, Instagram and Yahoo, and GM of AI for Products at Meta. But before her impressive string of big tech roles, she was a founder. 

She grew her company (Alike) from nothing, later being acquired by Yahoo. That experience shaped how Zhang thought about building products, teams, and long-term value. While working with the technology giants gave her the experience building and shaping complex systems reliably at the global level, she missed the start-up spirit. 

In February 2024, she quit her cushy Silicon Valley job at Google to start Palona – an AI agent for the restaurant industry. 

So, why the restaurant scene? 

Palona AI started as a research-driven effort by a group of engineers and researchers – focused on building truly multimodal AI systems that can see, hear, reason, and act by combining vision, voice, language, and operational data. 

“Restaurants became the natural environment for this work because they are real-time, physical settings where multiple signals intersect constantly. Running a restaurant requires understanding conversations, visual context, timing, and workflows together, not in isolation,” Zhang said. 

She explained that restaurants rely on fragmented tools that create blind spots. “Palona unifies vision, voice, and workflow automation into a single AI system, so operators can detect issues in real-time.” 

For an industry known for its razor-thin margins, implementing intelligent AI could save many beloved eateries, especially in cities like New York. 

Zhang’s time in big tech conditioned her to build with customer centricity in mind. 

This means “developing a deep understanding of how customers actually work day-to-day and making decisions based on real constraints and needs, not our assumptions.” For Palona, this meant creating technology that fits naturally into their customers’ businesses. 

With this in mind, Palona was built through tight design partnerships with real restaurants, starting with on-site deployments, rapid iteration, and measurable outcomes. 

The AI was designed to focus on what happens inside the restaurant already, rather than inspire behavior change. Restaurant customers can speak with the Palona agent to place an order or make a reservation. 

Palona’s go-to-market thesis is to “land and expand.” This included: 

Zhang said Palona operates on a B2B SaaS model, with pricing tied to locations and capabilities deployed. Their customers are multi-location restaurant groups, hospitality operators, and brands. 

Their most effective growth channel is operator-to-operator referrals and design partnerships. “When restaurant leaders see measurable improvements, adoption spreads organically. Hospitality is a trust-based industry.” 

Here’s what Zhang said is working today: 

In terms of future plans, computer vision will soon be added to the product that lets the AI watch restaurant video feeds in real-time and alert managers to issues and opportunities (long lines, dirty trash cans, poor service etc.)

“For the broader AI hospitality category, we believe the future is not point solutions or chatbots, but AI that understands the full operating environment,” Zhang shared. 

“The next phase will be about systems that can anticipate issues, coordinate actions across teams and tools, and quietly raise the baseline of service and operational consistency without replacing the human element that defines hospitality.” 

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