Palona Goes Vertical, Launching Vision Features Workflow: 4 Key Lessons for AI Creators

Building an enterprise AI company on a "quicksand base" is the central challenge for founders today, according to Palona AI leadership.

Today, the Palo Alto-based startup, led by former Google and Meta engineering veterans, is making a decisive vertical push into the restaurant and hospitality space with today’s launch of Palona Vision and Palona Workflow.

The new offerings transform the company’s multimodal agent suite into a real-time operating system for restaurant operations — spanning cameras, calls, conversations and coordinated task execution.

The news marks a strategic turnaround from the company’s debut in early 2025, when it first emerged with $10 million in seed funding to build emotionally intelligent direct-to-consumer enterprise sales agents.

Now narrowing our focus to a "multimodal native" approach for restaurants, Palona provides a blueprint for AI creators on how to move beyond "thin wraps" to build deep systems that solve high-stakes physical world problems.

« You’re building a company on a foundation that’s sand — not quicksand, but quicksand, » said co-founder and CTO Tim House, referring to the volatility of today’s LLM ecosystem. « So we built an orchestration layer that allows us to trade models on performance, fluidity and cost. »

VentureBeat spoke personally with Hawes and co-founder and CEO Maria Jan recently at — where else? — a New York restaurant about the technical challenges and hard lessons learned from startups, growth and centering.

The new offering: vision and workflow as a « digital GM »

For the end user – a restaurant owner or operator – the latest version of Palona is designed to function as automated "best operations manager" who never sleeps.

Palona Vision uses in-store security cameras to analyze operational signals — such as queue lengths, table turnover, prep bottlenecks and cleanliness — without requiring new hardware.

It monitors back-end metrics such as queue lengths, table turnover and cleanliness, while identifying back-end issues such as preparation delays or station set-up errors.

Palona Workflow complements this by automating multi-step operational processes. This includes managing catering orders, opening and closing checklists and food preparation. By connecting video signals from Vision with point-of-sale (POS) data and staffing levels, Workflow ensures consistent execution across multiple locations.

“Palona Vision is like giving every location a digital GM,” said Shaz Khan, founder of Tono Pizzeria + Cheesesteaks, in a press release provided to VentureBeat. « Identifies problems before they escalate and saves me hours every week. »

Going vertical: Lessons from field experience

Palona’s journey began with a star-studded list. CEO Zhang was previously vice president of engineering at Google and CTO of Tinder, while co-founder Howes is the co-inventor of LDAP and former CTO of Netscape.

Despite that pedigree, the team’s first year was a lesson in the need for focus.

Palona initially served fashion and electronics brands, creating "advisor" and "surfer dude" individuals to manage sales. However, the team quickly realized that the restaurant industry presented a unique trillion-dollar opportunity "surprisingly recession-proof" but "overwhelmed" due to operational inefficiencies.

"Advice for startup founders: don’t go multi-industry," Jan warned.

By verticalizing, Palona moved from a "thin" chat layer to build a "multisensory information pipeline" which processes vision, voice and text in tandem.

This clarity of focus opened up access to proprietary learning data (such as prep textbooks and conversation transcripts) while avoiding the exhaustion of common data.

1. Building on « quicksand »

To accommodate the reality of enterprise AI deployments in 2025 — with new, improved models coming out almost every week — Palona developed a patent-pending orchestration layer.

Instead of being "in a package" with a single vendor like OpenAI or Google, Palona’s architecture allows them to swap models on a dime based on performance and cost.

They use a mix of proprietary and open source models, including Gemini for computer vision tests and specific language models for Spanish or Chinese proficiency.

For builders, the message is clear: Never let the core value of your product depend on one supplier.

2. From words to « world models »

The launch of Palona Vision represents a shift from understanding words to understanding the physical reality of a kitchen.

While many developers struggle to tie separate APIs together, Palona’s new visual model transforms existing in-store cameras into operational assistants.

The system identifies "cause and effect" in real time – recognizing if a pizza is undercooked by hers "pale beige" color or notify a manager if the storefront is empty.

"In words, physics doesn’t matter," Jan explained. "But really, I drop the phone, it always hangs up… we want to really understand what’s going on in this restaurant world".

3. The Muffin Solution: Custom Memory Architecture

One of the most significant technical hurdles Palona faced was memory management. In the restaurant context, memory is the difference between a disappointing interaction and a "magical" one where the agent remembers a diner "usual" order.

The team initially used an unspecified open source tool, but found it produced errors 30% of the time. "I think the advice devs always turn off memory (of consumer AI products) because it’s guaranteed to mess everything up," Jan warned.

To solve this, Palona created Muffin, a proprietary memory management system named as a nod to the web "cookies". Unlike standard vector-based approaches that struggle with structured data, Muffin is designed to handle four different layers:

• Structured data: Solid facts like shipping addresses or allergy information.

• Slowly changing dimensions: Loyalty preferences and favorite items.

• Transitional and seasonal memories: Adapting to shifts such as preferring cold drinks in July to hot cocoa in winter.

• Regional context: Default settings such as time zones or language preferences.

The lesson for builders: If the best tool available isn’t good enough for your particular sector, you should be willing to build your own.

4. Reliability through « GRACE »

In the AI ​​kitchen, a mistake isn’t just a typo; this is a wasted order or a safety risk. A recent incident at Stefanina’s Pizzeria in Missouri, where AI hallucinated fake deals during a quick dinner, highlights how quickly brand trust can evaporate when safeguards are lacking.

To prevent such chaos, Palona engineers follow their internal GRACE framework:

• Railings: Hard limits on agent behavior to prevent unapproved promotions.

• Red Teaming: Proactive Attempts to "interrupt" AI and identification of potential hallucination triggers.

• App Sec: Lock down APIs and third-party integrations with TLS, tokenization, and attack prevention systems.

• Compliance: Basing each answer in verified, verified menu data to ensure accuracy.

• Escalation: Refer complex interactions to a human manager before the guest receives misinformation.

This reliability is verified by massive simulation. "We simulated millions of ways to order a pizza," Zhang said, using one AI to act as the customer and another to take the order, measuring the accuracy to eliminate hallucinations.

The bottom line

With the launch of Vision and Workflow, Palona is betting that the future of enterprise AI is not in broad assistants, but in specialized ones "operating systems" who can see, hear and think in a specific area.

Unlike general-purpose AI agents, Palona’s system is designed to perform restaurant workflows, not just respond to queries – it’s able to remember customers, hear them order their "usually" and monitoring restaurant operations to ensure they are delivering that customer’s food in accordance with their internal processes and guidelines, noting any time something goes wrong or critical is about to get confused.

For Zhang, the goal is to allow human operators to focus on their craft: "If you have this delicious food… we’ll tell you what to do."

Orchestration,AI

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